The FATED (Fairness, Accountability, and Transparency in Educational Data) 2022 Workshop is held in conjunction with EDM and will be in a hybrid format, with in-person participation in Durham, England, on July 27. This workshop builds on the FATED 2020 workshop at EDM, with a special interest in the following areas:
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Data Set Collection and Preparation: Shared datasets and benchmarks have been invaluable for progress in ML, but while an increasing number of educational datasets are available, there's not yet consensus about what educational datasets are best for comparison in the context of bias detection or correction algorithms. Further, educational data can pose unique challenges when examining questions of fairness because student demographic information is often highly protected by privacy laws.
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Evaluation Protocol and Metric Formulation: There exist a wide variety of metrics and evaluation protocols for quantifying fairness and bias ML. For particular educational data mining and machine learning tasks, which of these metrics most appropriate? What are the pros and cons of different evaluation protocols for empirical research on fairness and bias across common types of educational machine learning and data mining tasks?
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Detection and Countermeasure Design: We look forward to providing a venue for researchers to share their work on algorithmic bias detection and correction specifically in education-related contexts. We also invite discussion about what features of the questions that we address in educational machine learning and the datasets that we use pose particular challenges for detecting and/or addressing algorithmic bias.
We hope this workshop will promote connections among researchers working on fair ML who want to engage with educational data and questions and researchers working on educational machine learning who want to be more attentive to questions of fairness. Further, we hope this workshop provides an opportunity for exchange of ideas and communication among diverse stakeholders, including those working in both industry and academia.
See CFP for details on submissions.
- Collin Lynch, North Carolina State University
- Mirko Marras, University of Cagliari
- Mykola Pechenizkiy, Eindhoven University of Technology
- Anna Rafferty, Carleton College
- Steve Ritter, Carnegie Learning
- Vinitra Swamy, EPFL
- Renzhe Yu, University of California, Irvine