The paper introduces the mutatis mutandis (MM) comparator as a causal alternative to the ceteris paribus (CP) comparator in discrimination testing, arguing that MM enables more realistic complainant-comparator pairs and creates new opportunities for machine learning methods.
Discrimination in the Age of Algorithms
1 Pith paper cite this work. Polarity classification is still indexing.
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Pith paper citing it
abstract
The law forbids discrimination. But the ambiguity of human decision-making often makes it extraordinarily hard for the legal system to know whether anyone has actually discriminated. To understand how algorithms affect discrimination, we must therefore also understand how they affect the problem of detecting discrimination. By one measure, algorithms are fundamentally opaque, not just cognitively but even mathematically. Yet for the task of proving discrimination, processes involving algorithms can provide crucial forms of transparency that are otherwise unavailable. These benefits do not happen automatically. But with appropriate requirements in place, the use of algorithms will make it possible to more easily examine and interrogate the entire decision process, thereby making it far easier to know whether discrimination has occurred. By forcing a new level of specificity, the use of algorithms also highlights, and makes transparent, central tradeoffs among competing values. Algorithms are not only a threat to be regulated; with the right safeguards in place, they have the potential to be a positive force for equity.
fields
cs.LG 1years
2024 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
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Mutatis Mutandis: Revisiting the Comparator in Discrimination Testing
The paper introduces the mutatis mutandis (MM) comparator as a causal alternative to the ceteris paribus (CP) comparator in discrimination testing, arguing that MM enables more realistic complainant-comparator pairs and creates new opportunities for machine learning methods.