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.
In: Proceedings of the 20 23 ACM Con- ference on Fairness, Accountability, and Transparency, pp
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 3
citation-polarity summary
roles
background 2representative citing papers
Racial mismatch between applicant and AI avatar increased perceived ethnic bias, while sharing only one identity trait lowered fairness ratings compared to full or no match.
The paper proposes six interconnected elements of a design space to close the synergy gap in human-AI decision-making.
Prioritization algorithms in public services generate relative disparities among intersectional groups as resources become scarce, intensifying perceptions of inequality.
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.
PAFER estimates statistical parity for differentially private decision trees using Laplacian noise, achieving low error while preserving privacy and favoring interpretable trees.
A scoping review of AIES and FAccT literature concludes that AI trustworthiness research prioritizes technical precision over social, ethical, and institutional factors, leaving the sociotechnical nature of AI systems underexplored.
citing papers explorer
-
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.
-
Skin-Deep Bias: How Avatar Appearances Shape Perceptions of AI Hiring
Racial mismatch between applicant and AI avatar increased perceived ethnic bias, while sharing only one identity trait lowered fairness ratings compared to full or no match.
-
Addressing the Synergy Gap: The Six Elements of the Design Space
The paper proposes six interconnected elements of a design space to close the synergy gap in human-AI decision-making.
-
The Paradox of Prioritization in Public Sector Algorithms
Prioritization algorithms in public services generate relative disparities among intersectional groups as resources become scarce, intensifying perceptions of inequality.
-
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.
-
Privacy Constrained Fairness Estimation for Decision Trees
PAFER estimates statistical parity for differentially private decision trees using Laplacian noise, achieving low error while preserving privacy and favoring interpretable trees.
-
Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles
A scoping review of AIES and FAccT literature concludes that AI trustworthiness research prioritizes technical precision over social, ethical, and institutional factors, leaving the sociotechnical nature of AI systems underexplored.