How-To Tutorials - Data

Key takeaways from the discussion To solve problems with machine learning, you must first understand them. Different people or groups of people are going to define a problem in a different way. So, we shouldn't believe that the way we want to frame the problem computationally is the right way. If we allow that our systems include people and society, it is clear that we have to help negotiate values, not simply define them. Last week, at the 32nd NeurIPS 2018 annual conference, Nitin Koli, Joshua Kroll, and Deirdre Mulligan presented the common pitfalls we see when studying the human side of machine learning. Machine learning is being used in high-impact areas like medicine, criminal justice, employment, and education for making decisions. In recent years, we have seen that this use of machine learning and algorithmic decision making have resulted in unintended discrimination.  It’s becoming clear that even models developed with the best of intentions may exhibit discriminatory biases and perpetuate inequality. Although researchers have been analyzing how to put concepts like fairness, accountability, transparency, explanation, and interpretability into practice in machine learning, properly defining these things can prove a challenge. Attempts have been made to define them mathematically, but this can bring new problems. This is because applying mathematical logic to human concepts that have unique and contested political and social dimensions necessarily has blind spots - every point of contestation can’t be integrated into a single formula. In turn, this can cause friction with other disciplines as well as the public. Based on their research on what various terms mean in different contexts, Nitin Koli, Joshua Krill, and Deirdre Mulligan drew out some of the most common misconceptions machine learning researchers and practitioners hold. Sociotechnical problems To find a solution to a particular problem, data scientists need precise definitions. But how can we verify that these definitions are correct? Indeed, many definitions will be contested, depending on who you are and what you want them to mean. A definition that is fair to you will not necessarily be fair to me”, remarks Mr. Kroll. Mr. Kroll explained that while definitions can be unhelpful, they are nevertheless essential from a mathematical perspective.  This means there appears to be an unresolved conflict between concepts and mathematical rigor. But there might be a way forward. Perhaps it’s wrong to simply think in this dichotomy of logical rigor v. the messy reality of human concepts. One of the ways out of this impasse is to get beyond this dichotomy. Although it’s tempting to think of the technical and mathematical dimension on one side, with the social and political aspect on the other, we should instead see them as intricately related. They are, Kroll suggests, socio-technical problems. Kroll goes on to say that we cannot ignore the social consequences of machine learning: “Technologies don’t live in a vacuum and if we pretend that they do we kind of have put our blinders on and decided to ignore any human problems.” Fairness in machine learning In the real world, fairness is a concept directly linked to processes. Think, for example, of the voting system. Citizens cast votes to their preferred candidates and the candidate who receives the most support is elected. Here, we can say that even though the winning candidate was not the one a candidate voted for, but at least he/she got the chance to participate in the process. This type of fairness is called procedural fairness. However, in the technical world, fairness is often viewed in a subtly different way. When you place it in a mathematical context, fairness centers on outcome rather than process. Kohli highlighted that trade offs between these different concepts can’t be avoided. They’re inevitable. A mathematical definition of fairness places a constraint over the behavior of a system, and this constraint will narrow down the cause of models that can satisfy these conditions. So, if we decide to add too many fairness constraints to the system, some of them will be self-contradictory. One more important point machine learning practitioners should keep in mind is that when we talk about the fairness of a system, that system isn’t a self-contained and coherent thing. It is not a logical construct - it’s a social one. This means there are a whole host of values, ideas, and histories that have an impact on its reality.. In practice, this ultimately means that the complexity of the real world from which we draw and analyze data can have an impact on how a model works. Kohli explained this by saying, “it doesn’t really matter... whether you are building a fair system if the context in which it is developed and deployed in is fundamentally unfair.” Accountability in machine learning Accountability is ultimately about trust. It’s about the extent you can be sure you know what is ‘true’ about a system. It refers to the fact that you know how it works and why it does things in certain ways. In more practical terms, it’s all about invariance and reliability. To ensure accountability inside machine learning models, we need to follow a layered model. The bottom layer is an accounting or recording layer, that keeps track of what a given system is doing and the ways in which it might have been changed.. The next layer is a more analytical layer. This is where those records on the bottom layer are analyzed, with decisions made about performance - whether anything needs to be changed and how they should be changed. The final and top-most layer is about responsibility. It’s where the proverbial buck stops - with those outside of the algorithm, those involved in its construction. “Algorithms are not responsible, somebody is responsible for the algorithm,”  explains Kroll. Transparency Transparency is a concept heavily tied up with accountability. Arguably you have no accountability without transparency. The layered approach discussed above should help with transparency, but it’s also important to remember that transparency is about much more than simply making data and code available. Instead, it demands that the decisions made in the development of the system are made available and clear too. Mr. Kroll emphasizes, “to the person at the ground-level for whom the decisions are being taken by some sort of model, these technical disclosures aren’t really useful or understandable.” Explainability In his paper Explanation in Artificial Intelligence: Insights from the Social Sciences, Tim Miller describes what is explainable artificial intelligence. According to Miller, explanation takes many forms such as causal, contrastive, selective, and social. Causal explanation gives reasons behind why something happened, for example, while contrastive explanations can provide answers to questions like“Why P rather than not-P?". But the most important point here is that explanations are selective. An explanation cannot include all reasons why something happened; explanations are always context-specific, a response to a particular need or situation. Think of it this way: if someone asks you why the toaster isn’t working, you could just say that it’s broken. That might be satisfactory in some situations, but you could, of course, offer a more substantial explanation, outlining what was technically wrong with the toaster, how that technical fault came to be there, how the manufacturing process allowed that to happen, how the business would allow that manufacturing process to make that mistake… you could, of course, go on and on. Data is not the truth Today, there is a huge range of datasets available that will help you develop different machine learning models. These models can be useful, but it’s essential to remember that they are models. A model isn’t the truth - it’s an abstraction, a representation of the world in a very specific way. One way of taking this fact into account is the concept of ‘construct validity’. This sounds complicated, but all it really refers to is the extent to which a test - say a machine learning algorithm - actually measures what it says it’s trying to measure. The concept is widely used in disciplines like psychology, but in machine learning, it simply refers to the way we validate a model based on its historical predictive accuracy. In a nutshell, it’s important to remember that just as data is an abstraction of the world, models are also an abstraction of the data. There’s no way of changing this, but having an awareness that we’re dealing in abstractions ensures that we do not lapse into the mistake of thinking we are in the realm of ‘truth’. To build a fair(er) systems will ultimately require an interdisciplinary approach, involving domain experts working in a variety of fields. If machine learning and artificial intelligence is to make a valuable and positive impact in fields such as justice, education, and medicine, it’s vital that those working in those fields work closely with those with expertise in algorithms. This won’t fix everything, but it will be a more robust foundation from which we can begin to move forward. You can watch the full talk on the Facebook page of NeurIPS. Researchers unveil a new algorithm that allows analyzing high-dimensional data sets more effectively, at NeurIPS conference Accountability and algorithmic bias: Why diversity and inclusion matters [NeurIPS Invited Talk] NeurIPS 2018: A quick look at data visualization for Machine learning by Google PAIR researchers [Tutorial]

