96 citations · 351 across the 18 of their papers we have counts for
18 papers
"I want to be pushed, I want to grow": Enabling social workers to design evaluations of LLM augmentation in their work
Anna Kawakami, Chloe Qianhui Zhao, Renee Shelby +3
Workers are increasingly asked to adopt AI systems to assist their work, yet are rarely given a voice in defining what meaningful AI augmentation should look like or how to evaluat…
What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Research
Wesley Hanwen Deng, Agathe Balayn, Andrew Selbst +6
Responsible AI (RAI) has become a central concern for technology companies, regulators, and the public. How industry practitioners interpret, implement, and sustain RAI work direct…
PolicyCraft: Supporting Collaborative and Participatory Policy Design through Case-Grounded Deliberation
Tzu-Sheng Kuo, Quan Ze Chen, Amy X. Zhang +3
Community and organizational policies are typically designed in a top-down, centralized fashion, with limited input from impacted stakeholders. This can result in policies that are…
Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration
Philipp Spitzer, Joshua Holstein, Katelyn Morrison +3
Across various applications, humans increasingly use black-box artificial intelligence (AI) systems without insight into these systems' reasoning. To counter this opacity, explaina…
Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Use
Anna Kawakami, Amanda Coston, Hoda Heidari +2
As public sector agencies rapidly introduce new AI tools in high-stakes domains like social services, it becomes critical to understand how decisions to adopt these tools are made…
Predictive Performance Comparison of Decision Policies Under Confounding
Luke Guerdan, Amanda Coston, Kenneth Holstein +1
Predictive models are often introduced to decision-making tasks under the rationale that they improve performance over an existing decision-making policy. However, it is challengin…
The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder Early-stage Deliberations Around Public Sector AI Proposals
Anna Kawakami, Amanda Coston, Haiyi Zhu +2
Public sector agencies are rapidly deploying AI systems to augment or automate critical decisions in real-world contexts like child welfare, criminal justice, and public health. A…
Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia
Tzu-Sheng Kuo, Aaron Halfaker, Zirui Cheng +5
AI tools are increasingly deployed in community contexts. However, datasets used to evaluate AI are typically created by developers and annotators outside a given community, which…
Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledge
Anna Kawakami, Luke Guerdan, Yanghuidi Cheng +6
A growing body of research has explored how to support humans in making better use of AI-based decision support, including via training and onboarding. Existing research has focuse…
Recentering Validity Considerations through Early-Stage Deliberations Around AI and Policy Design
Anna Kawakami, Amanda Coston, Haiyi Zhu +2
AI-based decision-making tools are rapidly spreading across a range of real-world, complex domains like healthcare, criminal justice, and child welfare. A growing body of research…
Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Services
Tzu-Sheng Kuo, Hong Shen, Jisoo Geum +4
Recent years have seen growing adoption of AI-based decision-support systems (ADS) in homeless services, yet we know little about stakeholder desires and concerns surrounding their…
Counterfactual Prediction Under Outcome Measurement Error
Luke Guerdan, Amanda Coston, Kenneth Holstein +1
Across domains such as medicine, employment, and criminal justice, predictive models often target labels that imperfectly reflect the outcomes of interest to experts and policymake…
Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making
Luke Guerdan, Amanda Coston, Zhiwei Steven Wu +1
A growing literature on human-AI decision-making investigates strategies for combining human judgment with statistical models to improve decision-making. Research in this area ofte…
Towards a Learner-Centered Explainable AI: Lessons from the learning sciences
Anna Kawakami, Luke Guerdan, Yang Cheng +8
In this short paper, we argue for a refocusing of XAI around human learning goals. Drawing upon approaches and theories from the learning sciences, we propose a framework for the l…
Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservables
Kenneth Holstein, Maria De-Arteaga, Lakshmi Tumati +1
In many real world contexts, successful human-AI collaboration requires humans to productively integrate complementary sources of information into AI-informed decisions. However, i…
Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders
Logan Stapleton, Min Hun Lee, Diana Qing +5
Child welfare agencies across the United States are turning to data-driven predictive technologies (commonly called predictive analytics) which use government administrative data t…
Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits
Wesley Hanwen Deng, Manish Nagireddy, Michelle Seng Ah Lee +4
Recent years have seen the development of many open-source ML fairness toolkits aimed at helping ML practitioners assess and address unfairness in their systems. However, there has…
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…