Participatory design with 20 Afghan women reveals that safe GenAI learning companions must prioritize privacy, cultural fit, and genuine learning support, with the process itself linked to higher aspirations and agency.
The 2024 ACM Conference on Fairness Accountability and Transparency
6 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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The research identifies a 'modality gap' and a 'self-blame' phenomenon in blind users' interactions with agentic AI, advocating for non-visual, blame-aware explanation frameworks.
EvalAI providing pro/con arguments improves provision-level accuracy and reduces misclassification distance in DSA illegal content reporting under AI error conditions versus conventional XAI.
Trust calibration in agentic tool use is cast as preferential Bayesian optimization over a latent human risk-tolerance function observed through binary approve/deny feedback with a probit likelihood.
Large-scale review of 5300 AI incident reports shows harms are amplified up to three times at specific intersections including adolescent girls, lower-class people of color, and upper-class political elites.
A literature review concludes that pursuing consensus in data annotation creates biased AI by dismissing subjective disagreements and enforcing geographic hegemony, and proposes mapping diversity instead.
citing papers explorer
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Designing Safe and Accountable GenAI as a Learning Companion with Women Banned from Formal Education
Participatory design with 20 Afghan women reveals that safe GenAI learning companions must prioritize privacy, cultural fit, and genuine learning support, with the process itself linked to higher aspirations and agency.
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Explainable AI for Blind and Low-Vision Users: Navigating Trust, Modality, and Interpretability in the Agentic Era
The research identifies a 'modality gap' and a 'self-blame' phenomenon in blind users' interactions with agentic AI, advocating for non-visual, blame-aware explanation frameworks.
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AI at the Front Lines of Platform Governance: Using LLMs to Support Illegal Content Reporting under the Digital Services Act
EvalAI providing pro/con arguments improves provision-level accuracy and reduces misclassification distance in DSA illegal content reporting under AI error conditions versus conventional XAI.
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Progressive Autonomy as Preference Learning: A Formalization of Trust Calibration for Agentic Tool Use
Trust calibration in agentic tool use is cast as preferential Bayesian optimization over a latent human risk-tolerance function observed through binary approve/deny feedback with a probit likelihood.
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Why AI Harms Can't Be Fixed One Identity at a Time: What 5300 Incident Reports Reveal About Intersectionality
Large-scale review of 5300 AI incident reports shows harms are amplified up to three times at specific intersections including adolescent girls, lower-class people of color, and upper-class political elites.
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The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation
A literature review concludes that pursuing consensus in data annotation creates biased AI by dismissing subjective disagreements and enforcing geographic hegemony, and proposes mapping diversity instead.