The 32nd NeurIPS Conference kicked off on the 2nd of December and continued till the 8th of December in Montreal, Canada. This conference covered tutorials, invited talks, product releases, demonstrations, presentations, and announcements related to machine learning research. “Counterfactual Inference” is one such tutorial presented during the NeurIPS by Susan Athey, The Economics of Technology Professor at the Stanford Graduate School of Business. This tutorial reviewed the literature that brings together recent developments in machine learning with methods for counterfactual inference. It will focus on problems where the goal is to estimate the magnitude of causal effects, as well as to quantify the researcher’s uncertainty about these magnitudes. She starts by mentioning that there are two sets of issues make causal inference must know concepts for AI. Some gaps between what we are doing in our research, and what the firms are applying. There are success stories such as Google images and so on. However, the top tech companies also do not fully adopt all the machine learning / AI concepts fully. If a firm dumps their old simple regression credit scoring model and makes use of a black box based on ML, are they going to worry what’s going to happen when they use the Black Box algorithm? According to Susan, the reason why firms and economists historically use simple models is that just by looking at the data it is difficult to understand whether the approach used is right. Whereas, using a Black box algorithm imparts some of the properties such as Interpretability, which helps in reasoning about the correctness of the approach. This helps researchers to make improvements in the model. Secondly, stability and robustness are also important for applications. Transfer learning helps estimate the model in one setting and use the same learning in some other setting. Also, these models will show fairness as many aspects of discrimination relates to correlation vs. causation. Finally, machine learning imparts a Human-like AI behavior that gives them the ability to make reasonable and never seen before decisions. All of these desired properties can be obtained in a causal model. The Causal Inference Framework In this framework, the goal is to learn a model of how the world works. For example, what happens to a body while a drug enters. Impact of intervention can be context specific. If a user learns something in a particular setting but it isn't working well in the other setting, it is not a problem with the framework. It’s, however, hard to do causal inference, there are some challenges including: We do not have the right kind of variation in the data. Lack of quasi-experimental data for estimation Unobserved contexts/confounders or insufficient data to control for observed confounders Analyst’s lack of knowledge about model Prof. Athey explains the true AI algorithm by using an example of contextual bandit under which there might be different treatments. In this example, one can select among alternative choices. They must have an explicit or implicit model of payoffs from alternatives. They also learn from past data. Here, the initial stages of learning have limited data, where there is a statistician inside the AI which performs counterfactual reasoning. A statistician should use best performing techniques (efficiency, bias). Counterfactual Inference Approaches Approach 1: Program Evaluation or Treatment Effect Estimation The goal of this approach is to estimate the impact of an intervention or treatment assignment policies. This literature focuses mainly on low dimensional interventions. Here, the estimands or the things that people want to learn is the average effect (Did it work?). For more sophisticated projects, people seek the heterogeneous effect (For whom did it work?) and optimal policy (policy mapping of people’s behavior to their assignments). The main goal here is to set confidence intervals around these effects to avoid bias or noisy sampling. This literature focuses on design that enables identification and estimation of these effects without using randomized experiments. Some of the designs include Regression discontinuity, difference-in-difference, and so on. Approach 2: Structural Estimation or ‘Generative models and counterfactuals’ Here the goal is to impact on welfare/profits of participants in alternative counterfactual regimes. These regimes may not have ever been observed in relevant contexts. These also need a behavioral model of participants. One can make use of Dynamic structural models to learn about value function from agent choices in different states. Approach 3: Causal discovery The goal of this approach is to uncover the causal structure of a system. Here the analyst believes that there is an underlying structure where some variables are causes of others, e.g. a physical stimulus leads to biological responses. Application of this can be found in understanding software systems and biological systems. [box type="shadow" align="" class="" width=""]Recent literature brings causal reasoning, statistical theory, and modern machine learning algorithms together to solve important problems. The difference between supervised learning and causal inference is that supervised learning can evaluate in a test set in a model‐free way. In causal inference, parameter estimation is not observed in a test set. Also, it requires theoretical assumptions and domain knowledge. [/box] Estimating ATE (Average Treatment Effects) under unconfoundedness Here only the observational data is available and only an analyst has access to the data that is sufficient for the part of the information used to assign units to treatments that is related to potential outcomes. The speaker here has used an example of how online Ads are targeted using cookies. The user sees car ads because the advertiser knows that the user has visited car reviewer websites. Here the purchases cannot be related to users who saw an ad versus the ones who did not. Hence, the interest in cars is the unobserved confounder. However, the analyst can see the history of the websites visited by the user. This is the main source of information for the advertiser about user interests. Using Supervised ML to measure estimate ATE under unconfoundedness The first supervised ML method is propensity score weighting or KNN on propensity score. For instance, make use of the LASSO regression model to estimate the propensity score. The second method is Regression adjustment which tries to estimate the further outcomes or access the features of further outcomes to get a causal effect. The next method is estimating CATE (Conditional average treatment effect) and take averages using the BART model. The method mentioned by Prof. Athey here is, Double robust/ double machine learning which uses cross-fitted augmented inverse propensity scores. Another method she mentioned was Residual Balancing which avoids assuming a sparse model thus allowing applications with a complex assignment. If unconfoundedness fails, the alternate assumption: there exists an instrumental variable Zi that is correlated with Wi (“relevance”) and where: Structural Models Structural models enable counterfactuals for never‐seen worlds. Combining Machine learning with structural model provides attention to identification, estimation using “good” exogenous variation in data. Also, adding a sensible structure improves performance required for never‐seen counterfactuals, increased efficiency for sparse data (e.g. longitudinal data) Nature of structure includes: Learning underlying preferences that generalize to new situations Incorporating nature of choice problem Many domains have established setups that perform well in data‐poor environments With the help of Discrete Choice Model, users can evaluate the impact of a new product introduction or the removal of a product from choice set. On combining these Discrete Choice Models with ML, we have two approaches to product interactions: Use information about product categories, assume products substitutes within categories Do not use available information about categories, estimate subs/complements Susan has concluded by mentioning some of the challenges on Causal inference, which include data sufficiency, finding sufficient/useful variation in historical data. She also mentions that recent advances in computational methods in ML don’t help with this. However, tech firms conducting lots of experiments, running bandits, and interacting with humans at large scale can greatly expand the ability to learn about causal effects! Head over to the Susan Athey’s entire tutorial on Counterfactual Inference at NeurIPS Facebook page. Researchers unveil a new algorithm that allows analyzing high-dimensional data sets more effectively, at NeurIPS conference Accountability and algorithmic bias: Why diversity and inclusion matters [NeurIPS Invited Talk] NeurIPS 2018: A quick look at data visualization for Machine learning by Google PAIR researchers [Tutorial]

