Put Yourself in My Shoes: Revisiting the Moral Value of Algorithm Aversion Through Reciprocity and Vulnerability
Bibliographic record
Abstract
Abstract This paper begins by exploring the phenomenon known as algorithm aversion, where users distrust AI and prefer human advice or decision-making even when they are aware of the algorithm’s superior performance. Current literature generally frames it as a misguided bias that harms decision accuracy and speed, likening it to a form of neo-Luddism. This view, however, overlooks the fact that the two groups (supporters and sceptics of algorithmic decisions) are speaking different moral languages: the supporters are outcome-orientated, arguing for accuracy and performance, while the sceptics offer a Kantian-position, that asks us to challenge the very precept that AI systems can be a decision-maker, or obligation-bearer, for decisions constrained by rights. To consider their position on the human-AI moral relationship, I take advantage of Korsgaard’s work on interspecies moral relationships, concluding that to be an obligation-bearer toward human right-holders, there needs to be a reciprocal reasons-giving relationship which AI in principle cannot fulfil. To meet objections that argue AI could eventually replicate the necessary moral agency requirements, I show that reciprocal relationships also call for constitutive symmetry, highlighting the importance of not only matching rationality, but also the vulnerability inherent in the human condition. With this account, algorithm sceptics are not misguided and have something morally important to say. This does not suggest eliminating AI entirely from decision processes. AI-assisted decision-making can be defended as long as a robust human-centred approach where genuine human control is upheld.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".