What does this research mean for the field?

Algorithmic credit scoring systems create a reflexive opacity arms race that increases harm to consumers. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.CHALLENGES_CONSENSUS.

What question did this study set out to answer?

Explore the effects of opacity in algorithmic credit scoring on consumers and the resulting arms race.

February 25, 2026Open Access

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The Score Punished Me: Reflexive Opacity Arms Race in Algorithmic Credit Assessment

Key Points

Explore the effects of opacity in algorithmic credit scoring on consumers and the resulting arms race.
Applied the Void Framework to analyze credit scoring systems.
Conducted a theoretical examination of consumer visibility over algorithms.
Analyzed regulatory implications under the EU AI Act.
Identified a cycle where consumers can't see algorithms, prompting adaptive responses from systems.
Highlighted increased harm due to lack of transparency in credit scoring.
Revealed implications for high-risk AI classification under EU regulations.

Abstract

Applies the Void Framework to algorithmic credit scoring. Demonstrates that credit scoring systems create a reflexive opacity arms race — consumers cannot see the algorithm, the algorithm responds to behavior, and the system captures attention in ways that increase harm. EU AI Act Annex III analysis. Relevant to high-risk AI classification under the EU AI Act.

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The Score Punished Me: Reflexive Opacity Arms Race in Algorithmic Credit Assessment

Key Points

Abstract

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