MIT AI Risk Repository · Risk Sub-Category · 24.09.01
Equality and inequality
Category: Cooperation
Description
"AI assistant technology, like any service that confers a benefit to a user for a price, has the potential to disproportionately benefit economically richer individuals who can afford to purchase access (see Chapter 15). On a broader scale, the capabilities of local infrastructure may well bottleneck the performance of AI assistants, for example if network connectivity is poor or if there is no nearby data centre for compute. Thus, we face the prospect of heterogeneous access to technology, and this has been known to drive inequality (Mirza et al., 2019; UN, 2018; Vassilakopoulou and Hustad, 2
From The Ethics of Advanced AI Assistants (Gabriel2024), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Causal entity
- Human
- 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
How other frameworks describe this risk
Other entries from Gabriel2024
- Capability failures
- Lack of capability for task
- Difficult to develop metrics for evaluating benefits or harms caused by AI assistants
- Safe exploration problem with widely deployed AI assistants
- Goal-related failures
- Misaligned consequentialist reasoning
- Specification gaming
- Goal misgeneralisation
- Deceptive alignment
- Malicious Uses
- Offensive Cyber Operations (General)
- AI-Powered Spear-Phishing at Scale