Forget the Trolley Problem; Pragmatic and Fair AI in the Real World

Update: my article has been published on TowardsDataScience, selected as an editor’s pick, and highlighted in TowardsDataScience’s weekly newsletter “The Variable”!

The AI doomsday scenarios, ignited by books such as The Filter Bubble (2011) and Weapons of Math Destruction (2016), are slowly being superseded by more pragmatic and nuanced views of AI. Views in which we acknowledge we’re in control of AI and able to design them in ways that reflect values of our choice.

This shift can be seen in the rising involvement of computer scientists, e.g., through books such as The Ethical Algorithm (2019) or Understand, Manage, and Prevent Algorithmic Mitigate Bias (2019), books that describe and acknowledge the challenges and complexities of algorithmic fairness, but at the same time offer concrete methods and tools for more fair and ethical algorithms. This shift can too be seen in that the methods described in these books have already found their ways into the offerings of all major cloud providers, e.g., at the FAccT 2021 Tutorial “Responsible AI in Industry: Lessons Learned in Practice” Microsoft, Google, and Amazon demoed their fair AI solutions to the multidisciplinary audience of the FAccT community.

The message is clear: we can (and should!) operationalize algorithmic fairness.

Continue reading “Forget the Trolley Problem; Pragmatic and Fair AI in the Real World”

Keynote on Big Data, Machine Learning, and Algorithmic Bias at the Royal Marechaussee

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I was invited to give the opening keynote at the Intelligence Day of the Koninklijke Marechaussee (Military Police) on Big Data and Machine Learning, with the aim to explain the audience what ML and Big Data is.

I spent a disproportionate amount of time on Algorithmic Bias, because I think this is a hugely important topic — in particular for this audience! See the slides of my talk (in Dutch) below, or on slideshare: