17 citations · 34 across the 8 of their papers we have counts for
9 papers
Alignment has a Fantasia Problem
Nathanael Jo, Zoe De Simone, Mitchell Gordon +1
In accomplishing complex tasks, human cognition typically progresses from abstract to concrete (e.g., from brainstorming ideas to writing an essay). With the advent of highly capab…
Task-Dependent Evaluation of LLM Output Homogenization: A Taxonomy-Guided Framework
Shomik Jain, Jack Lanchantin, Maximilian Nickel +4
Large language models often generate homogeneous outputs, but whether this is problematic depends on the specific task. For objective math tasks, responses may vary in terms of pro…
Interaction Context Often Increases Sycophancy in LLMs
Shomik Jain, Charlotte Park, Matt Viana +2
We investigate how the presence and type of interaction context shapes sycophancy in LLMs. While real-world interactions allow models to mirror a user's values, preferences, and se…
Homogeneous Algorithms Can Reduce Competition in Personalized Pricing
Nathanael Jo, Kathleen Creel, Ashia Wilson +1
Firms' algorithm development practices are often homogeneous. Whether firms train algorithms on similar data, aim at similar benchmarks, or rely on similar pre-trained models, the…
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…