96 citations · 142 across the 4 of their papers we have counts for
5 papers
Fairness under uncertainty in sequential decisions
Michelle Seng Ah Lee, Kirtan Padh, David Watson +2
Fair machine learning (ML) methods help identify and mitigate the risk that algorithms encode or automate social injustices. Algorithmic approaches alone cannot resolve structural…
Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"
Kornel Lewicki, Michelle Seng Ah Lee, Jennifer Cobbe +1
"AI as a Service" (AIaaS) is a rapidly growing market, offering various plug-and-play AI services and tools. AIaaS enables its customers (users) - who may lack the expertise, data,…
Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits
Wesley Hanwen Deng, Manish Nagireddy, Michelle Seng Ah Lee +4
Recent years have seen the development of many open-source ML fairness toolkits aimed at helping ML practitioners assess and address unfairness in their systems. However, there has…
Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems
Jennifer Cobbe, Michelle Seng Ah Lee, Jatinder Singh
This paper introduces reviewability as a framework for improving the accountability of automated and algorithmic decision-making (ADM) involving machine learning. We draw on an und…
Monitoring Misuse for Accountable 'Artificial Intelligence as a Service'
Seyyed Ahmad Javadi, Richard Cloete, Jennifer Cobbe +2
AI is increasingly being offered 'as a service' (AIaaS). This entails service providers offering customers access to pre-built AI models and services, for tasks such as object reco…