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A Bayesian Approach to Identifying Representational Errors
Ramya Ramakrishnan, Vaibhav Unhelkar, Ece Kamar +1
Trained AI systems and expert decision makers can make errors that are often difficult to identify and understand. Determining the root cause for these errors can improve future de…
Understanding the Representation and Representativeness of Age in AI Data Sets
Joon Sung Park, Michael S. Bernstein, Robin N. Brewer +2
A diverse representation of different demographic groups in AI training data sets is important in ensuring that the models will work for a large range of users. To this end, recent…
Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs
Solon Barocas, Anhong Guo, Ece Kamar +5
Disaggregated evaluations of AI systems, in which system performance is assessed and reported separately for different groups of people, are conceptually simple. However, their des…