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/