Ethics in machine learning

Thank you for tuning in to this audio only podcast presentation. This is week 65 of The Lindahl Letter publication. A new edition arrives every Friday. This week the machine learning or artificial intelligence related topic under consideration is, “Ethics in machine learning.”

Ethics should be a part of every machine learning course. It has to be a part of every machine learning journey. Perhaps the best way to sum it up as an imperative would be to say, “Just because you can do a thing does not mean you should.” Machine learning opens the door to some incredibly advanced possibilities for drug discovery, medical image screening, or just spam detection to protect your inbox. The choices people make with machine learning use cases is where the technology and ethics have to be aligned. Full stop. That is the point I’m trying to make today and this essay could stop right here. I’m going to carry on anyway to celebrate the point as I consider it to be vitally important. 

No one really solid essay or set of essays on AI/ML ethics jumped out and caught my attention this week during my search. Part of my search involved digging into results from Google Scholar that yielded a ton of different options to read about “ethics in machine learning” [1]. A lot of those articles were about how to introduce ethics to machine learning courses and about the need to consider ethics when building machine learning implementations. Given that those two calls to action are the first things that come up and they are certainly adjacent to the primary machine learning content being shared it might make you take a moment to pause and consider how much the field of machine learning should deeply consider the idea that just because it can do something does not mean you should. Some use cases are pretty basic and the ethics of what is happening is fairly settled. Other use cases walk right up to the edge of what is reasonable in terms of fairness and equity.

An open access article from Nature did catch my attention by Samuele Lo Piano called, “Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward” [2]. That 7 page article has almost 2 pages of references which was pretty intense as citation to content ratios go in published articles. Within my search I was looking for a foundational article or essay that is commonly referenced. I never really did find one. I ended up moving on to an industry driven essay from the team over at Toward Data Science about, “Ethics in machine learning” [3]. That essay did scale back to the basics of the question at hand in terms of how ethical considerations are applied to building machine learning models.

I wanted to refocus my efforts on the macro considerations related to ethics in machine learning at this point. I remembered that Rob May shared a weekend commentary as a part of the Inside AI newsletter recently about the dark side of reducing friction in taking action with advanced technology [4]. Rob even went as far as sharing an article from one of my favorite technology related sources “The Verge” about just how easy and low friction it was to use machine learning to suggest new chemical weapon builds [5]. That is a very real example of where reducing friction to doing a thing opens the door to very problematic actions that illustrate the need for a foundational set of ethics.

If my call to action and introduction of an imperative to the machine learning ethics space were not enough to compel you to ground your efforts, then please consider a hand curated selection of three videos to assist you in your journey. Maybe one of them will catch your attention and help spread the word about ethics in machine learning.

1. In under 2 minutes Dan Frey who is a professor of mechanical engineering at the Massachusetts Institute of Technology (MIT) introduces this video, “Exploring fairness in machine learning.” This pitch goes back to the Comprehensive Initiative on Technology Evaluation (CITE) from the MIT D-Lab which was launched in 2012 and provides a framework you can use for evaluation [6].

2. If you were looking for something less tactical and more discussion oriented, then please consider this much longer 90 minute video from the New York University (NYU) Stern School of Business, Fubon Center AI, Business Speaker Series, titled, “Machine Learning, Ethics, and fairness.” The video is from back on Monday, April 15, 2019 by Dr. Solon Barocas of Cornell University and Professor Foster Provost who is director of the Fubon data analytics and AI intuitive and it really digs into the question of ethics in machine learning.

3. Finally the third curated selection for you is a shift to a 6 minute video from an industry leader.  The IBM Technology and the IBM Cloud group shared a video with Phaedra Biondiris whose title in the video is noted as, “Trustworthy AI Leader: IBM Global Business Services.” This video is more grounded and is probably a good place to wrap up this essay.

Links and thoughts:

Top 5 Tweets of the week:



[2] Lo Piano, S. Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward. Humanit Soc Sci Commun 7, 9 (2020). 





What’s next for The Lindahl Letter?

  • Week 66: Does a digital divide in machine learning exist?
  • Week 67: My thoughts on NFTs
  • Week 68: Publishing a model or selling the API?
  • Week 69: A machine learning cookbook?
  • Week 70: ML and Web3 (decentralized internet)

I’ll try to keep the what’s next list for The Lindahl Letter forward looking with at least five weeks of posts in planning or review. If you enjoyed this content, then please take a moment and share it with a friend. Thank you and enjoy the week ahead.  

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