Contribution to the handbook
Priority · Assessed 2026-10-27
The question raises a consequential executive decision and a normative choice: whether profiling is arbitrary or tied to relevant grounds. The evidence also identifies bias entering during system design and qualifies claims that automation would reduce bias.
Does this challenge the goal?
In-scope gap
The question explores whether automated profiling could reduce bias and what could make its grounds legitimate, within the definition’s scope for examining citizen interests, risks, and safeguards.
The evidence describes possible fairness benefits and ways bias may persist, but does not establish effectiveness or settle what counts as legitimate grounds.
Why this category?
- system specification
- consequential risks/limits
- safeguards
- tests
These are interpretations of attributed arguments, not empirical proof that automation reduces bias or that the example of legitimate grounds supplies a complete standard.
This is an AI assessment of contribution to the goal, separate from whether the question has an answer.
Authority, accountability and resilience →
Tests, stop conditions and unresolved work →
Read the exploration goalAssess whether automation could reduce bias in profiling, including ways bias might persist.
What we can say so far
The evidence supports a qualified possibility, not an established empirical effect: automation might reduce some implicit bias associated with human decisions. Bias can nevertheless persist because people encode tasks into system designs and code, and because human reviewers may introduce bias when they review or override automated decisions. The supplied material does not establish that automated profiling reduces bias in practice, how large any reduction would be, or whether it outweighs bias introduced elsewhere.
What remains open
- No supplied evidence directly compares bias in automated profiling with bias in human profiling.
- No supplied evidence measures whether any reduction in human decision bias offsets bias introduced through system design, coding, or human review.
- The supplied evidence does not establish how the reported model-performance differences relate to profiling outcomes.
Conditions and limits
- The Danaher materials are interpretations of the readable author manuscript from an accessible mirror, not the publisher typeset version, and are not independent new evidence.
- The proposed fairness benefit and the proposed enhancement of human reviewers are presented as possibilities, not demonstrated effects.
- The Hidalgo findings concern participants and models in that study; they do not establish results for other populations, models, or profiling tasks.
Objections to consider
- The proposed benefit is only a possibility; the supplied material does not establish an empirical bias-reduction effect.
- Bias may enter through system design and coding, and the passage does not establish how effectively careful design limits it.
- The reported accuracy differences are limited to the study described and do not establish how bias or performance varies in profiling.
- The evidence cautions against transferring bias results across tasks, and the supplied material does not establish which findings transfer to profiling.
Evidence and related ideas
Supporting evidence, qualifications and context play different roles in this answer.
qualifies
Fairness and legitimate grounds The author describes a possible fairness benefit: automated profiling might avoid some implicit biases in human decisions. The concern is not profiling itself, but targeting people arbitrarily rather than on legitimate grounds, such as being genuinely more likely to commit the crime in question. The author presents reduced bias as a possibility, not a guarantee.
Explain how profiling could remain tied to legitimate rather than arbitrary grounds, and what makes those grounds legitimate.
What we can say so far
The author distinguishes arbitrary discriminatory targeting from profiling based on a person being genuinely more likely to commit the specific crime at issue. That provides an illustrative substantive ground: a profile should be connected to a relevant likelihood of the conduct in question, rather than selected arbitrarily. The author presents reduced bias from automated profiling as a possibility, not a guarantee, and notes that bias can also enter during system design. The supplied material does not establish a complete test for what makes a ground legitimate or how to determine that a person is genuinely more likely to engage in the conduct. It also raises a procedural concern: legitimate decision-making should allow participation and understanding, without specifying how those requirements apply to profiling.
What remains open
- No complete normative criteria for distinguishing legitimate from arbitrary profiling grounds.
- No supplied procedure or evidence for establishing that a person is genuinely more likely to engage in the conduct being profiled.
- No account of how the procedural requirements of participation and understanding should be applied to profiling.
Conditions and limits
- The example concerns likelihood of the specific crime in question; it does not establish that predictive relevance alone is sufficient for legitimacy.
- Automated systems may reproduce bias through their design, so automation does not itself ensure legitimate grounds or reduced bias.
- The procedural criterion of participation and understanding is presented as an author's moral argument, not as a measured finding.
- The evidence is based on the readable author manuscript from an accessible mirror, rather than the publisher typeset version.
Objections to consider
- The evidence itself notes that bias can enter automated systems during design. It does not supply a separate safeguard or evidence resolving that limitation.
- The author's procedural criterion indicates that predictive relevance may not settle legitimacy, but the supplied material does not explain how participation and understanding apply to profiling or how to reconcile them with the substantive ground.
Evidence and related ideas
Supporting evidence, qualifications and context play different roles in this answer.
qualifies
Fairness and legitimate grounds The author describes a possible fairness benefit: automated profiling might avoid some implicit biases in human decisions. The concern is not profiling itself, but targeting people arbitrarily rather than on legitimate grounds, such as being genuinely more likely to commit the crime in question. The author presents reduced bias as a possibility, not a guarantee.