- PhD Student
Biography
Michelle Seng Ah Lee is a Ph.D. candidate at the Dept. of Computer Science & Technology in the Compliant and Accountable Systems group, supervised by Jat Singh and Jon Crowcroft. 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, supervised by Luciano Floridi. She completed her undergraduate degree at Stanford University. Michelle's Ph.D. is funded by Aviva.
Outside of Cambridge, Michelle works part-time as a Senior Manager in Risk Analytics at Deloitte UK, 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.
Themes
Teaching
Supervised research projects in:
- Detecting undesired bias risk in AI-as-a-service and Auto-ML (in progress)
- Evaluating algorithmic fairness without individual-level demographic data (in progress)
- Mitigating racial bias in harmful tweet detection (completed and published)
Publications
Articles
Lee, Michelle Seng Ah, Luciano Floridi, and Jatinder Singh. “From fairness metrics to key ethics indicators (KEIs): a context-aware approach to algorithmic ethics in an unequal society.” [Under review]. Link to pre-print
Lee, Michelle Seng Ah and Jatinder Singh. “Risk identification questionnaire for unintended bias in machine learning development lifecycle.” [Under review at AIES 2021]. Link to pre-print
Lee, Michelle Seng Ah and Jatinder Singh. “The Landscape and Gaps in Open Source Fairness Toolkits.” [Accepted and pending publication at ACM CHI 2021]. Link to pre-print
Lee, Michelle Seng Ah and Luciano Floridi. “Algorithmic fairness: from absolute conditions to relational trade-offs.” Minds and Machines (2020). Link
Lee, Michelle Seng Ah and Jatinder Singh. “Spelling errors and non-standard language in peer-to-peer loan applications and the borrower’s probability of default.” [Under review for IEEE Transactions Journal]. Link to pre-print
Lee, Michelle Seng Ah. “Context-conscious fairness in using machine learning to make decisions.” AI Matters 5.2 (2019): 23-29. Link
Lee, Michelle Seng Ah, Luciano Floridi, and Alexander Denev. “Innovating with confidence: Embedding AI governance and fairness in financial services risk management framework.” Berkeley Technology Law Journal (2020). Link
Seyyed, Ahmad Javadi, Richard Cloete, Jennifer Cobbe, Michelle Seng Ah Lee and Jatinder Singh. “Monitoring ‘artificial intelligence as a service’ for misuse.” Proceedings of the 2020 AAAI/ACM Conference on AI, Ethics, and Society (2020). Link
J. Cobbe, M. Lee, H. Janssen and J. Singh, “Centering the Law in the Digital State” in Computer (2020), vol. 53, no. 10, pp. 47-58. Link
Book chapters
Lee, Michelle Seng Ah, Jennifer Cobbe, Heleen Janssen, and Jatinder Singh. “Shifting the conceptual framing of guidance from AI/ADM technology to specific risk factors.” European Data Protection Handbook 2021. [accepted and pending publication]
