Responsibility for AI outcomes rests with design decisions
Explains how human aims shape AI and why tracing responsibility matters
- Publication
- PNAS
- Stage
- Journal article
- What we read
- Summary of the abstract
- Authors
- Martin Hilbert
- Universities and research institutions
- University of California System
Institution metadata: OpenAlex record ↗
What they did and found
The source says every machine learning system starts with a human goal. Data, how it is used, user interaction, and postlaunch checks add layers of human intent, so results reflect many aims, not one single voice, for any situation today.
Why it matters
Knowing who is in charge helps set rules and fixes; it avoids guessing and points to where rules should apply in AI system work today.
The main idea is that human goals guide what the system does. By checking goals, data choices, and after-launch oversight, teams can see who is responsible.
In practice, organizations should map who decided what, how data was chosen, and who watches the system after launch. This keeps safety tied to accountability.
What remains uncertain
Abstract-only scope; no new experiments or results are reported; preprint status not specified
Original sources · 1
- Where intention goes, responsibility flows: Grounding AI accountability in the machine learning paradigm ↗PNAS · 2026-10-30
Check the original paper for its authors, methods, version and access terms.
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