Assessing Injury Rates in Amazon Warehouses: A Responsible AI Perspective

In this article, we delve into the injury rates at Amazon warehouses, a critical aspect of workplace safety. With the increasing integration of AI and robotics in these facilities, it's essential to scrutinize their impact on human safety. By examining these data, we can gain insights into improving safety measures, promoting a harmonious coexistence of humans and machines.

Matched TAIM controls

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

MEASURE 2.6 — similarity 0.553, rank 1. TAIM detail and related incidents →
MEASURE 3.2 — similarity 0.552, rank 2. TAIM detail and related incidents →
MAP 3.2 — similarity 0.531, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/125

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.