+ HEALTH Rationale and objectives To propose a new imaging biomarker, the pulmonary artery-to-vein volume difference (…± HEALTH Rationale and objectives The international application of artificial intelligence (AI) for lesion detection b…± HEALTH Rationale and objectives To address the challenge of preoperative prediction of synchronous liver metastasis…± HEALTH Objectives To conduct a nationwide survey among professionals working in oncology departments in China to inv…+ HEALTH The utilization of deep convolutional neural networks for the purpose of diagnosing diseases in the skin area…± HEALTH BACKGROUND . Gadolinium-based contrast agents remain essential for MRI but carry risks. Deep learning (DL) me…+ SCIENCE Gas Chromatography is a versatile separation technique widely used in analytical chemistry for constituent de…+ HEALTH Background Intracardiac echocardiography (ICE) facilitates left atrial (LA) reconstruction during atrial fibr…
TruaceTracing the truth around AIThursday, August 27, 2026
Subscribe
TRV-2026-0056Version 1 · Certified

Written 2026-07-12 20:50:18 UTC · current record

Reason for this version

Certified into the record

Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-0056
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-12T20:50:18.580047Z
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 result, I give a detailed overview of the field of AI ethics. 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.
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 | Minds and Machines | https://doi.org/10.1007/s11023-020-09517-8 | 2020-02-01
prev: 0000000000000000000000000000000000000000000000000000000000000000
sha256
c301d28a3a8e321a22c786488aec1aef74381241a3fbf0aff91b07a3d3a1b574
previous
0000000000000000000000000000000000000000000000000000000000000000
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.