17 citations · 34 across the 7 of their papers we have counts for
5 papers · 1 filter
Allocation Multiplicity: Evaluating the Promises of the Rashomon Set
Shomik Jain, Margaret Wang, Kathleen Creel +1
The Rashomon set of equally-good models promises less discriminatory algorithms, reduced outcome homogenization, and fairer decisions through model ensembles or reconciliation. How…
Automating Transparency Mechanisms in the Judicial System Using LLMs: Opportunities and Challenges
Ishana Shastri, Shomik Jain, Barbara Engelhardt +1
Bringing more transparency to the judicial system for the purposes of increasing accountability often demands extensive effort from auditors who must meticulously sift through nume…
As an AI Language Model, "Yes I Would Recommend Calling the Police": Norm Inconsistency in LLM Decision-Making
Shomik Jain, D Calacci, Ashia Wilson
We investigate the phenomenon of norm inconsistency: where LLMs apply different norms in similar situations. Specifically, we focus on the high-risk application of deciding whether…
Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized
Shomik Jain, Kathleen Creel, Ashia Wilson
Contrary to traditional deterministic notions of algorithmic fairness, this paper argues that fairly allocating scarce resources using machine learning often requires randomness. W…
Algorithmic Pluralism: A Structural Approach To Equal Opportunity
Shomik Jain, Vinith Suriyakumar, Kathleen Creel +1
We present a structural approach toward achieving equal opportunity in systems of algorithmic decision-making called algorithmic pluralism. Algorithmic pluralism describes a state…