Ad allocation should add constraints against hermeneutical deprivation and distortion to prevent epistemic harms from concentrating in disadvantaged groups.
Proceedings of the 23rd acm sigkdd international conference on knowledge discovery and data mining , pages=
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
LLMs show up to 60.58% social bias in generated code; a new Fairness Monitor Agent cuts bias by 65.1% and raises functional correctness from 75.80% to 83.97%.
Causality provides a unifying framework for resolving trade-offs in trustworthy AI by managing invariance conflicts under changes to the data-generating process.
citing papers explorer
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Beyond Distributive Justice: Hermeneutical Fairness in Ad Delivery
Ad allocation should add constraints against hermeneutical deprivation and distortion to prevent epistemic harms from concentrating in disadvantaged groups.
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Social Bias in LLM-Generated Code: Benchmark and Mitigation
LLMs show up to 60.58% social bias in generated code; a new Fairness Monitor Agent cuts bias by 65.1% and raises functional correctness from 75.80% to 83.97%.
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Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution
Causality provides a unifying framework for resolving trade-offs in trustworthy AI by managing invariance conflicts under changes to the data-generating process.