Discrimination and Equality in Algorithmic Decision-making
Algorithms are often used to supplement or make decisions in a way that “optimizes'' some objective; often, these decisions are made under limited resource constraints specific to the given domain. The MD4SG group on Discrimination and Equity in Algorithmic Decision-making focuses on understanding how these optimization choices, constraints, and mechanisms impact different stakeholders of algorithmic systems. This group has larger biweekly meetings, as well as smaller project subgroups focusing on different research areas such as understanding the impacts of ranking problems and design of resource allocation mechanisms. Larger biweekly group meetings will discuss topics including, but not limited to, long-term effects and feedback loops, impacts of resource constraints, implications of discrimination metrics, and contextualization across different domains such as education, hiring, and the gig economy.
Ongoing Projects
- Bridging Mechanism Design and Machine Learning towards Algorithmic Fairness
Working Group Organizers
| Richard Lanas Phillips | Ph.D. Student in Computer Science | Cornell University |
| Samuel Galler | Social Sector Consultant for Major Foundations and Nonprofits | Redstone Strategy Group |
Working Group Members
| Shubham Singh | University of Illinois at Chicago |
| Savannah Thais | Princeton University |
| Kate Donahue | Cornell University |
| Sandro Radovanović | University of Belgrade |
| Soham Mukherjee | Purdue University |
| Jose M. Alvarez | University of Pisa |
| Alejandro Bellogin | Universidad Autónoma de Madrid, Spain |
| Sakina Hansen | Graduate Data Scientist Office for National Statistics |
| Violet (Xinying) Chen | Carnegie Mellon University |
| Elie Alhajjar | USMA |
| Corinna Hertweck | University of Zurich and Zurich University of Applied Sciences |
| Carlos Mougan | University of Southampton |
| Jakob Schoeffer | Karlsruhe Institute of Technology |