Google CEO, Sundar Pichai testified before the House Judiciary Committee earlier this week. The hearing titled “Transparency & Accountability: Examining Google and its Data Collection, Use, and Filtering Practices” was a three-and-a-half-hour question-answer session that centered mainly around user data collection at Google, allegations of political bias in its search algorithms, and Google’s controversial plans with China. “All of these topics, competition, censorship, bias, and others..point to one fundamental question that demands the nation’s attention. Are America’s technology companies serving as instruments of freedom or instruments of control?,” said Representative Kevin McCarthy of California, the House Republican leader. The committee members could have engaged with Pichai on more important topics had they not been busy focussing on opposing each other’s opinions over whether Google search and its other products are biased against conservatives. Also, most of Pichai’s responses were unsatisfactory as he cleverly dodged questions regarding its Project Dragonfly and user data. Here are the key highlights from the testimony. Allegations of Political Bias One common theme throughout the long hearing session was Republicans asking questions based around alleged bias against conservatives on Google's platforms. Google search Bias Rep. Lamar Smith asked questions regarding the alleged political bias that is “imbibed” in Google’s search algorithms and its culture. Smith presented an example of a study by Robert Epstein, a Harvard trained psychologist. As per the study’s results, Google’s search bias likely swung 2.6 million votes to Hillary Clinton during the 2016 elections. To this Pichai’s reply was that Google has investigated some of the studies including the one by Dr. Epstein, and found that there were issues with the methodology and its sample size. He also mentioned how Google evaluates their search results for accuracy by using a “robust methodology” that it has been using for the past 20 years. Pichai also added that “providing users with high quality, accurate, and trusted information is sacrosanct to us. It’s what our principles are and our business interests and our natural long-term incentives are aligned with that. We need to serve users everywhere and we need to earn their trust in order to do so.” Google employees’ bias, the reason for biased search algorithms, say Republicans Smith also presented examples of pro-Trump content and immigration laws being tagged as hate speech on Google search results posing threat to the democratic form of government. He also alleged that people at Google were biased and intentionally transferred their biases into these search algorithms to get the results they want and management allows it. Pichai clarified that Google doesn't manually intervene on any particular search result. “Google doesn’t choose conservative voices over liberal voices. There’s no political bias and Google operates in a neutral way,” added Pichai. Would Google allow an independent third party to study its search results to determine the degree of political bias? Pichai responded to this question saying that they already have third parties that are completely independent and haven’t been appointed by Google in place for evaluating its search algorithms. “We’re transparent as to how we evaluate our search. We publish our rater guidelines. We publish it externally and raters evaluate it, we’re trying hard to understand what users want and this is what we think is right. It’s not possible for an employee or a group of employees to manipulate our search algorithm”. Political advertising bias The Committee Chairman Bob Goodlatte, a Republican from Virginia also asked Pichai about political advertising bias on Google’s ad platforms that offer different rates for different political candidates to reach prospective voters. This is largely different than how other competitive media platforms like TV and radio operate - offering the lowest rate to all political candidates. He asked if Google should charge the same effective ad rates to political candidates. Pichai explained that their advertising products are built without any bias and the rates are competitive and set by a live auction process. The prices are calculated automatically based on the keywords that you’re bidding for, and on the demand in the auction. There won’t be a difference in rates based on any political reasons unless there are keywords that are of particular interest. He referred the whole situation to a demand-supply equilibrium, where the rates can differ but that will vary from time to time. There could be occasions when there is a substantial difference in rates based on the time of the day, location, how keywords are chosen etc, and it’s a process that Google has been using for over 20 years. Pichai further added that “anything to do with the civic process, we make sure to do it in a non-partisan way and it's really important for us”. User data collection and security Another highlight of the hearing was Google’s practices around user data collection and security. “Google is able to collect an amount of information about its users that would even make the NSA blush. Americans have no idea the sheer volume of information that is collected”, said Goodlatte. Location tracking data related privacy concerns During Mr. Pichai’s testimony, the first question from Rep. Goodlatte was about whether consumers understand the frequency and amount of location data that Google collects from its Android operating system. Goodlatte asked Pichai about the collection of location data and apps running on Android. To this Pichai replied that Google offers users controls for limiting location data collection. “We go to great lengths to protect their privacy, we give them transparency, choice, and control,” says Pichai. Pichai highlighted that Android is a powerful smartphone that offers services to over 2 billion people. User data that is collected via Android depends on the applications that users choose to use. He also pointed out that Google makes it very clear to its users about what information is collected. He pointed out that there are terms of service and also a “privacy checkup”. Going to  “my account” settings on Gmail gives you a clear picture of what user data they have. He also says that users can take that data to other platforms if they choose to stop using Google. On Google+ data breach Another Rep. Jerrold Nadler talked about the recent Google plus data breach that affected some 52.5 million users. He asked Pichai about the legal obligations that the company is under to publicly expose the security issues. Pichai responded to this saying that Google “takes privacy seriously,” and that Google needs to alert the users and the necessary authorities of any kind of data breach or bugs within 72 hours. He also mentions "building software inevitably has bugs associated as part of the process”.  Google undertakes a lot of efforts to find bugs and the root cause of it, and make sure to take care of it. He also says how they have advanced protection in Gmail to offer a stronger layer of security to its users. Google’s commitment to protecting U.S. elections from foreign interference It was last year when Google discovered that Russian operatives spent tens of thousands of dollars on ads on its YouTube, Gmail and Google Search products in an effort to meddle in the 2016 US presidential election. “Does Google now know the full extent to which its online platforms were exploited by Russian actors in the election 2 years ago?” asked Nadler. Pichai responded that Google conducted a thorough investigation in 2016. It found out that there were two main ads accounts linked to Russia which advertised on google for about 4700 dollars in advertising. “We found a limited activity, improper activity, we learned from that and have increased the protections dramatically we have around our elections offering”, says Pichai. He also added that to protect the US elections, Google will do a significant review of how ads are bought, it will look for the origin of these accounts, share and collaborate with law enforcement, and other tech companies. “Protecting our elections is foundational to our democracy and you have my full commitment that we will do that,” said Pichai. Google’s plans with China Rep. Sheila Jackson Lee was the first person to directly ask Pichai about the company’s Project Dragonfly i.e. its plans of building a censored search engine with China. “We applauded you in 2010 when Google took a very powerful stand principle and democratic values over profits and came out of China,” said Jackson. Other who asked Pichai regarding Google's China plans were Rep. Tom Marino and Rep. David Cicilline. Google left China in 2010 because of concerns regarding hacking, attacks, censorship, and how the Chinese government was gaining access to its data. How is working with the Chinese govt to censor search results a part of Google’s core values? Pichai repeatedly said that Google has no plans currently to launch in China. “We don't have a search product there. Our core mission is to provide users with access to information and getting access to information is an important right (of users) so we try hard to provide that information”, says Pichai. He added that Google always has evidence based on every country that it has operated in. “Us reaching out and giving users more information has a very positive impact and we feel that calling but right now there are no plans to launch in China,” says Pichai. He also mentioned that if Google ever approaches a decision like that he’ll be fully transparent with US policymakers and “engage in consult widely”. He further added that Google only provides Android services in China for which it has partners and manufacturers all around the world. “We don't have any special agreements on user data with the Chinese government”, said Pichai.  On being asked by Rep. Marino about a report from The Intercept that said Google created a prototype for a search engine to censor content in China, Pichai replied, “we designed what a search could look like if it were to be launched in a country like China and that’s what we explored”. Rep. Cicilline asked Pichai whether any employees within Google are currently attending product meetings on Dragonfly. Pichai replied evasively saying that Google has “undertaken an internal effort, but right now there are no plans to launch a search service in China necessarily”. Cicilline shot another question at Pichai asking if Google employees are talking to members of the Chinese government, which Pichai dodged by responding with "Currently we are not in discussions around launching a search product in China," instead. Lastly, when Pichai was asked if he would rule out "launching a tool for surveillance and censorship in China”, he replied that Google’s mission is providing users with information, and that “we always think it’s in our duty to explore possibilities to give users access to information. I have a commitment, but as I’ve said earlier we’ll be very thoughtful and we’ll engage widely as we make progress”. On ending forced arbitration for all forms of discrimination Last month 20,000 Google employees along with Temps, Vendors, and Contractors walked out of their respective Google offices to protest discrimination and sexual harassment in the workplace. As part of the walkout, Google employees laid out five demands urging Google to bring about structural changes within the workplace. One of the demands was ending forced arbitration meaning that Google should no longer require people to waive their right to sue. Also, that every co-worker should have the right to bring a representative, or supporter of their choice when meeting with HR for filing a harassment claim. Rep. Pramila Jayapal asked Pichai if he can commit to expanding the policy of ending forced arbitration for any violation of an employee’s (also contractors) right not just sexual harassment. To this Pichai replied that Google is currently definitely looking into this further. “It’s an area where I’ve gotten feedback personally from our employees so we’re currently reviewing what we could do and I’m looking forward to consulting, and I’m happy to think about more changes here. I’m happy to have my office follow up to get your thoughts on it and we are definitely committed to looking into this more and making changes”, said Pichai. Managing misinformation and hate speech During the hearing, Pichai was questioned about how Google is handling misinformation and hate speech. Rep. Jamie Raskin asked why videos promoting conspiracy theory known as “Frazzledrip,” ( Hillary Clinton kills young women and drinks their blood) are still allowed on YouTube. To this Pichai responded with, “We would need to validate whether that specific video violates our policies”. Rep. Jerry Nadler also asked Pichai about Google’s actions to "combat white supremacy and right-wing extremism." Pichai said Google has defined policies against hate speech and that if Google finds violations, it takes down the content. “We feel a tremendous sense of responsibility to moderate hate speech, define hate speech clearly inciting violence or hatred towards a group of people. It's absolutely something we need to take a strict line on. We’ve stated our policies strictly and we’re working hard to make our enforcement better and we’ve gotten a lot better but it's not enough so yeah we’re committed to doing a lot more here”, said Pichai. Our Take Hearings between tech companies and legislators, in the current form, are an utter failure. In addition to making tech reforms, there is an urgent need to also make reforms in how policy hearings are conducted. It is high time we upgraded ourselves to the 21st century. These were the key highlights of the hearing held on 11th December 2018. We recommend you watch the complete hearing for a more comprehensive context. As Pichai defends Google’s “integrity” ahead of today’s Congress hearing, over 60 NGOs ask him to defend human rights by dropping Drag Google bypassed its own security and privacy teams for Project Dragonfly reveals Intercept Google employees join hands with Amnesty International urging Google to drop Project Dragonfly

2018 has thrown up a huge range of examples of the unintended consequences of algorithms. From the ACLU’s research in July which showed how the algorithm in Amazon’s facial recognition software incorrectly matched images of congress members with mugshots, to the same organization’s sexist algorithm used in the hiring process, this has been a year where the damage that algorithms can cause has become apparent. But this week, an open letter by Gillian Brockell, who works at The Washington Post, highlighted the traumatic impact algorithmic personalization can have. In it, Brockell detailed how personalized ads accompanied her pregnancy, and speculated how the major platforms that dominate our digital lives. “...I bet Amazon even told you [the tech companies to which the letter is addressed] my due date… when I created an Amazon registry,” she wrote. But she went on to explain how those very algorithms were incapable of processing the tragic death of her unborn baby, blind to the grief that would unfold in the aftermath. “Did you not see the three days silence, uncommon for a high frequency user like me”. https://twitter.com/STFUParents/status/1072759953545416706 But Brockell’s grief was compounded by the way those companies continued to engage with her through automated messaging. She explained that although she clicked the “It’s not relevant to me” option those ads offer users, this only led algorithms to ‘decide’ that she had given birth, offering deals on strollers and nursing bras. As Brockell notes in her letter, stillbirths aren’t as rare as many think, with 26,000 happening in the U.S. alone every year. This fact only serves to emphasise the empathetic blind spots in the way algorithms are developed. “If you’re smart enough to realize that I’m pregnant, that I’ve given birth, then surely you’re smart enough to realize my baby died.” Brockell’s open letter garnered a lot of attention on social media, to such an extent that a number of the companies at which Brockell had directed her letter responded. Speaking to CNBC, a Twitter spokesperson said, “We cannot imagine the pain of those who have experienced this type of loss. We are continuously working on improving our advertising products to ensure they serve appropriate content to the people who use our services.” Meanwhile, a Facebook advertising executive, Rob Goldman responded, “I am so sorry for your loss and your painful experience with our products.” He also explained how these ads could be blocked. “We have a setting available that can block ads about some topics people may find painful — including parenting. It still needs improvement, but please know that we’re working on it & welcome your feedback.” Experian did not respond to requests for comment. However, even after taking Goldman’s advice, Brockell revealed she was then shown adoption adverts: https://twitter.com/gbrockell/status/1072992972701138945 “It crossed the line from marketing into Emotional Stalking,” said one Twitter user. While the political impact of algorithms has led to sustained commentary and criticism in 2018, this story reveals the personal impact algorithms can have. It highlights that as artificial intelligence systems become more and more embedded in everyday life, engineers will need an acute sensitivity and attention to detail to the potential use cases and consequences that certain algorithms may have. You can read Brockell’s post on Twitter. Facebook’s artificial intelligence research team, FAIR, turns five. But what are its biggest accomplishments? FAT Conference 2018 Session 3: Fairness in Computer Vision and NLP FAT Conference 2018 Session 4: Fair Classification

