On the occasion of International Women’s Day (March 8th), IHES is thrilled to host Solenne Gaucher, Assistant Professor at École polytechnique, for a public lecture (in English) titled “Unbiasing AI – From Biased Data to Fair Algorithms” on March 6 at 6:30 PM at the Institute’s Marilyn & James Simons Conference Center.
Artificial intelligence (AI) is increasingly shaping the decisions that affect our lives—from hiring and education to healthcare and access to social services. While AI promises efficiency and objectivity, it also carries the risk of perpetuating and even amplifying societal biases embedded in the data used to train these systems. Many real-world examples highlight the dangers of relying on automated decision-making, as these algorithms can reinforce existing inequalities and discrimination.
In her talk, Solenne Gaucher will explore the challenges of algorithmic fairness, unraveling the complexities of how machine learning algorithms can unintentionally lead to biased and unfair outcomes. Real-world examples, will demonstrate the intricate ways in which data and algorithms intersect, revealing the origins of bias and its profound implications for decision-making systems. Along the way, Solenne will highlight innovative approaches aimed at addressing these biases, offering insights into how AI systems can be designed to promote inclusivity and fairness.
The talk will also be streamed live via this Zoom link.
Solenne Gaucher is a French mathematician and machine learning researcher. Her work focuses on developing fair machine learning algorithms to adress algorithmic biaises and mitigate their broader societal impact. She is currently an assistant professor at the Centre de Mathématiques Appliquées at École polytechnique. Prior to this role, she completed a postdoctoral fellowship at the Center for Research in Economics and Statistics (CREST) at ENSAE Paris. Solenne holds a Ph.D. in mathematics from Université Paris-Saclay. She was awarded the "Young French Talent" prize by the L'Oréal-UNESCO Foundation For Women in Science.
