Aporia makes design decisions explicit and interactive in AI-assisted programming, leading to higher engagement and 5x fewer mental model disagreements with code in a 14-person user study compared to a baseline agent.
2018.Statistical rethinking: A Bayesian course with examples in R and Stan
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
citing papers explorer
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Decision-Oriented Programming with Aporia
Aporia makes design decisions explicit and interactive in AI-assisted programming, leading to higher engagement and 5x fewer mental model disagreements with code in a 14-person user study compared to a baseline agent.
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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.