The ’Strengthening African Machine Learning’ conference organized by Deep Learning Indaba, at Stellenbosch, South Africa, is ongoing right now. This 6-day conference will celebrate and strengthen machine learning in Africa through state-of-the-art teaching, networking, policy debate, and through support programmes. Yesterday, three conference organizers, Sebastian Ruder, Herman Kamper, and Stephan Gouws asked tech experts their view on the state of Natural Language Processing, more specifically these 4 questions: What do you think are the three biggest open problems in Natural Language Processing at the moment? What would you say is the most influential work in Natural Language Processing in the last decade, if you had to pick just one? What, if anything, has led the field in the wrong direction? What advice would you give a postgraduate student in Natural Language Processing starting their project now? The tech experts interviewed included the likes of Yoshua Bengio, Hal Daumé III, Barbara Plank, Miguel Ballesteros, Anders Søgaard, Lea Frermann, Michael Roth, Annie Louise, Chris Dyer, Felix Hill,  Kevin Knight and more. https://twitter.com/seb_ruder/status/1072431709243744256 Biggest open problems in Natural Language Processing at the moment Although each expert talked about a variety of Natural Language Processing open issues, the following common key themes recurred. No ‘real’ understanding of Natural language understanding Many experts argued that natural Language understanding is central and also important for natural language generation. They agreed that most of our current Natural Language Processing models do not have a “real” understanding. What is needed is to build models that incorporate common sense, and what (biases, structure) should be built explicitly into these models. Dialogue systems and chatbots were mentioned in several responses. Maletšabisa Molapo, a Research Scientist at IBM Research and one of the experts answered, “Perhaps this may be achieved by general NLP Models, as per the recent announcement from Salesforce Research, that there is a need for NLP architectures that can perform well across different NLP tasks (machine translation, summarization, question answering, text classification, etc.)” NLP for low-resource scenarios Another open problem is using NLP for low-resource scenarios. This includes generalization beyond the training data, learning from small amounts of data and other techniques such as Domain-transfer, transfer learning, multi-task learning. Also includes different supervised learning techniques, semi-supervised, weakly-supervised, “Wiki-ly” supervised, distantly-supervised, lightly-supervised, minimally-supervised and unsupervised learning. Per Karen Livescu, Associate Professor Toyota Technological Institute at Chicago, “Dealing with low-data settings (low-resource languages, dialects (including social media text "dialects"), domains, etc.).  This is not a completely "open" problem in that there are already a lot of promising ideas out there; but we still don't have a universal solution to this universal problem.” Reasoning about large or multiple contexts Experts believed that NLP has problems in dealing with large contexts. These large context documents can be either text or spoken documents, which currently lack common sense incorporation. According to, Isabelle Augenstein, tenure-track assistant professor at the University of Copenhagen, “Our current models are mostly based on recurrent neural networks, which cannot represent longer contexts well. One recent encouraging work in this direction I like is the NarrativeQA dataset for answering questions about books. The stream of work on graph-inspired RNNs is potentially promising, though has only seen modest improvements and has not been widely adopted due to them being much less straight-forward to train than a vanilla RNN.” Defining problems, building diverse datasets and evaluation procedures “Perhaps the biggest problem is to properly define the problems themselves. And by properly defining a problem, I mean building datasets and evaluation procedures that are appropriate to measure our progress towards concrete goals. Things would be easier if we could reduce everything to Kaggle style competitions!” - Mikel Artetxe. Experts believe that current NLP datasets need to be evaluated. A new generation of evaluation datasets and tasks are required that show whether NLP techniques generalize across the true variability of human language. Also what is required are more diverse datasets. “Datasets and models for deep learning innovation for African Languages are needed for many NLP tasks beyond just translation to and from English,” said Molapo. Advice to a postgraduate student in NLP starting their project Do not limit yourself to reading NLP papers. Read a lot of machine learning, deep learning, reinforcement learning papers. A PhD is a great time in one’s life to go for a big goal, and even small steps towards that will be valued. — Yoshua Bengio Learn how to tune your models, learn how to make strong baselines, and learn how to build baselines that test particular hypotheses. Don’t take any single paper too seriously, wait for its conclusions to show up more than once. — George Dahl I believe scientific pursuit is meant to be full of failures. If every idea works out, it’s either because you’re not ambitious enough, you’re subconsciously cheating yourself, or you’re a genius, the last of which I heard happens only once every century or so. so, don’t despair! — Kyunghyun Cho Understand psychology and the core problems of semantic cognition. Understand machine learning. Go to NeurIPS. Don’t worry about ACL. Submit something terrible (or even good, if possible) to a workshop as soon as you can. You can’t learn how to do these things without going through the process. — Felix Hill Make sure to go through the complete list of all expert responses for better insights. Google open sources BERT, an NLP pre-training technique Use TensorFlow and NLP to detect duplicate Quora questions [Tutorial] Intel AI Lab introduces NLP Architect Library  

The Australian competition and consumer commission (ACCC) have today published a 378-page preliminary report to make the Australian government and the public aware of the impact of social media and digital platforms on targeted advertising and user data collection. The report also highlights the ACCC's concerns regarding the “market power held by these key platforms, including their impact on Australian businesses and, in particular, on the ability of media businesses to monetize their content.” This report was published following an investigation when ACCC Treasurer Scott Morrison MP had asked the ACCC, late last year, to hold an inquiry into how online search engines, social media, and digital platforms impact media and advertising services markets. The inquiry demanded answers on the range and reliability of news available via Google and Facebook. The ACCC also expressed concerns on the large amount and variety of data which Google and Facebook collect on Australian consumers, which users are not actively willing to provide. Why did ACCC choose Google and Facebook? Google and Facebook are the two largest digital platforms in Australia and are the most visited websites in Australia. Google and Facebook also have similar business models, as they both rely on consumer attention and data to sell advertising opportunities and also have substantial market power. Per the report, each month, approximately 19 million Australians use Google Search, 17 million access Facebook, 17 million watch YouTube (which is owned by Google) and 11 million access Instagram (which is owned by Facebook). This widespread and frequent use of Google and Facebook means that these platforms occupy a key position for businesses looking to reach Australian consumers, including advertisers and news media businesses. Recommendations made by the ACCC The report contains 11 preliminary recommendations to these digital platforms and eight areas for further analysis. Per the report: #1 The ACCC wants to amend the merger law to make it clearer that the following are relevant factors: the likelihood that an acquisition would result in the removal of a potential competitor, and the amount and nature of data which the acquirer would likely have access to as a result of the acquisition. #2 ACCC wants Facebook and Google to provide advance notice of the acquisition of any business with activities in Australia and to provide sufficient time to enable a thorough review of the likely competitive effects of the proposed acquisition. #3 ACCC wants suppliers of operating systems for mobile devices, computers, and tablets to provide consumers with options for internet browsers and search engines (rather than providing a default). #4 The ACCC wants a regulatory authority to monitor, investigate and report on whether digital platforms are engaging in discriminatory conduct by favoring their own business interests above those of advertisers or potentially competing businesses. #5 The regulatory authority should also monitor, investigate and report on the ranking of news and journalistic content by digital platforms and the provision of referral services to news media businesses. #6 The ACCC wants the government to conduct a separate, independent review to design a regulatory framework to regulate the conduct of all news and journalistic content entities in Australia. This framework should focus on underlying principles, the extent of regulation, content rules, and enforcement. #7 Per ACCC, the ACMA (Australian Communications and Media Authority) should adopt a mandatory standard regarding take-down procedures for copyright infringing content. #8 ACCC proposes amendments to the Privacy Act. These include: Strengthen notification requirements Introduce an independent third-party certification scheme Strengthen consent requirements Enable the erasure of personal information Increase the penalties for breach of the Privacy Act Introduce direct rights of action for individuals Expand resourcing for the OAIC (Office of the Australian Information Commissioner) to support further enforcement activities #9 The ACCC wants OAIC to develop a code of practice under Part IIIB of the Privacy Act to provide Australians with greater transparency and control over how their personal information is collected, used and disclosed by digital platforms. #10 Per ACCC, the Australian government should adopt the Australian Law Reform Commission’s recommendation to introduce a statutory cause of action for serious invasions of privacy. #11 Per the ACCC, unfair contract terms should be illegal (not just voidable) under the Australian Consumer Law “The inquiry has also uncovered some concerns that certain digital platforms have breached competition or consumer laws, and the ACCC is currently investigating five such allegations to determine if enforcement action is warranted,” ACCC Chair Rod Sims said. The ACCC is also seeking feedback on its preliminary recommendations and the eight proposed areas for further analysis and assessment. Feedback can be shared by email to platforminquiry@accc.gov.au by 15 February 2019. AI Now Institute releases Current State of AI 2018 Report Australia passes a rushed anti-encryption bill “to make Australians safe”; experts find “dangerous loopholes” that compromise online privacy and safety Australia’s Facial recognition and identity system can have “chilling effect on freedoms of political discussion, the right to protest and the right to dissent”: The Guardian report

