activity
20172026
most citedHarms from Increasingly Agentic Algorithmic Systems

124 citations · 344 across the 42 of their papers we have counts for

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Showing 2025Show all

7 papers · 1 filter

cs.HC2025★ 6 cited

Beyond Quantification: Navigating Uncertainty in Professional AI Systems

Sylvie Delacroix, Diana Robinson, Umang Bhatt +12

The growing integration of large language models across professional domains transforms how experts make critical decisions in healthcare, education, and law. While significant res…

cs.CY2025★ 2 cited

Measuring and mitigating overreliance to build human-compatible AI

Lujain Ibrahim, Katherine M. Collins, Sunnie S. Y. Kim +14

Large language models (LLMs) distinguish themselves from previous technologies by functioning as collaborative ``thought partners,'' capable of engaging more fluidly in natural lan…

cs.CY2025

Documenting Deployment with Fabric: A Repository of Real-World AI Governance

Mackenzie Jorgensen, Kendall Brogle, Katherine M. Collins +10

Artificial intelligence (AI) is increasingly integrated into society, from financial services and traffic management to creative writing. Academic literature on the deployment of A…

cs.CY2025★ 1 cited

Unequal Uncertainty: Rethinking Algorithmic Interventions for Mitigating Discrimination from AI

Holli Sargeant, Mackenzie Jorgensen, Arina Shah +3

Uncertainty in artificial intelligence (AI) predictions raises pressing legal and ethical questions for AI-assisted decision-making. This article examines two uncertainty-based alg…

cs.MA2025

When Should We Orchestrate Multiple Agents?

Umang Bhatt, Sanyam Kapoor, Mihir Upadhyay +6

Strategies for orchestrating the interactions between multiple agents, both human and artificial, can wildly overestimate performance and underestimate the cost of orchestration. W…

cs.LG2025

Faster, Cheaper, Better: Multi-Objective Hyperparameter Optimization for LLM and RAG Systems

Matthew Barker, Andrew Bell, Evan Thomas +3

While Retrieval Augmented Generation (RAG) has emerged as a popular technique for improving Large Language Model (LLM) systems, it introduces a large number of choices, parameters…

cs.AI2025

Revisiting Rogers' Paradox in the Context of Human-AI Interaction

Katherine M. Collins, Umang Bhatt, Ilia Sucholutsky

Humans learn about the world, and how to act in the world, in many ways: from individually conducting experiments to observing and reproducing others' behavior. Different learning…