MIT AI Risk Repository · Risk Sub-Category · 11.05.04
Labor & material/Macro-socio economic harms
Category: Societal System Harms
Description
Algorithmic systems can increase “power imbalances in socio-economic relations” at the societal level [4, 137, p. 182], including through exacerbating digital divides and entrenching systemic inequalities [114, 230]. The development of algorithmic systems may tap into and foster forms of labor exploitation [77, 148], such as unethical data collection, worsening worker conditions [26], or lead to technological unemployment [52], such as deskilling or devaluing human labor [170]... when algorithmic financial systems fail at scale, these can lead to “flash crashes” and other adverse incidents wit
From Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (Shelby2023), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Causal entity
- Other
- Intent
- Other
- Timing
- Post-deployment
Subdomain definition: AI-driven concentration of power and resources within certain entities or groups, especially those with access to or ownership of powerful AI systems, leading to inequitable distribution of benefits and increased societal inequality.
Real-world incidents in this subdomain
- LLM Scrapers Allegedly Target Multiple Open Source Projects Disrupting the FOSS Ecosystem
- Coupang Allegedly Tweaked Search Algorithms to Boost Own Products
- Amazon Allegedly Tweaked Search Algorithm to Boost Its Own Products
- Gmail’s Inbox Sorting System Reportedly Reduced Visibility of Political Emails and Campaign Calls-to-Action
- Google Fined for Changing Shopping Algorithms in EU to Favor Own Service
- Amazon India Allegedly Rigged Search Results to Promote Own Products