arXiv:2611.09924v1 Announce Type: cross
Abstract: Both humans and AI exhibit systemic biases, yet little is known about how interface design can foster fairness in hybrid human-AI decision-making. This paper introduces a method for extracting fair data samples from biased datasets and adapts a fairness-explanation technique to highlight potential AI bias during decisions. We conducted a within-subjects study (n=48) where non-experts made decisions across three conditions: independent human judgment, unexplained AI assistance, and AI assistance augmented with visual ethnic-bias explanations (Fair-XAI). To isolate effects, the AI conditions provided systematically balanced recommendations. Results show that fairness explanations significantly reduced both sex and ethnic bias compared to human-only decisions. Fair-XAI also marginally improved fairness over balanced AI recommendations while enhancing task performance. These findings demonstrate that fairness-centered explanation interfaces can meaningfully shape user behavior, providing concrete design insights for developing equitable and accountable human-AI systems.
Abstract: Both humans and AI exhibit systemic biases, yet little is known about how interface design can foster fairness in hybrid human-AI decision-making. This paper introduces a method for extracting fair data samples from biased datasets and adapts a fairness-explanation technique to highlight potential AI bias during decisions. We conducted a within-subjects study (n=48) where non-experts made decisions across three conditions: independent human judgment, unexplained AI assistance, and AI assistance augmented with visual ethnic-bias explanations (Fair-XAI). To isolate effects, the AI conditions provided systematically balanced recommendations. Results show that fairness explanations significantly reduced both sex and ethnic bias compared to human-only decisions. Fair-XAI also marginally improved fairness over balanced AI recommendations while enhancing task performance. These findings demonstrate that fairness-centered explanation interfaces can meaningfully shape user behavior, providing concrete design insights for developing equitable and accountable human-AI systems.
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