MIT AI Risk Repository · Risk Sub-Category · 17.06.04
Disparate access to benefits due to hardware, software, skills constraints
Category: Automation, Access and Environmental Harms
Description
"Due to differential internet access, language, skill, or hardware requirements, the benefits from LMs are unlikely to be equally accessible to all people and groups who would like to use them. Inaccessibility of the technology may perpetuate global inequities by disproportionately benefiting some groups."
From Ethical and social risks of harm from language models (Weidinger2021), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Causal entity
- Human
- Intent
- Unintentional
- 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 Weidinger2021
- Discrimination, Exclusion and Toxicity
- Social stereotypes and unfair discrmination
- Social stereotypes and unfair discrmination
- Exclusionary norms
- Exclusionary norms
- Exclusionary norms
- Exclusionary norms
- Toxic language
- Lower performance for some languages and social groups
- Lower performance for some languages and social groups
- Information Hazards
- Compromising privacy by leaking private infiormation