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This equation states the optimization target or decision rule used to choose the final output.
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Fairness under uncertainty in sequential decisions explores A taxonomy and framework for understanding and mitigating fairness risks in sequential decision-making systems under uncertainty. in Fairness in AI.
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/buildability/fairness-under-uncertainty-in-sequential-decisions
Subject: Fairness under uncertainty in sequential decisions
Verdict
Ignore
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This equation states the optimization target or decision rule used to choose the final output.
Page and bbox are available; crop image is pending.
This equation captures one of the core mathematical components of the system. Readers more familiar with Pearl’s do-calculus [43] may prefer the notation 𝑝𝑌| 𝒙,𝑑𝑜(𝐷= 1), which emphasizes that our ta
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Receipt path
/buildability/fairness-under-uncertainty-in-sequential-decisions
Paper ref
fairness-under-uncertainty-in-sequential-decisions
arXiv id
2604.21711
Generated at
2026-04-24T20:33:35.522Z
Evidence freshness
fresh
Last verification
2026-04-24T20:33:35.522Z
Sources
3
References
0
Coverage
50%
Lineage hash
bce4d6d0f90354c39f7824f1627ae5cb5be25f2f943ff72e48f0143b6fbe33af
Canonical opportunity-kernel lineage hash.
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unsigned_external
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not_verified
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Pending verification refs / 3 sources / Verification pending
repo_url
references
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This equation captures one of the core mathematical components of the system. disadvantaged minority. Assume a state space S = X × G × L × C, comprising applicant profiles X; gains G and
Page and bbox are available; crop image is pending.
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