One of the most awaited machine learning conference, NeurIPS 2018 is happening throughout this week in Montreal, Canada. It will feature a series of tutorials, invited talks, product releases, demonstrations, presentations, and announcements related to machine learning research. For the first time, NeurIPS invited a diversity and inclusion (D&I) speaker Laura Gomez to talk about the lack of diversity in the tech industry, which leads to biased algorithms, faulty products, and unethical tech. Laura Gomez is the CEO of Atipica that helps tech companies find and hire diverse candidates. Being a Latina woman herself, she had to face oppression when seeking capital and funds for her startup trying to establish herself in Silicon Valley. This experience led to her realization that there is a strong need to talk about why diversity and inclusion matters. Her efforts were not in vain and recently, she raised $2M in seed funding led by True Ventures. “At Atipica, we think of Inclusive AI in terms of data science, algorithms, and their ethical implications. This way you can rest assure our models are not replicating the biases of humans that hinder diversity while getting patent-pending aggregate demographic insights of your talent pool,” reads the website. She talks about her journey as a Latina woman in the tech industry. She reminisced on how she was the only one like her who got an internship with Hewlett Packard and the fact that she hated it. Nevertheless, she still decided to stay, determined not to let the industry turn her into a victim. She believes she made the right choice going forward with tech; now, years later, diversity is dominating the conversation in the industry. After HP, she also worked at Twitter and YouTube, helping them translate and localize their applications for a global audience. She is also a founding advisor of Project Include, which is a non-profit organization run by women, that uses data and advocacy to accelerate diversity and inclusion solutions in the tech industry. She opened her talk by agreeing to a quote from Safiya Noble, who wrote Algorithms of Oppression. “Artificial Intelligence will become a major human rights issue in the twenty-first century.” She believes we need to talk about difficult questions such as where AI is heading? And where should we hold ourselves and each other accountable.” She urges people to evaluate their role in AI, bias, and inclusion, to find the empathy and value in difficult conversations, and to go beyond your immediate surroundings to consider the broader consequences. It is important to build accountable AI in a way that allows humanity to triumph. She touched upon discriminatory moves by tech giants like Amazon and Google. Amazon recently killed off its AI recruitment tool because it couldn’t stop discriminating against women. She also criticized upon Facebook’s Myanmar operation where Facebook data scientists were building algorithms for hate speech. They didn’t understand the importance of localization or language or actually internationalize their own algorithms to be inclusive towards all the countries. She also talked about algorithmic bias in library discovery systems, as well as how even ‘black robots’ are being impacted by racism. She also condemned Palmer Luckey's work who is helping U.S. immigration agents on the border wall identify Latin refugees. Finally, she urged people to take three major steps to progress towards being inclusive: Be an ally Think of inclusion as an approach, not a feature Work towards an Ethical AI Head over to NeurIPS facebook page for the entire talk and other sessions happening at the conference this week. NeurIPS 2018: Deep learning experts discuss how to build adversarially robust machine learning models NeurIPS 2018 paper: DeepMind researchers explore autoregressive discrete autoencoders (ADAs) to model music in raw audio at scale NeurIPS 2018: A quick look at data visualization for Machine learning by Google PAIR researchers [Tutorial]

The AI Now Institute, New York University, released its third annual report on the current state of AI, yesterday.  2018 AI Now Report focused on themes such as industry AI scandals, and rising inequality. It also assesses the gaps between AI ethics and meaningful accountability, as well as looks at the role of organizing and regulation in AI. Let’s have a look at key recommendations from the AI Now 2018 report. Key Takeaways Need for a sector-specific approach to AI governance and regulation This year’s report reflects on the need for stronger AI regulations by expanding the powers of sector-specific agencies (such as United States Federal Aviation Administration and the National Highway Traffic Safety Administration) to audit and monitor these technologies based on domains. Development of AI systems is rising and there aren’t adequate governance, oversight, or accountability regimes to make sure that these systems abide by the ethics of AI. The report states how general AI standards and certification models can’t meet the expertise requirements for different sectors such as health, education, welfare, etc, which is a key requirement for enhanced regulation. “We need a sector-specific approach that does not prioritize the technology but focuses on its application within a given domain”, reads the report. Need for tighter regulation of Facial recognition AI systems Concerns are growing over facial recognition technology as they’re causing privacy infringement, mass surveillance, racial discrimination, and other issues. As per the report, stringent regulation laws are needed that demands stronger oversight, public transparency, and clear limitations. Moreover, only providing public notice shouldn’t be the only criteria for companies to apply these technologies. There needs to be a “high threshold” for consent, keeping in mind the risks and dangers of mass surveillance technologies. The report highlights how “affect recognition”, a subclass of facial recognition that claims to be capable of detecting personality, inner feelings, mental health, etc, depending on images or video of faces, needs to get special attention, as it is unregulated. It states how these claims do not have sufficient evidence behind them and are being abused in unethical and irresponsible ways.“Linking affect recognition to hiring, access to insurance, education, and policing creates deeply concerning risks, at both an individual and societal level”, reads the report. It seems like progress is being made on this front, as it was just yesterday when Microsoft recommended that tech companies need to publish documents explaining the technology’s capabilities, limitations, and consequences in case their facial recognition systems get used in public. New approaches needed for governance in AI The report points out that internal governance structures at technology companies are not able to implement accountability effectively for AI systems. “Government regulation is an important component, but leading companies in the AI industry also need internal accountability structures that go beyond ethics guidelines”, reads the report.  This includes rank-and-file employee representation on the board of directors, external ethics advisory boards, along with independent monitoring and transparency efforts. Need to waive trade secrecy and other legal claims The report states that Vendors and developers creating AI and automated decision systems for use in government should agree to waive any trade secrecy or other legal claims that would restrict the public from full auditing and understanding of their software. As per the report, Corporate secrecy laws are a barrier as they make it hard to analyze bias, contest decisions, or remedy errors. Companies wanting to use these technologies in the public sector should demand the vendors to waive these claims before coming to an agreement. Companies should protect workers from raising ethical concerns It has become common for employees to organize and resist technology to promote accountability and ethical decision making. It is the responsibility of these tech companies to protect their workers’ ability to organize, whistleblow, and promote ethical choices regarding their projects. “This should include clear policies accommodating and protecting conscientious objectors, ensuring workers the right to know what they are working on, and the ability to abstain from such work without retaliation or retribution”, reads the report. Need for more in truth in advertising of AI products The report highlights that the hype around AI has led to a gap between marketing promises and actual product performance, causing risks to both individuals and commercial customers. As per the report, AI vendors should be held to high standards when it comes to them making promises, especially when there isn’t enough information on the consequences and the scientific evidence behind these promises. Need to address exclusion and discrimination within the workplace The report states that the Technology companies and the AI field focus on the “pipeline model,” that aims to train and hire more employees. However, it is important for tech companies to assess the deeper issues such as harassment on the basis of gender, race, etc, within workplaces. They should also examine the relationship between exclusionary cultures and the products they build, so to build tools that do not perpetuate bias and discrimination. Detailed account of the “full stack supply chain” As per the report, there is a need to better understand the parts of an AI system and the full supply chain on which it relies for better accountability. “This means it is important to account for the origins and use of training data, test data, models, the application program interfaces (APIs), and other components over a product lifecycle”, reads the paper. This process is called accounting for the ‘full stack supply chain’ of AI systems, which is necessary for a more responsible form of auditing. The full stack supply chain takes into consideration the true environmental and labor costs of AI systems. This includes energy use, labor use for content moderation and training data creation, and reliance on workers for maintenance of AI systems. More funding and support for litigation, and labor organizing on AI issues The report states that there is a need for increased support for legal redress and civic participation. This includes offering support to public advocates representing people who have been exempted from social services because of algorithmic decision making, civil society organizations and labor organizers who support the groups facing dangers of job loss and exploitation. Need for University AI programs to expand beyond computer science discipline The report states that there is a need for university programs and syllabus to expand its disciplinary orientation. This means the inclusion of social and humanistic disciplines within the universities AI programs. For AI efforts to truly make social impacts, it is necessary to train the faculty and students within the computer science departments, to research the social world. A lot of people have already started to implement this, for instance, Mitchell Baker, chairwoman, and co-founder of Mozilla talked about the need for the tech industry to expand beyond the technical skills by bringing in humanities. “Expanding the disciplinary orientation of AI research will ensure deeper attention to social contexts, and more focus on potential hazards when these systems are applied to human populations”, reads the paper. For more coverage, check out the official AI Now 2018 report. Unity introduces guiding Principles for ethical AI to promote responsible use of AI Teaching AI ethics – Trick or Treat? Sex robots, artificial intelligence, and ethics: How desire shapes and is shaped by algorithms

