Promises and Challenges of Causality for Ethical Machine Learning

no code implementations • 26 Jan 2022 • Aida Rahmattalabi, Alice Xiang

In recent years, there has been increasing interest in causal reasoning for designing fair decision-making systems due to its compatibility with legal frameworks, interpretability for human stakeholders, and robustness to spurious correlations inherent in observational data, among other factors.

BIG-bench Machine Learning Causal Inference +3

Learning Resource Allocation Policies from Observational Data with an Application to Homeless Services Delivery

no code implementations • 25 Jan 2022 • Aida Rahmattalabi, Phebe Vayanos, Kathryn Dullerud, Eric Rice

The resources are assigned in a first come first served (FCFS) fashion according to an eligibility structure that encodes the resource types that serve each queue.

Causal Inference Fairness +1

Fair Influence Maximization: A Welfare Optimization Approach

no code implementations • 14 Jun 2020 • Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Max Izenberg, Ryan Brown, Eric Rice, Milind Tambe

Under this framework, the trade-off between fairness and efficiency can be controlled by a single inequality aversion design parameter.

Fairness Management