This page contains links to relevant workshops, projects, and principle documents.
Workshops and Events
- Fairness, Accountability, and Transparency in Machine Learning (FAT/ML), 2014-2017
- Workshop on Responsible Recommendation (FAT/Rec), RECSYS 2017
- Workshop on Data and Algorithmic Bias, CIKM 2017
- Singapore Workshop on Fairness, Accountability and Transparency in AI and Big Data, 2017
- Ethics in Natural Language Processing, EACL 2017
- Workshop on Fairness, Accountability, and Transparency on the Web, WWW 2017
- Special Session on Explainability of Learning Machines, IJCNN 2017
- Workshop on Data and Algorithmic Transparency (DAT), 2016
- The Human Use of Machine Learning: An Interdisciplinary Workshop, IEEE SMC
- International Workshop on Privacy and Discrimination in Data Mining, IEEE ICDM 2016
- Machine Learning and the Law, NIPS 2016
- Interpretable Machine Learning for Complex Systems, NIPS 2016
- Workshop on Human Interpretability in Machine Learning, ICML 2016
- Workshop on the Ethics of Online Experimentation, WSDM 2016
- Auditing Algorithms From the Outside: Methods and Implications, ICWSM 2015
- Discrimination and Privacy-Aware Data Mining, IEEE ICDM 2012
- Workshop on Novelty and Diversity in Recommender Systems, ACM RECSYS 2011
Numerous groups are conducting research, building tools, and developing policy statements related to fairness, accountability, and transparency in socio-technical systems.
- Optimizing Government: Policy Challenges in the Machine Learning Age, University of Pennsylvania
- Responsible Data Science, Eindhoven University of Technology, Leiden University, University of Amsterdam, Radboud University Nijmegen, Tilburg University, VU University, Amsterdam Medical Center, VU Medical Center, Leiden University Medical Center, Delft University of Technology, and CWI (National Research Institute for Mathematics and Computer Science)
- Explainable Artificial Intelligence (XAI), Defense Advanced Research Projects Agency
- Computer science and legal methods for enforcing the personal rights of non-discrimination and privacy in ICT systems, Italian Fund for Basic Research
- Data Mining without Discrimination, Netherlands Organisation for Scientific Research
- On algorithmic fairness, discrimination and disparate impact, Haverford College
- The GenderMag Project, Oregon State University and many others
- Auditing Algorithms @ Northeastern, Northeastern University
Software and libraries that implement fair learning algorithms or facilitate algorithm auditing.
Principles and Best Practices
Guidelines and documentation developed by standards bodies, practitioners, and researchers.