This year the Neural Information Processing Systems Conference is asking serious questions to improve diversity, equity, and inclusion at NeurIPS. “Our goal is to make the conference as welcoming as possible to all.” said the heads of the new diversity and inclusion chairs introduced this year. https://twitter.com/InclusionInML/status/1069987079285809152 The Diversity and Inclusion chairs were headed by Hal Daume III, a professor from the University of Maryland and machine learning and fairness groups researcher at Microsoft Research and Katherine Heller, assistant professor at Duke University and research scientist at Google Brain. They opened up the talk by acknowledging the respective privilege that they get as a group of white man and woman and the fact that they don’t reflect the diversity of experience in the conference room, much less the world. They talk about the three major goals with respect to inclusion at NeurIPS: Learn about the challenges that their colleagues have faced. Support those doing the hard work of amplifying the voices of those who have been historically excluded. To begin structural changes that will positively impact the community over the coming years. They urged attendees to start building an environment where everyone can do their best work. They want people to: see other perspectives remember the feeling of being an outsider listen, do research and learn. make an effort and speak up Concrete actions taken by the NeurIPS diversity and inclusion chairs This year they have assembled an advisory board and run a demographics and inclusion survey. They have also conducted events such as WIML (Women in Machine Learning), Black in AI, LatinX in AI, and Queer in AI. They have established childcare subsidies and other activities in collaboration with Google and DeepMind to support all families attending NeurIPS by offering a stipend of up to $100 USD per day. They have revised their Code of Conduct, to provide an experience for all participants that is free from harassment, bullying, discrimination, and retaliation. They have added inclusion tips on Twitter offering tips and bits of advice related to D&I efforts. The conference also offers pronoun stickers (only them and they), first-time attendee stickers, and information for participant needs. They have also made significant infrastructure improvements for visa handling. They had discussions with people handling visas on location, sent out early invitation letters for visas, and are choosing future locations with visa processing in mind. In the future, they are also looking to establish a legal team for details around Code of Conduct. Further, they are looking to improve institutional structural changes that support the community, and improve the coordination around affinity groups & workshops. For the first time, NeurIPS also invited a diversity and inclusion (D&I) speaker Laura Gomez to talk about the lack of diversity in the tech industry, which leads to biased algorithms, faulty products, and unethical tech. Head over to NeurIPS website for interesting tutorials, invited talks, product releases, demonstrations, presentations, and announcements. NeurIPS 2018: Deep learning experts discuss how to build adversarially robust machine learning models NeurIPS 2018 paper: DeepMind researchers explore autoregressive discrete autoencoders (ADAs) to model music in raw audio at scale NeurIPS 2018: A quick look at data visualization for Machine learning by Google PAIR researchers [Tutorial]

Yesterday, Google employees have signed a petition protesting Google’s infamous Project Dragonfly. “We are Google employees and we join Amnesty International in calling on Google to cancel project Dragonfly”, they wrote on a post on Medium. This petition also marks the first time over 300 Google employees (at the time of writing this post) have used their actual names in a public document. Project Dragonfly is the secretive search engine that Google is allegedly developing which will comply with the Chinese rules of censorship. It has been on the receiving end of constant backlash from various human rights organizations and investigative reporters, since it was revealed earlier this year. On Monday, it also faced critique from human rights organization Amnesty International. Amnesty launched a petition opposing the project, and coordinated protests outside Google offices around the world including San Francisco, Berlin, Toronto and London. https://twitter.com/amnesty/status/1067488964167327744 Yesterday, Google employees joined Amnesty and wrote an open letter to the firm. “We are protesting against Google’s effort to create a censored search engine for the Chinese market that enables state surveillance. Our opposition to Dragonfly is not about China: we object to technologies that aid the powerful in oppressing the vulnerable, wherever they may be. Dragonfly in China would establish a dangerous precedent at a volatile political moment, one that would make it harder for Google to deny other countries similar concessions. Dragonfly would also enable censorship and government-directed disinformation, and destabilize the ground truth on which popular deliberation and dissent rely.” Employees have expressed their disdain over Google’s decision by calling it a money-minting business. They have also highlighted Google’s previous disappointments including Project Maven, Dragonfly, and Google’s support for abusers, and believe that “Google is no longer willing to place its values above its profits. This is why we’re taking a stand.” Google spokesperson has redirected to their previous response on the topic: "We've been investing for many years to help Chinese users, from developing Android, through mobile apps such as Google Translate and Files Go, and our developer tools. But our work on search has been exploratory, and we are not close to launching a search product in China." Twitterati have openly sided with Google employees in this matter. https://twitter.com/Davidramli/status/1067582476262957057 https://twitter.com/shabirgilkar/status/1067642235724972032 https://twitter.com/nrambeck/status/1067517570276868097 https://twitter.com/kuminaidoo/status/1067468708291985408 OK Google, why are you ok with mut(at)ing your ethos for Project DragonFly? Amnesty International takes on Google over Chinese censored search engine, Project Dragonfly. Google’s prototype Chinese search engine ‘Dragonfly’ reportedly links searches to phone numbers

