MIT AI Risk Repository · Risk Sub-Category · 16.06.02
Increasing inequality and negative effects on job quality
Category: Risk area 6: Environmental and Socioeconomic harms
Description
"Advances in LMs and the language technologies based on them could lead to the automation of tasks that are currently done by paid human workers, such as responding to customer-service queries, with negative effects on employment [3, 192]."
From Taxonomy of Risks posed by Language Models (Weidinger2022), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Causal entity
- Human
- Intent
- Other
- Timing
- Post-deployment
Subdomain definition: Widespread use of AI increasing social and economic inequalities, such as by automating jobs, reducing the quality of employment, or producing exploitative dependencies between workers and their employers.
Real-world incidents in this subdomain
- Polish Radio Station Replaces Human Hosts with AI-Generated Presenters to Simulate Interviewing Deceased Poet Wisława Szymborska
- Fast Food Chains' AI Chatbots Failed to Assist Job Applicants with Scheduling Interviews
- Kenyan Data Annotators Allegedly Exposed to Graphic Content for OpenAI's AI
- RealPage Algorithm Allegedly Inflates Rents and Reduces Competition in Housing Market
- Amazon Flex Drivers Allegedly Fired via Automated Employee Evaluations
- Kronos Scheduling Algorithm Allegedly Caused Financial Issues for Starbucks Employees
How other frameworks describe this risk
Other entries from Weidinger2022
- Risk area 1: Discrimination, Hate speech and Exclusion
- Social stereotypes and unfair discrimination
- Social stereotypes and unfair discrimination.
- Hate speech and offensive language
- Exclusionary norms
- Exclusionary norms
- Exclusionary norms
- Exclusionary norms
- Exclusionary norms
- Lower performance for some languages and social groups
- Lower performance for some languages and social groups
- Lower performance for some languages and social groups