Can algorithms also fight bias instead of entrenching it?

The promise of algorithms is to help us make decisions not only faster and cheaper than humans but also to eliminate cognitive biases and thus arbitrary and subjective discrimination. Free of fatigue and other mental foibles, an ideal algorithm is a wonderful tool. Unfortunately, of course, we now recognize that algorithms often fall short of that ideal. In particular, they are trained on data from the real world, and thus replicate the biases we show as society. It does not have to be that way, however - join us for a lively discussion and debate how to design and use algorithms to fight bias instead of entrenching it, thus making them an agent for societal change, be it in business, academia, or government.

Speaker Bio

Tobias is an ex McKinsey Partner with over 25 years of experience in financial risk management and a scholar of psychology and data science. He has served over a hundred financial institutions on all continents, built McKinsey's Credit Risk Analytics capabilities, and is regarded as a pioneer in data science and fighting both cognitive and algorithmic bias.

In his consulting work, Tobias is focused on developing innovative and bespoke risk management solutions that use advanced analytics/AI, Big Data, nontraditional data, qualitative decision systems, debiasing techniques, behavioural economics, and psychological insights. His unique insights on algorithmic bias and long experience with fighting it are also the subject of a recently published book.

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Speaker : Tobias Baer