Verifying Authenticity in Online Profiles: The Role of AI

A new wave of deepfake technology is challenging the authenticity of online profiles, particularly on LinkedIn. This development underscores the need for responsible AI practices to ensure trustworthy digital interactions. HISPI Project Cerebellum TAIM aims to prevent harm and establish guardrails for AI, providing a platform for collaboration in safe and secure AI development.

Matched TAIM controls

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

MEASURE 2.10 — similarity 0.477, rank 1. TAIM detail and related incidents →
MAP 1.6 — similarity 0.470, rank 2. TAIM detail and related incidents →
MAP 4.2 — similarity 0.465, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/174

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.