AI · Algorithm-in-the-loop empirical analysis.

Disparate interactions: An algorithm-in-the-loop analysis of fairness

FAT*. · 2019

Green, B., Chen, Y.

Original paper ↗

Summary

Human interaction can reproduce or amplify disparities; fairness is socio-technical.

For your marketing

Possible founder application

Audit user behavior and downstream outcomes, not model parity only.

Reported result

What the study found

Human interaction can reproduce or amplify disparities; fairness is socio-technical.

Research question

What the authors investigated

How do human–algorithm interactions affect disparate outcomes?

Design

Algorithm-in-the-loop empirical analysis.

Sample and setting

People interacting with risk-assessment algorithms.

Scope and transfer

Limitations to keep in view

Specific risk-assessment context.

Research sources

Publisher abstract/full text supports the reported design, sample, and findings.

Additional research source ↗

Study strength

4/5 Research record provides a clear design and context; apply the finding within its stated limits.

How this was assessed

The 1–5 evidence-check score is a practical reading aid, not a formal peer review or a probability that a conclusion is true. It combines design strength (2 points), sample reporting (1), comparison/control clarity (1), and how much of the paper could be checked (1). Access affects how confidently this directory can assess a study, not the study's underlying quality. Blinding is reported separately because it is not relevant to every design.

Design transparency · 2/2

Based on the reported study design in the retrieved source.

Sample and setting · 1/1

The sample context is stated; a numerical count is preserved only when reported.

Outcome clarity · 1/1

The finding is summarized from publisher or article metadata.

Access for checking · 1/1

Full text was identified.

Double-blind? The retrieved publisher record does not establish whether participants or outcome assessors were blinded.

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