Search Results for author: Saleema Amershi

Found 13 papers, 3 papers with code

Interactive Debugging and Steering of Multi-Agent AI Systems

1 code implementation3 Mar 2025 Will Epperson, Gagan Bansal, Victor Dibia, Adam Fourney, Jack Gerrits, Erkang Zhu, Saleema Amershi

Fully autonomous teams of LLM-powered AI agents are emerging that collaborate to perform complex tasks for users.

AI Agent

Challenges in Human-Agent Communication

no code implementations28 Nov 2024 Gagan Bansal, Jennifer Wortman Vaughan, Saleema Amershi, Eric Horvitz, Adam Fourney, Hussein Mozannar, Victor Dibia, Daniel S. Weld

Remarkable advancements in modern generative foundation models have enabled the development of sophisticated and highly capable autonomous agents that can observe their environment, invoke tools, and communicate with other agents to solve problems.

AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems

1 code implementation9 Aug 2024 Victor Dibia, Jingya Chen, Gagan Bansal, Suff Syed, Adam Fourney, Erkang Zhu, Chi Wang, Saleema Amershi

Multi-agent systems, where multiple agents (generative AI models + tools) collaborate, are emerging as an effective pattern for solving long-running, complex tasks in numerous domains.

Supporting Human-AI Collaboration in Auditing LLMs with LLMs

no code implementations19 Apr 2023 Charvi Rastogi, Marco Tulio Ribeiro, Nicholas King, Harsha Nori, Saleema Amershi

Through the design process we highlight the importance of sensemaking and human-AI communication to leverage complementary strengths of humans and generative models in collaborative auditing.

Language Modelling Large Language Model +1

How Different Groups Prioritize Ethical Values for Responsible AI

no code implementations16 May 2022 Maurice Jakesch, Zana Buçinca, Saleema Amershi, Alexandra Olteanu

Compared to the US-representative sample, AI practitioners appear to consider responsible AI values as less important and emphasize a different set of values.

Fairness

REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research

1 code implementation5 May 2022 Jessie J. Smith, Saleema Amershi, Solon Barocas, Hanna Wallach, Jennifer Wortman Vaughan

Transparency around limitations can improve the scientific rigor of research, help ensure appropriate interpretation of research findings, and make research claims more credible.

BIG-bench Machine Learning

Machine Teaching: A New Paradigm for Building Machine Learning Systems

no code implementations21 Jul 2017 Patrice Y. Simard, Saleema Amershi, David M. Chickering, Alicia Edelman Pelton, Soroush Ghorashi, Christopher Meek, Gonzalo Ramos, Jina Suh, Johan Verwey, Mo Wang, John Wernsing

This significantly limits the number of machine learning systems that can be created and has led to a mismatch between the demand for machine learning systems and the ability for organizations to build them.

BIG-bench Machine Learning

Active Learning with Oracle Epiphany

no code implementations NeurIPS 2016 Tzu-Kuo Huang, Lihong Li, Ara Vartanian, Saleema Amershi, Jerry Zhu

We present a theoretical analysis of active learning with more realistic interactions with human oracles.

Active Learning

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