Fairness and foundations in machine learning
July 13, 2026 – July 17, 2026
This workshop, sponsored by AIM and the NSF, will advance mathematically rigorous methods for fairness and privacy in machine learning and deepen the mathematical understanding of the underlying problems. One thrust of the workshop will advance algorithmic methods to detect and mitigate bias, including deeper study of how embeddings represent topics and potentially propagate bias. Motivated by privacy regulations and the need to remove data influence without retraining, a second thrust focuses on machine unlearning, covering efficient algorithms and provable certification, with strategies for underspecified data. A third thrust will focus on differential privacy in fair ML.
The workshop aims to seed new collaborations and foster a community of researchers at the interface of mathematics, ML foundations, fairness, privacy, and unlearning. The main topics for the workshop are:
- Algorithmic and mathematical foundations of fairness in ML.
- Algorithmic and mathematical foundations of machine unlearning.
- Differential privacy and its tradeoffs with other desired properties of ML systems, such as fairness.
This event will be run as an AIM-style workshop. Participants will be invited to suggest open problems and questions before the workshop begins, and these will be posted on the workshop website. These include specific problems on which there is hope of making some progress during the workshop, as well as more ambitious problems which may influence the future activity of the field. Lectures at the workshop will be focused on familiarizing the participants with the background material leading up to specific problems, and the schedule will include discussion and parallel working sessions.
For more information email workshops@aimath.org