PhilArchive
open access
Animal cognition and human values
Animal welfare scientists face an acute version of the problem of inductive risk, since they must choose whether or not to affirm attributions of mental states to animals in advisory contexts, knowing that their decisions hold significant consequences for animal welfare. In such contexts, the burden of proof should be sensitive to the moral consequences of error, but a framework for setting appropriate burdens of proof is lacking. Through reflection on two cases—the case of pain, and the case of cognitive enrichment—I arrive at a tentative general framework based on the principle of expected welfare maximization. I then discuss the limitations of this framework and the important questions it leaves open.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- How precaution should track uncertainty in animal sentience judgements#692935
- Animal Experimentation and the Argument from Limited Resources#733806
- Human outcome-irrelevant value learning is insensitive to its monetary cost#976809
- Human outcome-irrelevant value learning is insensitive to its monetary cost#976731
- Explicaciones de nivel personal en las ciencias del comportamiento animal#716314
Record · ID 701590
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive