Samuel F. Way, Jean Garcia Gathright, Henriette Cramer
Translation, Tracks & Data: an Algorithmic Bias Effort in Practice
Potential negative outcomes of machine learning and algorithmic bias have gained deserved attention. However, there are still relatively few standard processes to assess and address algorithmic biases in industry practice. Practical tools that integrate into engineers’ workflows are needed. As a case study, we present two tooling efforts to create tools for teams in practice to address algorithmic bias. Both intend to increase understanding of data, models, and outcome measurement decisions. We describe the development of 1) a prototype checklist based on existing literature frameworks; and 2) dashboarding for quantitatively assessing outcomes at scale. We share both technical and organizational lessons learned on checklist perceptions, data challenges and interpretation pitfalls.
Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems
Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, Fernando Diaz
Not so Autonomous, Very Human Decisions in Machine Learning. AAAI Spring Symposium on UX for ML: Designing the User Experience of Machine Learning Systems.
Henriette Cramer, Jennifer Thom