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Jeffrey P. Bigham

8 accepted papers

2026

VLSU: Mapping the Limits of Joint Multimodal Understanding for AI Safety

Shruti Palaskar, Leon Alexander Gatys, Mona Abdelrahman, Mar Jacobo, Laurence F Lindsey, Rutika Moharir +6
ICLR 2026poster

Safety evaluation of multimodal foundation models often treats vision and language inputs separately, missing risks from joint interpretation where benign content becomes harmful in combination. Existing approaches also fail to distinguish clearly unsafe content from borderline cases, leading to pro…

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2025

CowPilot: A Framework for Autonomous and Human-Agent Collaborative Web Navigation

Faria Huq, Zora Zhiruo Wang, Frank F. Xu, Tianyue Ou, Shuyan Zhou, Jeffrey P. Bigham +1
NAACL 2025system demonstrations

While much work on web agents emphasizes the promise of autonomously performing tasks on behalf of users, in reality, agents often fallshort on complex tasks in real-world contexts and modeling user preference. This presents an opportunity for humans to collaborate with the agent and leverage the ag…

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2025

Position: Towards Bidirectional Human-AI Alignment

Hua Shen, Tiffany Knearem, Reshmi Ghosh, Kenan Alkiek, Kundan Krishna, Yachuan Liu +15
NeurIPS 2025poster

Recent advances in general-purpose AI underscore the urgent need to align AI systems with human goals and values. Yet, the lack of a clear, shared understanding of what constitutes "alignment" limits meaningful progress and cross-disciplinary collaboration. In this position paper, we argue that the…

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2023

USB: A Unified Summarization Benchmark Across Tasks and Domains

Kundan Krishna, Prakhar Gupta, Sanjana Ramprasad, Byron C Wallace, Jeffrey P. Bigham, Zachary Chase Lipton
EMNLP 2023long findings

While the NLP community has produced numerous summarization benchmarks, none provide the rich annotations required to simultaneously address many important problems related to control and reliability. We introduce a Wikipedia-derived benchmark, complemented by a rich set of crowd-sourced annotatio…

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2022

Nonverbal Sound Detection for Disordered Speech

Colin Lea, Zifang Huang, Dhruv Jain, Lauren Tooley, Zeinab Liaghat, Shrinath Thelapurath +2
ICASSP 2022accepted

Voice assistants have become an essential tool for people with various disabilities because they enable complex phone-or tablet-based interactions without the need for fine-grained motor control, such as with touchscreens. However, these systems are not tuned for the unique characteristics of indivi…

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2021

SEP-28k: A Dataset for Stuttering Event Detection from Podcasts with People Who Stutter

Colin Lea, Vikramjit Mitra, Aparna Joshi, Sachin Kajarekar, Jeffrey P. Bigham
ICASSP 2021accepted

The ability to automatically detect stuttering events in speech could help speech pathologists track an individual’s fluency over time or help improve speech recognition systems for people with atypical speech patterns. Despite increasing interest in this area, existing public datasets are too small…

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2019

VizWiz-Priv: A Dataset for Recognizing the Presence and Purpose of Private Visual Information in Images Taken by Blind People

Danna Gurari, Qing Li, Chi Lin, Yinan Zhao, Anhong Guo, Abigale Stangl +1
CVPR 2019poster

We introduce the first visual privacy dataset originating from people who are blind in order to better understand their privacy disclosures and to encourage the development of algorithms that can assist in preventing their unintended disclosures. It includes 8,862 regions showing private content ac…

Cited by 132PDF ↗Scholar ↗
2018

VizWiz Grand Challenge: Answering Visual Questions From Blind People

Danna Gurari, Qing Li, Abigale J. Stangl, Anhong Guo, Chi Lin, Kristen Grauman +2
CVPR 2018poster

The study of algorithms to automatically answer visual questions currently is motivated by visual question answering (VQA) datasets constructed in artificial VQA settings. We propose VizWiz, the first goal-oriented VQA dataset arising from a natural VQA setting. VizWiz consists of 31,000 visual qu…

Cited by 961Source ↗PDF ↗Scholar ↗