Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Kawakami, Anna"'
The responsible AI (RAI) community has introduced numerous processes and artifacts (e.g., Model Cards, Transparency Notes, Data Cards) to facilitate transparency and support the governance of AI systems. While originally designed to scaffold and docu
Externí odkaz:
http://arxiv.org/abs/2408.12047
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 in practice. We borrow from the anthropological practice to ``study up
Externí odkaz:
http://arxiv.org/abs/2405.12458
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 growing body of work documents how these AI systems often fail to impr
Externí odkaz:
http://arxiv.org/abs/2402.18774
Autor:
Kawakami, Anna, Guerdan, Luke, Cheng, Yanghuidi, Lee, Matthew, Carter, Scott, Arechiga, Nikos, Glazko, Kate, Zhu, Haiyi, Holstein, Kenneth
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 focused on decision-making tasks where it is possible to evaluate "appropria
Externí odkaz:
http://arxiv.org/abs/2308.15700
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 has called for increased scrutiny around the validity of AI system des
Externí odkaz:
http://arxiv.org/abs/2303.14602
Autor:
Chowdhary, Shreya, Kawakami, Anna, Gray, Mary L., Suh, Jina, Olteanu, Alexandra, Saha, Koustuv
Publikováno v:
2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT '23), June 12--15, 2023, Chicago, IL, USA
Sensing technologies deployed in the workplace can unobtrusively collect detailed data about individual activities and group interactions that are otherwise difficult to capture. A hopeful application of these technologies is that they can help busin
Externí odkaz:
http://arxiv.org/abs/2303.07242
Autor:
Kawakami, Anna, Chowdhary, Shreya, Iqbal, Shamsi T., Liao, Q. Vera, Olteanu, Alexandra, Suh, Jina, Saha, Koustuv
With the heightened digitization of the workplace, alongside the rise of remote and hybrid work prompted by the pandemic, there is growing corporate interest in using passive sensing technologies for workplace wellbeing. Existing research on these te
Externí odkaz:
http://arxiv.org/abs/2303.06794
Autor:
Kawakami, Anna, Guerdan, Luke, Cheng, Yang, Sun, Anita, Hu, Alison, Glazko, Kate, Arechiga, Nikos, Lee, Matthew, Carter, Scott, Zhu, Haiyi, Holstein, Kenneth
Publikováno v:
Human-Centered Explainable AI Workshop at ACM CHI Conference on Human Factors in Computing Systems 2022
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 learner-centered design and evaluation of XAI systems. We illustrate ou
Externí odkaz:
http://arxiv.org/abs/2212.05588
Recent research increasingly brings to question the appropriateness of using predictive tools in complex, real-world tasks. While a growing body of work has explored ways to improve value alignment in these tools, comparatively less work has centered
Externí odkaz:
http://arxiv.org/abs/2206.14983
Autor:
Stapleton, Logan, Cheng, Hao-Fei, Kawakami, Anna, Sivaraman, Venkatesh, Cheng, Yanghuidi, Qing, Diana, Perer, Adam, Holstein, Kenneth, Wu, Zhiwei Steven, Zhu, Haiyi
This is an extended analysis of our paper "How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions," which looks at racial disparities in the Allegheny Family Screening Tool, an algorithm used to help child welfare workers decide
Externí odkaz:
http://arxiv.org/abs/2204.13872