R2 · Decision paths & selective correction

Causal Fairness

R2 examines how protected attributes influence decisions through specific causal paths. It distinguishes paths that are permitted, blocked or require review, providing a basis for selective correction and a renewed assessment of the influence that remains.

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Two independent observation chambers comparing decision paths under controlled conditions — concept artwork
Decision paths & selective correctionOriginal concept artwork
Causal Fairness / KEY RESPONSIBILITIES
01

Analyze the paths

02

Correct selectively

03

Verify the remainder

THE PURPOSE

Locate the pathway behind an unfair influence, then evaluate whether a targeted correction addressed it.

HOW IT WORKS

A clear process.
Traceable decisions.

01

Analyze the paths

Use the causal model to examine how protected attributes can reach the decision, retaining the distinction between allowed, blocked and unresolved paths.

02

Correct selectively

Apply correction to the relevant influence paths while preserving the basis for assessing the intervention and its intended scope.

03

Verify the remainder

Reexamine residual influence, preserve the supporting evidence and use independent verification to assess the result of the correction.

APPLICATIONS

Where the architecture
can make a difference.

  • Decision-system fairness analysis and optional causal fairness indicators for Competition AOS.

A path that cannot be judged remains unresolved rather than being treated as fair.

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Discuss Causal Fairness.

Explore the architecture, relevant licensing scope and a concrete application.

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STUDIO FILM

Technology behind the challenge

A concept film connecting Competition AOS, Authorship OS and six AI Unlimited Challenge™ series.

1:00 · English / 한국어 subtitles · Sound on

Open technology film (MP4)