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Bias in AI: How Scientists are trying to fix it
February 15, 2021 @ 10:00 am - 11:00 am PST
The growing use of AI in many sensitive areas caused a debate on bias and fairness. The decision making in sensitive areas such as hiring, finances, justice and healthcare can be flawed and skewed due to the biased AI models.
In this talk, we try to explain what bias in AI means, and the ways to detect and prevent it.
Nazanin Zaker Nazanin Zaker is a senior data scientist at Intuit and part of AI lending team. She has also worked on document understanding and actionable insights for QuickBook Advance customers at Inuit. Prior to that, she worked as an AI researcher in companies like Motorola (A Google Company), Cisco and SAP. She is an advocate for women in tech, founder of Women in AI at Intuit and recognized as influencer of the year in Women in Big Data (WiBD) in 2018.
Shir Meir Lador is a Data Science group manager at Intuit, a global leader in the industry of financial management software. Additionally, Shir is the co-founder of PyData Tel Aviv meetups, the co-host of “Unsupervised” (a podcast about data science in Israel), and gives talks at various machine learning and data science conferences and meetups. Shir holds an M.Sc. in electrical engineering and computers with a major in machine learning and signal processing from Ben-Gurion University.
Cynthia Rudin is professor of Computer Science, Electrical and Computer Engineering, and Statistical Science at Duke University. She is well known for her research on machine learning tools that help humans make better decisions, mainly interpretable machine learning. Her work includes variable importance measures, causal inference methods, interpretable deep learning, and methods that can incorporate domain-based constraints and other types of domain knowledge into machine learning. In 2019, she was elected as a Fellow of the American Statistical Association and of the Institute of Mathematical Statistics “for her contributions to interpretable machine learning algorithms, prediction in large scale medical databases, and theoretical properties of ranking algorithms”.