Unraveling AI bias

Technical exploration of model bias

Presented by Monitaur CTO Dr. Andrew Clark and Research Scientist Sid Mangalik

What we unraveled

AI bias is one of the most complex business challenges of our time. During this 60-minute event, presented by Dr. Andrew Clark and Sid Mangalik, we covered:

  • An introduction to algorithmic bias
  • Types of bias and how it happens from a technical perspective
  • Why mitigating model bias is so challenging
  • Best practices for addressing bias

This presentation is built for data science leaders who want to strengthen their foundational understanding of model bias. It's also geared toward anyone who is passionate about AI ethics and AI governance.

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Related Resources

Breaking down bias in AI

Making ethical decisions with machine learning algorithms

As one of my favorite professors used to say: "things are complex." What a simple but profound truth. And nothing could be more true when it comes to AI bias. Read more.

How does bias happen, technically?

Dealing with data bias

A common error is focusing on algorithms as the cause of bias. There is truth to this belief, but Machine Learning (ML) models are incredibly unintelligent, despite contrary representations in the media. Read more.

Top bias metrics and how they work

Metrics for detecting bias

How to measure for bias can be a moving target. In this blog post, we examine the common methods to evaluate for bias, and how they conflict with our recommended approaches. Read more.

Meet the presenters

Andrew Clark
Dr. Andrew Clark

Dr. Andrew Clark is Monitaur’s co-founder and Chief Technology Officer. A trusted domain expert on the topic of ML auditing and assurance, Andrew built and deployed ML auditing solutions at Capital One. He has contributed to ML auditing education and standards at organizations including ISACA and ICO in the UK. He currently serves as a key contributor to ISO AI Standards and the NIST AI Risk Management framework. Prior to Monitaur, he also served as an economist and modeling advisor for several very prominent crypto-economic projects while at Block Science.

Andrew received a B.S. in Business Administration with a concentration in Accounting, Summa Cum Laude, from the University of Tennessee at Chattanooga, an M.S. in Data Science from Southern Methodist University, and a Ph.D. in Economics from the University of Reading. He also holds the Certified Analytics Professional and American Statistical Association Graduate Statistician certifications. Andrew is a professionally trained concert trumpeter and Team USA triathlete.

Sid Mangalik
Sid Mangalik

Sid Mangalik is a Ph.D. candidate in natural language processing at Stony Brook University working with Andy Schwartz. His previous studies focused on the intersection of artificial intelligence and psychology, where he worked with Ritwik Banerjee on language use in scientific writing and among abusers. Sid's previous experience also includes a role at Capital One as a Data Engineer.

His current research is working on identifying connections between community-level language and psychological outcomes. He is a Research Scientist at Monitaur, a start-up focused on making machine learning models fairer, safer, and more transparent.

Graphic of Data Understanding Step and Controls
Graphic of Data Preparation Step and Controls
Graphic of Modeling Step and Controls
Graphic of Evaluation Step and Controls
Graphic of Deployment Step and Controls

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Take a small step toward unraveling one of the most complex business challenges of our time.

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