Earlier this month, 20,000 Google employees along with temps, Vendors, and Contractors walked out of their respective Google offices to protest against the discrimination, racism, and sexual harassment that they encountered at Google’s workplace. As a part of the walkout, Google employees had laid out five demands urging Google to bring about structural changes within the workplace. In the latest episode of Recode Decode with Kara Swisher, yesterday, six of the Google walkout organizers, namely, Erica Anderson, Claire Stapleton, Meredith Whittaker, Stephanie Parker, Cecilia O’Neil-Hart and Amr Gaber spoke out about Google’s dismissive approach towards the five demands laid out by the Google employees. A day after the Walkout, Google addressed these demands in a note written by Sundar Pichai, where he admitted that they have “not always gotten everything right in the past” and are “sincerely sorry”. Pichai also mentioned that  “It’s clear that to live up to the high bar we set for Google, we need to make some changes. Going forward, we will provide more transparency into how you raise concerns and how we handle them”. The 'walkout for real change' was a response to the New York Times report, published last month, that exposed how Google has protected its senior executives (Andy Rubin, Android Founder being one of them) that had been accused of sexual misconduct in the recent past. We’ll now have a look at the major highlights from the podcast. Key Takeaways The podcast talks about how the organizers formulated their demands, the rights of contractors at Google, post walkout town hall meeting, and what steps will be taken next by the Google employees. How the walkout mobilized collective action and the formulation of demands As per the Google employees, collating demands was a collective effort from the very beginning. They were inspired by stories of sexual harassment at Google that were floating around in an internal email chain. This urged the organizers of the walkout to send out an email to a large group of women stating that they need to do something about it, to which a lot of employees suggested that they should put out their demands. A doc was prepared in Google Doc Live that listed all the suggested demands by the fellow Googlers. “it was just this truly collective action, living, moving in a Google Document that we were all watching and participating in” said Cecelia O’Neil Hart, a marketer at YouTube.  Cecelia also pointed out that the demands that were being collected were not new and had represented the voices of a lot of groups at Google. “It was just completely a process of defining what we wanted in solidarity with each other. I think it showed me the power of collective action, writing the demands quite literally as a collective” said Cecelia. Rights of Contractors One of the demands laid out by the Google employees as a part of the walkout, states, “commitment to ending pay and opportunity inequity for all levels of the organization”. They expected a change that is applicable to not just full-time employees, but also contract workers as well as subcontract workers, as they are the ones who work at Google with rights that are restricted and different than those of the full-time employees. “We have contractors that manage teams of upwards of 10, 20, even more, other people but left in this second-class state where they don’t have healthcare benefits, they don’t have paid sick leave and they definitely don’t get access to the same well-being resources: Counseling, professional development, any of that”, adds Stephanie Parker, a policy specialist on Trust and Safety, YouTube. Other examples of discrimination against contractors at Google include the shooting at YouTube Headquarters in April where contractor workers (security guards, cafeteria workers, etc) were excluded from the post-shooting town hall meeting conducted by Susan Wojcicki, CEO, YouTube. Also, while the shooting was taking place, all the employees were being updated on the Security via texts, except the contractors. Similarly, the contractors were not allowed in the town hall meeting that was conducted six days post walkout, although the demands applied to them just as much as it did to full-time employees. There’s also systemic racism in hiring and promotion for certain job ladders like engineering, versus other job ladders, versus contract work. Parker mentioned that by including contractors in the five demands, they wanted to bring it to everyone’s attention that despite Google striving to be a company with the best workplace that offers the best benefits, it’s quite far-off from leading in that space. “The solution is to convert them to full-time or to treat them fairly with respect. Not to throw up our hands and say, “Oh well” said Parker. Post walkout town hall meeting Six days after the walkout, a mail was sent over to the employees regarding the town hall meeting, which Google said was accidentally “leaked”. Stapleton, a marketing manager at YouTube, says that the “the town hall was really tough to watch” and that the Google executives “did not ever address, acknowledge, the list of demands nor did they adequately provide solutions to all the five. They did drop forced arbitration, but for sexual harassment only, not discrimination, which was a key omission”. As per the employees, Google seemed to use the same old methods to get the situation under control. Google said that they’ll be focusing on committing to the OKRs (Objective and Key Result) i.e. the main goal for the company as a whole. Moreover, they also tried to play down the other concerns and core issues such as discrimination (apart from sexual), racism, and the abuse of power while only focussing on one kind of behavior i.e. sexual assault. They mentioned how Google refused to address any issues surrounding the TVCs (temps, vendors, and contractors), despite being asked about it in the town hall. Also, Google did not acknowledge that the HR processes and systems within the company are not working. Instead, Google decided to conduct a survey to ensure how people really feel about the HR teams within the workplace. “They heard loud and clear from 20,000 of us that these processes and reporting lines that are in place are set up the wrong way and need to be redesigned so that we normal employees have more of a say and more of a look into the decision-making processes, and they didn’t even acknowledge that as a valid sentiment or idea”, said Parker. All in all, there wasn’t much “leadership”, and there wasn’t an understanding that “accountability was necessary”. Employees want their demands to be met Employees want an employee representative on board to speak on behalf of all the employees. They want accountability systems in place and for Google to begin analyzing the cultures within companies that use racism, discrimination, abuse of power, sexism, the kind that excludes many from power and accrue resources to only a few. The employees acknowledge that Google is continuing to discuss and talk about the issue, but that the employees would have to keep pushing the conversation forward every step of the way. “I think we need to not be afraid to say the real words. I want to hear our execs say the real words like “discrimination,” which was erased from their response to the demands. Like ‘systemic racism’.I want to hear those real words” said Cecelia. Employees also want the demand no. 2 i.e. ending pay inequity specifically to be addressed by Google as all they keep getting in response is that Google is “looking into it” and “studying” about it. “I think that what they have to do is embrace the tough critique that they’ve gotten and try to understand where we’re coming from and make these changes, and make them in collaboration with us, which has not happened,” said Stapleton. Employees continue to be cautiously hopeful Employees believe that Google has incredible people at the company. Thousands of people came together and worked on their vision for the world altogether on something that really mattered. “You know, we’ve called this the ‘Walkout for Real Change’ for a reason. Even if all of our optimism comes true and the best outcome and our demands are met, real change happens over time and we’re going to hold people accountable to that real change actually going down, and hold us accountable for demanding it also, because we’ve got to get the rest of the demands met”, says Cecelia. Our thoughts on this topic Just as history has proven time and again, information and data can be used to drive a narrative that benefits the storyteller and their agendas. Based on collecting feedback from workers across the company, the Google walkout organizers pointed out systemic issues within the company that enabled the sexual predatory behavior. They pointed out that sexual harassment is one of the symptoms and not the cause. They demanded that the root causes be addressed holistically through their set of five demands. To extinguish a movement or dissension in its infancy, regimes and corporations throughout history have used the following tactics: Be the benevolent ruler Divide and conquer the crowd by appealing to individual group needs but never to everyone’s collective demands Find a middle ground by agreeing to some demands while signaling that the other side also takes a few steps forward thereby disengaging those whose demands aren’t met. This would weaken the movement’s leadership Use the information to support the status quo. Promote the influencers into top management roles It appears that Google is using a lot of the approaches to appease the walkout participants. The Google management adopted classic labor negotiation tactics by sanctioning the protest, also encouraging managers to participate, then agreeing to adopt the easiest item on the list of demands which have already been implemented in some other tech companies but restricted it to only their employees. But restricting the reforms to only their employees, and creating a larger distance for TVCs, they seem to be thinning out the protesting crowd. By not engaging in open dialog on all key issues highlighted and by removing key decision makers on top out of the town hall, they have created a situation for deniability. Lastly, by going back to surveying sentiments on key issues, they are not only relying on time to subdue anger felt but also on the grassroots voice to dissipate. Will this be the tipping point for Google employees to unionize? BuzzFeed Report: Google’s sexual misconduct policy “does not apply retroactively to claims already compelled to arbitration” OK Google, why are you ok with mut(at)ing your ethos for Project DragonFly? Following Google, Facebook changes its forced arbitration policy for sexual harassment claims

On 4th November, the New York Times published a scathing report on Facebook that threw the tech giant under scrutiny for its leadership morales. The report pointed out how Facebook has been following the strategy of 'delaying, denying and deflecting’ the blame for all the controversies surrounding it. One of the recent scandals it was involved in was hiring a PR firm- called Definers- who did opposition research and shared content that criticized Facebook’s rivals Google and Apple, diverting focus from the impact of Russian interference on Facebook. They also pushed the idea that liberal financier George Soros was behind a growing anti-Facebook movement. Now, in a memo sent by Elliot Schrage (Facebook’s outgoing Head of Communications and Policy) to Facebook employees and obtained by TechCrunch, he takes the blame for hiring The Definers. Elliot Schrage, who after the Cambridge Analytica scandal, announced in June that he was leaving, admitted that his team asked Definers to push negative narratives about Facebook's competitors. He also stated that Facebook asked Definers to conduct research on liberal financier George Soros. His argument was that after George Soros attacked Facebook in a speech at Davos, calling them a “menace to society”, they wanted to determine if he had any financial motivation. According to the TechCrunch report, Elliot denied that the company asked the PR firm to distribute or create fake news. "I knew and approved of the decision to hire Definers and similar firms. I should have known of the decision to expand their mandate," Schrage said in the memo. He further stresses on being disappointed that a lot of the company’s internal discussion has become public. According to the memo, “This is a serious threat to our culture and ability to work together in difficult times.” Saving Mark and Sheryl from additional finger pointing, Schrage further added "Over the past decade, I built a management system that relies on the teams to escalate issues if they are uncomfortable about any project, the value it will provide or the risks that it creates. That system failed here and I'm sorry I let you all down. I regret my own failure here." As a follow-up note to the memo, Sheryl Sandberg (COO, Facebook) also shares accountability of hiring Deniers. She says “I want to be clear that I oversee our Comms team and take full responsibility for their work and the PR firms who work with us” Conveniently enough, this memo comes after the announcement that Elliot is stepping down from his post at Facebook. Elliot’s replacement, Facebook’s new head of global policy and former U.K. Deputy Prime Minister, Nick Clegg will now be reviewing its work with all political consultants. The entire scandal has led to harsh criticism from the media circle like Kara Swisher and from academics like Scott Galloway. On an episode of Pivot with Kara Swisher and Scott Galloway,  Kara comments that “Sheryl Sandberg ... really comes off the worst in this story, although I still cannot stand the ability of people to pretend that this is not all Mark Zuckerberg’s responsibility,” She further followed up with a jarring comment stating “He is the CEO. He has 60 percent. He’s an adult, and they’re treating him like this sort of adult boy king who doesn’t know what’s going on. It’s ridiculous. He knows exactly what’s going on.” Galloway added that since Sheryl had “written eloquently on personal loss and the important discussion around gender equality”, these accomplishments gave her “unfair” protection, and that it might also be true that she will be “unfairly punished.” He raises questions on both, Mark and Sheryl’s leadership saying “Can you think of any individuals who have made so much money doing so much damage? I mean, they make tobacco executives look like Mister Rogers.” On 19th November, he tweeted a detailed theory on why Sandberg is yet a part of Facebook; because “The Zuck can't be (fired)” and nobody wants to be the board who "fires the woman". https://twitter.com/profgalloway/status/1064559077819326464 Here’s another recent tweet thread from Scott which is a sarcastic take on what a “Big Tech” company actually is: https://twitter.com/profgalloway/status/1065315074259202048 Head over to CNBC to know more about this news. What is Facebook hiding? New York Times reveals Facebook’s insidious crisis management strategy NYT Facebook exposé fallout: Board defends Zuckerberg and Sandberg; Media call and transparency report Highlights BuzzFeed Report: Google’s sexual misconduct policy “does not apply retroactively to claims already compelled to arbitration”  

