Nurturing Capabilities: Unpacking the Gap in Human-Centered Evaluations of AI-Based Systems

要旨

Human-Computer Interaction (HCI) scholarship has studied how Artificial Intelligence (AI) can be leveraged to support care work(ers) by recognizing, reducing, and redistributing workload. Assessment of AI's impact on workers requires scrutiny and is a growing area of inquiry within human-centered evaluations of AI. We add to these conversations by unpacking the sociotechnical gap between the broader aspirations of workers from an AI-based system and the narrower existing definitions of success. We conducted a mixed-methods study and drew on Amartya Sen's Capability Approach to analyze the gap. We shed light on the social factors---on top of performance on evaluation metrics---that guided the AI model choice and determined whose wellbeing must be evaluated while conducting such evaluations. We argue for assessing broader achievements enabled through AI's use when conducting human-centered evaluations of AI. We discuss and recommend the dimensions to consider while conducting such evaluations.

著者
Aman Khullar
Georgia Institute of Technology, Atlanta, Georgia, United States
Nikhil Nalin
Noora Health, Bangalore, India
Abhishek Prasad
Noora Health, Bangalore, India
Ann John Mampilli
Noora Health, Bangalore, India
Neha Kumar
Georgia Tech, Atlanta, Georgia, United States
DOI

10.1145/3706598.3713278

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713278

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Better Work and Career

G414+G415
7 件の発表
2025-04-29 20:10:00
2025-04-29 21:40:00
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