Fairness in algorithms: Hans Sigrist Prize to Aaron Roth

 The University of Bern's Hans Sigrist Prize has been awarded to Penn computer scientist Aaron Roth, and will be celebrated today.

Here are today's symposium details and schedule:

The Hans Sigrist Symposium

Data Science: The Power of the Human Mind for the Sake of Humankind

Here's an interview:

Aaron Roth: Pioneer of fair algorithms  In December 2023, the most highly endowed prize of the University of Bern will go to the US computer scientist Aaron Roth. His research aims to incorporate social norms into algorithms and to better protect privacy.  by Ivo Schmucki 

"There are researchers who sit down and take on long-standing problems and just solve them, but I am not smart enough to do that," says Aaron Roth. "So, I have to be the other kind of researcher. I try to define a new problem that no one has worked on yet but that might be interesting."

"Aaron Roth's own modesty may stand in the way of understanding the depth of his contributions. In fact, when he authored his doctoral thesis on differential privacy about 15 years ago and then wrote on the fairness of algorithms a few years later, terms like “Artificial Intelligence” and “Machine Learning” were far from being as firmly anchored in our everyday lives as they are today. Aaron Roth was thus a pioneer, laying the foundation for a new branch of research.

"I am interested in real problems. Issues like data protection are becoming increasingly important as more and more data is generated and collected about all of us," says Aaron Roth about his research during the Hans Sigrist Foundation’s traditional interview with the prize winner. He focuses on algorithmic fairness, differential privacy, and their applications in machine learning and data analysis.

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"It is important that more attention is paid to these topics," says Mathematics Professor Christiane Tretter, chair of this year's Hans Sigrist Prize Committee. Tretter says that many people perceive fairness and algorithms as two completely different poles, situated in different disciplines and incompatible with each other. "It is fascinating that Aaron Roth’s work shows that this is not a contradiction."

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"The first step to improving the analysis of large data sets is to be aware of the problem: "We need to realize that data analysis can be problematic. Once we agree on this, we can consider how we can solve the problems," says Aaron Roth."