Two months after the OpenCV team announced the alpha release of Open CV 4.0, the final version 4.0 of OpenCV is here. OpenCV 4.0 was announced last week and is now available as a c++11 library that requires a c++ 11- compliant compiler. This new release explores features such as a G-API module, QR code detector, performance improvements, and DNN improvements among others. OpenCV is an open source library of programming functions which is mainly aimed at real-time computer vision. OpenCV is cross-platform and free for use under the open-source BSD license. Let’s have a look at what’s new in OpenCV 4.0. New Features G-API: OpenCV 4.0 comes with a completely new module opencv_gapi. G-API is an engine responsible for very efficient image processing, based on the lazy evaluation and on-fly construction of the processing graph. QR code detector and decoder: OpenCV 4.0 comprises QR code detector and decoder that has been added to opencv/objdetect module along with a live sample. The decoder is currently built on top of QUirc library. Kinect Fusion algorithm: A popular Kinect Fusion algorithm has been implemented, optimized for CPU and GPU (OpenCL), and integrated into opencv_contrib/rgbd module.  Kinect 2 support has also been updated in opencv/videoio module to make the live samples work. DNN improvements Support has been added for Mask-RCNN model. A new Integrated ONNX parser has been added. Support added for popular classification networks such as the YOLO object detection network. There’s been an improvement in the performance of the DNN module in OpenCV 4.0 when built with Intel DLDT support by utilizing more layers from DLDT. OpenCV 4.0 comes with experimental Vulkan backend that has been added for the platforms where OpenCL is not available. Performance improvements In OpenCV 4.0, hundreds of basic kernels in OpenCV have been rewritten with the help of "wide universal intrinsics". Wide universal intrinsics map to SSE2, SSE4, AVX2, NEON or VSX intrinsics, depending on the target platform and the compile flags. This leads to better performance, even for the already optimized functions. Support has been added for IPP 2019 using the IPPICV component upgrade. For more information, check out the official release notes. Image filtering techniques in OpenCV 3 ways to deploy a QT and OpenCV application OpenCV and Android: Making Your Apps See

This Monday the Department of Commerce, Bureau of Industry and Security (BIS) published a proposal to control the export of AI from USA. This move seems to lean towards restricting AI tech going out of the country to protect the national security of USA. The areas that come under the licensing proposal Artificial intelligence, as we’ve seen in recent years has great potential for both good and harm. The DoC in the United States of America is not taking any chances with it. The proposal lists many areas of AI that could potentially require a license to be exported to certain countries. Other than computer vision, natural language processing, military-specific products like adaptive camouflage and faceprint for surveillance is also listed in the proposal to restrict the export of AI. The areas major areas listed in the proposal are: Biotechnology including genomic and genetic engineering Artificial intelligence (AI) and machine learning including neural networks, computer vision, and natural language processing Position, Navigation, and Timing (PNT) technology Microprocessor technology like stacked memory on chip Advanced computing technology like memory-centric logic Data analytics technology like data analytics by visualization and analysis algorithms Quantum information and sensing technology like quantum computing, encryption, and sensing Logistics technology like mobile electric power Additive manufacturing like 3D printing Robotics like micro drones and molecular robotics Brain-computer interfaces like mind-machine interfaces Hypersonics like flight control algorithms Advanced Materials like adaptive camouflage Advanced surveillance technologies faceprint and voiceprint technologies David Edelman, a former adviser to ex-US president Barack Obama said: “This is intended to be a shot across the bow, directed specifically at Beijing, in an attempt to flex their muscles on just how broad these restrictions could be”. Countries that could be affected with regulation on export of AI To determine the level of export controls, the department will consider the potential end-uses and end-users of the technology. The list of countries is not clear but ones to which exports are restricted like embargoed countries will be considered. Also, China could be one of them. What does this mean for companies? If your organization creates products in ‘emerging technologies’ then there will be restrictions on the countries you can export to and also on disclosure of technology to foreign nationals in United States. Depending on the criteria, non-US citizens might even need licenses to participate in research and development of such technology. This will restrict non-US citizens to participate and take back anything from, say an advanced AI research project. If the new regulations go into effect, it will affect the security review of foreign investments across these areas. When the list of technologies is finalized, many types of foreign investments will be subject to a review and deals could be halted or undone. Public views on academic research In addition to commercial applications and products, this regulation could also be bad news for academic research. https://twitter.com/jordanbharrod/status/1065047269282627584 https://twitter.com/BryanAlexander/status/1064941028795400193 Even Google Home, Amazon Alexa, iRobot Roomba could be affected. https://twitter.com/R_D/status/1064511113956655105 But it does not look like research papers will be really affected. The document states that the commerce does not intend to expand jurisdiction on ‘fundamental research’ for ‘emerging technologies’ that is intended to be published and not currently subject to EAR as per § 734.8. But will this affect open-source technologies? We really hope not. Deadline for comments is less than 30 days away BIS has invited comments to the proposal for defining and categorizing emerging technologies, the impact of the controls in US technology leadership among other topics. However the short deadline of December 19, 2018 indicates their haste to implement licensing export of AI quickly. For more details, and to know where you can submit your comments, read the proposal. The US Air Force lays groundwork towards artificial general intelligence based on hierarchical model of intelligence Google open sources BERT, an NLP pre-training technique Teaching AI ethics – Trick or Treat?

Last week, 20,000 Google employees along with Temps, Vendors, and Contractors walked out to protest the discrimination, racism, and sexual harassment that they encountered at Google’s workplace. This global walkout by Google workers was a response to the New York times report on Google published last month, shielding senior executives accused of sexual misconduct. Yesterday, Google addressed these demands in a note written by Sundar Pichai to their employees. He admits that they have “not always gotten everything right in the past” and they are “sincerely sorry”  for the same. This supposedly ‘comprehensive’ plan will provide more transparency into how employees raise concerns and how Google will handle them. Here are some of the major changes that caught our attention: Following suite after Uber and Microsoft, Google has eliminated forced arbitration in cases of sexual harassment. Fostering a more transparent nature in reporting a sexual harassment case, employees can now be accompanied with support persons to the meetings with HR. Google is planning to update and expand their mandatory sexual harassment training. They will now be conducting these annually instead of once in two years. If an employee fails to complete his/her training, they will receive a one-rating dock in the employees performance review system. This applies to senior management as well where they could be downgraded from ‘exceeds expectation’ to ‘meets expectation’. They will turn increase focus towards diversity, equity and inclusion in 2019, through hiring, progression and retention, in order to create a more inclusive culture for everyone. Google found that one of the most common factors among the harassment complaints is that the perpetrator was under the influence of alcohol (~20% of cases). Stating the policy again, the plan mentions that excessive consumption of alcohol is not permitted when an employee is at work, performing Google business, or attending a Google-related event, whether onsite or offsite. Going forward, all leaders at the company will be expected to create teams, events, offsites and environments in which excessive alcohol consumption is strongly discouraged. They will be expected to follow the two-drink rule. Although the plan is a step towards making workplace conditions stable, it does leave out some of the more inherent concerns related to structural changes as stated by the organizers of the Google Walkout. For example, the structural inequity that separates ‘full time’ employees from contract workers. Contract workers make up more than half of Google’s workforce, and perform essential roles across the company. However, they receive few of the benefits associated with tech company employment. They are also largely women, people of color, immigrants, and people from working class backgrounds. “We demand a truly equitable culture, and Google leadership can achieve this by putting employee representation on the board and giving full rights and protections to contract workers, our most vulnerable workers, many of whom are Black and Brown women.” -Google Walkout Organizer Stephanie Parker Google’s plan to bring transparency at the workplace looks like a positive step towards improving their workplace culture. It would be interesting to see how the plan works out for Google’s employees, as well as other organizations using this as an example to maintain a peaceful workplace environment for their workers. You can head over to Medium.com to read the #GoogleWlakout organizers’ response to the update. Head over to Pichai’s blog post for details on the announcement itself. Technical and hidden debts in machine learning – Google engineers’ give their perspective 90% Google Play apps contain third-party trackers, share user data with Alphabet, Facebook, Twitter, etc: Oxford University Study OK Google, why are you ok with mut(at)ing your ethos for Project DragonFly?