TRV-2026-0056Version 6 · Revised
Reason for this version
Reading revised
Canonical text (the exact bytes fingerprinted)
TRUVACE RECORD VERSION record: TRV-2026-0056 version: 6 kind: revised reason: Reading revised timestamp: 2026-07-12T20:58:06.484954Z status: published lens: g_space sector: science headline: The Ethics of AI Ethics: An Evaluation of Guidelines dek: Abstract Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compares 22 guidelines, highlighting overlaps but also omissions. As a res… gain_reading: Finally, I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems, and how the effectiveness in the demands of AI ethics can be improved. problem_reading: (none) limitation: Historical research candidate. An editor must verify study design, population, effect size, and whether later evidence changes the reading before publication. tag: Evidence-backed gain key_points: Abstract Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. | In consequence, a number of ethics guidelines have been released in recent years. | These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. rundown: Abstract Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compares 22 guidelines, highlighting overlaps but also omissions. sources: - peer_reviewed | Information Fusion | https://doi.org/10.1016/j.inffus.2023.101805 | 2023-04-18 - peer_reviewed | International Journal of Information Management | https://doi.org/10.1016/j.ijinfomgt.2023.102642 | 2023-03-11 - peer_reviewed | JMIR Mental Health | https://doi.org/10.2196/60432 | 2025-02-21 - peer_reviewed | Minds and Machines | https://doi.org/10.1007/s11023-020-09517-8 | 2020-02-01 - peer_reviewed | Psychology & Marketing | https://doi.org/10.1002/mar.21767 | 2022-12-16 prev: d2b32683b25089d75d66a75091860dfd899d1312f80c71fead4d4cc9714b6db7
- sha256
- 05663c1fb47be75ba4c98bc2c83c86ad991a583d275a2e1981407eacad01b7fb
- previous
- d2b32683b25089d75d66a75091860dfd899d1312f80c71fead4d4cc9714b6db7
Verify this record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-0056 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.