Practical Considerations

There are a bunch of algorithms presented in this book that can be used to solve real-world problems. In this chapter, we’ll examine the practicality of the algorithms presented in this book. Our focus will be on their real-world applicability, potential challenges, and overarching themes, including utility and ethical implications.

This chapter is organized as follows. We will start with an introduction. Then, we will present the issues around the explainability of an algorithm, which is the degree to which the internal mechanics of an algorithm can be explained in understandable terms. Then, we will present the ethics of using an algorithm and the possibility of creating biases when implementing them. Next, the techniques for handling NP-hard problems will be discussed. Finally, we will investigate factors that should be considered before choosing an algorithm.

By the end of this chapter, you will have learned about the practical considerations...

Challenges facing algorithmic solutions

In addition to designing, developing, and testing an algorithm, in many cases, it is important to consider certain practical aspects of starting to rely on a machine to solve a real-world problem. For certain algorithms, we may need to consider ways to reliably incorporate new, important information that is expected to keep changing even after we have deployed our algorithm. For example, the unexpected disruption of global supply chains may negate some of the assumptions we used to train a model predicting the profit margins for a product. We need to carefully consider whether incorporating this new information will change the quality of our well-tested algorithm in any way. If so, how is our design going to handle it?

Expecting the unexpected

Most solutions to real-world problems developed using algorithms are based on some assumptions. These assumptions may unexpectedly change after the model has been deployed. Some algorithms use...

Failure of Tay, the Twitter AI bot

Let’s present the classical example of Tay, which was presented as the first-ever AI Twitter bot created by Microsoft in 2016. Using an AI algorithm, Tay was trained as an automated Twitter bot capable of responding to tweets about a particular topic. To achieve that, it had the capability of constructing simple messages using its existing vocabulary by sensing the context of the conversation. Once deployed, it was supposed to keep learning from real-time online conversations and by augmenting its vocabulary of the words used often in important conversations. After living in cyberspace for a couple of days, Tay started learning new words. In addition to some new words, unfortunately, Tay picked up some words from the racism and rudeness of ongoing tweets. It soon started using newly learned words to generate tweets of its own. A tiny minority of these tweets were offensive enough to raise a red flag. Although it exhibited intelligence and...

The explainability of an algorithm

First, let us differentiate between a black box and a white box algorithm:

• A black box algorithm is one whose logic is not interpretable by humans either due to its complexity or due to its logic being represented in a convoluted manner.
• A white box algorithm is one whose logic is visible and understandable to a human.

Explainability in the context of machine learning refers to our capacity to grasp and articulate the reasons behind an algorithm’s specific outputs. In essence, it gauges how comprehensible an algorithm’s inner workings and decision pathways are to human cognition.

Many algorithms, especially within the machine learning sphere, are often termed “black box” due to their opaque nature. For instance, consider neural networks, which we delve into in Chapter 8, Neural Network Algorithms. These algorithms, which underpin many deep learning applications, are quintessential examples...

Understanding ethics and algorithms

Algorithmic ethics, also known as computational ethics, delves into the moral dimensions of algorithms. This crucial domain aims to ensure that machines operating on these algorithms uphold ethical standards. The development and deployment of algorithms may inadvertently foster unethical outcomes or biases. Crafting algorithms poses a challenge in anticipating their full range of ethical repercussions. When we discuss large-scale algorithms in this context, we refer to those processing vast volumes of data. However, the complexity is magnified when multiple users or designers contribute to an algorithm, introducing varied human biases. The overarching goal of algorithmic ethics is to spotlight and address concerns arising in these areas:

• Bias and discrimination: There are many factors that can affect the quality of the solutions created by algorithms. One of the major concerns is unintentional and algorithmic bias. The reason may be the...

Reducing bias in models

As we have discussed, the bias in a model is about certain attributes of a particular algorithm that cause it to create unfair outcomes. In the current world, there are known, well-documented general biases based on gender, race, and sexual orientation. It means that the data we collect is expected to exhibit those biases unless we are dealing with an environment where an effort has been made to remove them before collecting the data.

Most of the time, bias in algorithms is directly or indirectly introduced by humans. Humans introduce bias either unintentionally through negligence or intentionally through subjectivity. One of the reasons for human bias is the fact that the human brain is vulnerable to cognitive bias, which reflects a person’s own subjectivity, beliefs, and ideology in both the data process and logic creation process of an algorithm. Human bias can be reflected either in data used by the algorithm or in the formulation of the algorithm...

When to use algorithms

Algorithms are like tools in a practitioner’s toolbox. First, we need to understand which tool is the best one to use under the given circumstances. Sometimes, we need to ask ourselves whether we even have a solution for the problem we are trying to solve and when the right time to deploy our solution is. We need to determine whether the use of an algorithm can provide a solution to a real problem that is actually useful, rather than the alternative. We need to analyze the effect of using the algorithm in terms of three aspects:

• Cost: Can use justify the cost related to the effort of implementing the algorithm?
• Time: Does our solution make the overall process more efficient than simpler alternatives?
• Accuracy: Does our solution produce more accurate results than simpler alternatives?

To choose the right algorithm, we need to find the answers to the following questions:

• Can we simplify the problem by making assumptions...

Summary

In this chapter, we learned about the practical aspects that should be considered while designing algorithms. We looked into the concept of algorithmic explainability and the various ways we can provide it at different levels. We also looked into the potential ethical issues in algorithms. Finally, we described which factors to consider while choosing an algorithm.

Algorithms are engines in the new automated world that we are witnessing today. It is important to learn about, experiment with, and understand the implications of using algorithms. Understanding their strengths and limitations and the ethical implications of using algorithms will go a long way in making this world a better place to live in, and this book is an effort to achieve this important goal in this ever-changing and evolving world.

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