The Right to Explanation in AI: In a Lonely Place
Bibliographic record
Abstract
Technology is increasingly being used in decision-making in all fields, particularly in health care. Automated decision-making promises to change medical practice and potentially improve and streamline the provision of health care. Although the integration of artificial intelligence (AI) into medicine is encouraging, it is also accompanied by fears concerning transparency and accountability. This is where the right to explanation has come in. Legislators and policymakers have relied on the right to explanation, a new right guaranteed to those who are affected by automated decision-making, to ease fears surrounding AI. This is particularly apparent in the province of Quebec in Canada, where legislators recently passed Law 5, an act respecting health and social services information and amending various legislative provisions. This paper explores the practical implications of Law 5, and by extension of the right to explanation internationally, in the health care field. We highlight that the right to explanation is anticipated to alter physicians' obligation to patients, namely the duty to inform. We also discuss how the drafting of the legislation on the right to explanation is vague and hard to enforce. This dilutes the potential of the right to explanation to provide meaningful protections for those affected by automated decisions. After all, AI is a complex and innovative technology and, as such, requires complex and innovative policies. The right to explanation is not necessarily the answer.
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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 | 0.037 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.105 |
| Scholarly communication | 0.020 | 0.029 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".