- 50 Years of Test (Un)fairness: Lessons for Machine Learning
- A Comparative Study of Fairness-Enhancing Interventions in Machine Learning
- A Survey Of Methods For Explaining Black Box Models
- A Marauder’s Map of Security and Privacy in Machine Learning
- Challenges for Transparency
- Closing the AI Accountability Gap
- DQI: Measuring Data Quality in NLP
- Explaining by Removing: A Unified Framework for Model Explanation
- Explaining Explanations: An Overview of Interpretability of Machine Learning
- Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI
- Interpretable Machine Learning: Definitions, Methods, and Applications
- Limitations of Interpretable Machine Learning
- Machine Learning Explainability in Finance
- On the Art and Science of Machine Learning Explanations
- Please Stop Explaining Black Box Models for High-Stakes Decisions
- Software Engineering for Machine Learning: A Case Study
- The Mythos of Model Interpretability
- Towards A Rigorous Science of Interpretable Machine Learning
- Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
- The Security of Machine Learning
- Techniques for Interpretable Machine Learning
- Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda
- Underspecification Presents Challenges for Credibility in Modern Machine Learning