References and further reading
- https://fairmlbook.org/tutorial2.html
- https://fairmlbook.org/tutorial2.html
- Nonfunctional requirements verb: https://en.wikipedia.org/wiki/Listofsystemqualityattributes
- https://www.Merriam-webster.com/thesaurus/explainable
- Ethics guidelines for trustworthy AI. The umbrella term implies that the decision-making process of AI systems must be transparent, and the capabilities and purpose of the systems must be openly communicated to those affected. Even though it may not always be possible to provide an explanation for why a model generated a particular output or decision, efforts must be made to make the decision-making process as clear as possible. When the decision-making process of a model is not transparent, it is referred to as a “black box” algorithm and requires special attention. In these cases, other measures such as traceability, auditability, and transparent communication on system capabilities may be required.
- Even though the terms might sound similar, explicability refers to a broader concept of transparency, communication, and understanding in machine learning, while explainability is specifically focused on the ability to provide clear and understandable explanations for how a model makes its decisions. While explainability is a specific aspect of explicability, explicability encompasses a wider range of measures to ensure the decision-making process of a machine learning model is understood and trusted.
- Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images: https://arxiv.org/abs/1412.1897
- https://www.youtube.com/watch?v=93Xv8vJ2acI
- https://fairmlbook.org/tutorial2.html
- https://fairmlbook.org/tutorial2.html
- https://blogs.partner.microsoft.com/mpn/shared-responsibility-ai-2/
- https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai
- https://en.oxforddictionaries.com/definition/ethics
- https://hbswk.hbs.edu/item/minorities-who-whiten-job-resumes-get-more-interviews
- Interpretability is necessary for Machine Learning: https://www.youtube.com/watch?v=93Xv8vJ2acI
- https://www.wired.com/story/googles-ai-guru-computers-think-more-like-brains/
- Geoff Hinton Dismissed The Need For Explainable AI: Experts Explain Why He’s Wrong: https://www.forbes.com/sites/cognitiveworld/2018/12/20/geoff-hinton-dismissed-the-need-for-explainable-ai-8-experts-explain-why-hes-wrong
- In defense of the black box: https://pubmed.ncbi.nlm.nih.gov/30948538/
- https://dictionary.cambridge.org/us/dictionary/english/ymmv
- Interpretability is necessary for Machine Learning: https://www.youtube.com/watch?v=93Xv8vJ2acI
- Interpretable Machine Learning by Christoph Molnar: https://christophm.github.io/interpretable-ml-book/
- Explainable AI: Interpreting, Explaining and Visualizing Deep Learning by Wojciech Samek, et al: https://books.google.co.in/books?id=j5yuDwAAQBAJ
- Fairness and Machine Learning by Matt Kusner, et al: https://fairmlbook.org/
- The Ethics of AI by Nick Bostrom and Eliezer Yudkowsky: https://intelligence.org/files/EthicsofAI.pdf
- Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil: https://www.goodreads.com/book/show/29981085-weapons-of-math-destruction
- Explainable AI (XAI) by Defense Advanced Research Projects Agency (DARPA): https://www.darpa.mil/program/explainable-artificial-intelligence
