How Can I Tell if My Algorithm Was Reasonable?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Self-learning algorithms are gradually dominating more and more aspects of our lives. They do so by performing tasks and reaching decisions that were once reserved exclusively for human beings. And not only that—in certain contexts, their decision-making performance is shown to be superior to that of humans. However, as superior as they may be, self-learning algorithms (also referred to as artificial intelligence (AI) systems, “smart robots,” or “autonomous machines”) can still cause damage. When determining the liability of a human tortfeasor causing damage, the applicable legal framework is generally that of negligence. To be found negligent, the tortfeasor must have acted in a manner not compliant with the standard of “the reasonable person.” Given the growing similarity of self-learning algorithms to humans in the nature of decisions they make and the type of damages they may cause (for example, a human driver and a driverless vehicle causing similar car accidents), several scholars have proposed the development of a “reasonable algorithm” standard, to be applied to self-learning systems. To date, however, academia has not attempted to address the practical question of how such a standard might be applied to algorithms, and what the content of analysis ought to be in order to achieve the goals behind tort law of promoting safety and victims’ compensation on the one hand, and achieving the right balance between these goals and encouraging the development of beneficial technologies on the other. This Article analyzes the “reasonableness” standard used in tort law in the context of the unique qualities, weaknesses, and strengths that algorithms possess comparatively to human actors and also examines whether the reasonableness standard is at all compatible with self-learning algorithms. Concluding that it generally is, the Article’s main contribution is its proposal of a concrete “reasonable algorithm” standard that could be practically applied by decisionmakers. This standard accounts for the differences between human and algorithmic decision-making. The “reasonable algorithm” standard also allows the application of the reasonableness standard to algorithms in a manner that promotes the aims of tort law while avoiding a dampening effect on the development and usage of new, beneficial technologies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it