Foreseeing Challenges: Navigating Legal and Ethical Landmines in Responsible AI

Exploring the intricate web of legal and ethical dilemmas that arise from the deployment and use of artificial intelligence (AI). This article sheds light on potential risks, focusing on fostering safe and secure AI practices, and highlights the importance of AI governance, trustworthy AI, and harm prevention. JOIN US to contribute insights and help shape the future of AI.

Matched TAIM controls

Suggested mapping from embedding similarity (not a formal assessment). Browse all TAIM controls

MEASURE 2.6 — similarity 0.687, rank 1. TAIM detail and related incidents →
MAP 1.6 — similarity 0.681, rank 2. TAIM detail and related incidents →
MAP 4.1 — similarity 0.674, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/38

Data source

Incident data is from the AI Incident Database (AIID).

When citing the database as a whole, please use:

McGregor, S. (2021) Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database. In Proceedings of the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21). Virtual Conference.

Pre-print on arXiv · Database snapshots & citation guide

We use weekly snapshots of the AIID for stable reference. For the official suggested citation of a specific incident, use the “Cite this incident” link on each incident page.