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Michelle Seng Ah Lee Discusses Challenges of Algorithmic Fairness in Financial Services
As the financial services sector increasingly adopts advanced algorithms, including machine learning and AI, there has been greater regulatory and public scrutiny on the potential for algorithms to replicate unfair bias and discrimination against disadvantaged groups, exacerbating existing inequalities. This webinar will walk through use cases in mortgage lending and in peer-to-peer lending to discuss the complex challenges of trying to make the algorithms "fair" in evaluating credit risk of minority groups.

The presentation will last approximately 40 minutes followed by 15-20 minutes for Q&A. Session will be recorded, and the recording link will be sent to those who have registered for the webinar.
In preparation for the webinar, please read
- "Algorithmic Fairness in Mortgage Lending: from Absolute Conditions to Relational Trade-offs" : https://link.springer.com/article/10.1007/s11023-020-09529-4#Abs1
- "Spelling errors and non-standard language in peer-to-peer loan applications and the borrower's probability of default" https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3609834

Optional introductory reading for non-technical audience:
- “Challenges of fairness in AI decisions": https://ukfinancialservicesinsights.deloitte.com/post/102ftmq/challenges-of-fairness-in-ai-decisions
- "Context-conscious fairness in using machine learning to make decisions": https://dl.acm.org/doi/10.1145/3340470.3340477
- "Innovating with Confidence: Embedding AI Governance and Fairness in a Financial Services Risk ManagementFramework" : https://btlj.org/2020/01/innovating-with-confidence-embedding-ai-governance-and-fairness-in-a-financial-services-risk-management-framework/

Jul 29, 2020 01:00 PM in Eastern Time (US and Canada)

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Speakers

Michelle Seng Ah Lee
PhD Researcher, Compliant & Accountable Systems Research Group @University of Cambridge
Michelle Seng Ah Lee is a Ph.D. candidate at the Dept. of Computer Science & Technology in the Compliant and Accountable Systems group. Her research focuses on fairness, bias, and discrimination in machine learning algorithms and their trade-offs on aggregate and individual levels. Michelle holds an MSc in Social Data Science from the Oxford Internet Institute, where she was a part of the Digital Ethics Lab. She completed her undergraduate degree at Stanford University. Outside of Cambridge, Michelle is a part-time Manager in Risk Analytics at a Big 4 professional services firm, specialising in designing the enterprise AI ethics framework and controls library for financial services. Michelle is an active volunteer and former UK Chapter Lead at DataKind, a pro bono data science charity.