What does this research mean for the field?

Algorithmic fairness is fundamentally a human-technology interaction problem rather than a purely technical challenge, as fairness depends on how algorithms are designed, used, and experienced by affected individuals. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.ESTABLISHES_NEW_DIRECTION.

What question did this study set out to answer?

To reframe algorithmic fairness as a human-technology interaction challenge rather than an isolated mathematical or computational problem.

September 8, 2026Current Opinion in PsychologyOpen Access

Algorithmic fairness as a human–technology interaction problem

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Authors

ALAurélie Lemmens

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Overview

Theoretical analysis reveals algorithmic fairness depends on human-technology dynamics rather than purely technical metrics, highlighting the need for interdisciplinary evaluation frameworks.

Key Points

To reframe algorithmic fairness as a human-technology interaction challenge rather than an isolated mathematical or computational problem.
Synthesized theoretical frameworks across computer science, psychology, social justice, judgment and decision-making, and management.
Evaluated how algorithmic mechanisms—such as objective functions, proxy variables, and feedback loops—interact with human decision-makers and affected populations.
Algorithmic systems can reproduce and amplify societal biases through feedback loops and proxy variables, but can also heighten transparency and mitigate discrimination.
Fairness outcomes depend fundamentally on human context, including how systems are designed, operated, and perceived, rather than purely on statistical criteria.

Cite This Study

Aurélie Lemmens (2026) studied this question.

synapsesocial.com/papers/6a9fd78a58e84d0ff5b4645c https://doi.org/10.1016/j.copsyc.2026.102411

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September 8, 2026Current Opinion in PsychologyOpen Access

Algorithmic fairness as a human–technology interaction problem

View Full Paper

Ask AI

Bookmark

Authors

ALAurélie Lemmens

Discussion

Member takes

Overview

Theoretical analysis reveals algorithmic fairness depends on human-technology dynamics rather than purely technical metrics, highlighting the need for interdisciplinary evaluation frameworks.

Key Points

To reframe algorithmic fairness as a human-technology interaction challenge rather than an isolated mathematical or computational problem.
Synthesized theoretical frameworks across computer science, psychology, social justice, judgment and decision-making, and management.
Evaluated how algorithmic mechanisms—such as objective functions, proxy variables, and feedback loops—interact with human decision-makers and affected populations.
Algorithmic systems can reproduce and amplify societal biases through feedback loops and proxy variables, but can also heighten transparency and mitigate discrimination.
Fairness outcomes depend fundamentally on human context, including how systems are designed, operated, and perceived, rather than purely on statistical criteria.

Cite This Study

Aurélie Lemmens (2026) studied this question.

synapsesocial.com/papers/6a9fd78a58e84d0ff5b4645c https://doi.org/10.1016/j.copsyc.2026.102411

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