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20162025
most citedDiscovering and Validating AI Errors With Crowdsourced Failure Reports

61 citations · 122 across the 11 of their papers we have counts for

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cs.HC20221 cited

"Public(s)-in-the-Loop": Facilitating Deliberation of Algorithmic Decisions in Contentious Public Policy Domains

Hong Shen, Ángel Alexander Cabrera, Adam Perer +1

This position paper offers a framework to think about how to better involve human influence in algorithmic decision-making of contentious public policy issues. Drawing from insight…

cs.HC20221 cited

Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support

Anna Kawakami, Venkatesh Sivaraman, Hao-Fei Cheng +7

AI-based decision support tools (ADS) are increasingly used to augment human decision-making in high-stakes, social contexts. As public sector agencies begin to adopt ADS, it is cr…

cs.HC2022

Emblaze: Illuminating Machine Learning Representations through Interactive Comparison of Embedding Spaces

Venkatesh Sivaraman, Yiwei Wu, Adam Perer

Modern machine learning techniques commonly rely on complex, high-dimensional embedding representations to capture underlying structure in the data and improve performance. In orde…

cs.HC20211 cited

Characterizing Human Explanation Strategies to Inform the Design of Explainable AI for Building Damage Assessment

Donghoon Shin, Sachin Grover, Kenneth Holstein +1

Explainable AI (XAI) is a promising means of supporting human-AI collaborations for high-stakes visual detection tasks, such as damage detection tasks from satellite imageries, as…

cs.HC202161 cited

Discovering and Validating AI Errors With Crowdsourced Failure Reports

Ángel Alexander Cabrera, Abraham J. Druck, Jason I. Hong +1

AI systems can fail to learn important behaviors, leading to real-world issues like safety concerns and biases. Discovering these systematic failures often requires significant dev…

cs.HC2019

Ablate, Variate, and Contemplate: Visual Analytics for Discovering Neural Architectures

Dylan Cashman, Adam Perer, Remco Chang +1

Deep learning models require the configuration of many layers and parameters in order to get good results. However, there are currently few systematic guidelines for how to configu…