MIT AI Risk Repository · Risk Sub-Category · 18.06.05
Exploitative data sourcing and enrichment
Category: Socioeconomic and environmental harms
Description
"Perpetuating exploitative labour practices to build AI systems (sourcing, user testing)"
From Sociotechnical Safety Evaluation of Generative AI Systems (Weidinger2023), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Causal entity
- Human
- Intent
- Intentional
- Timing
- Pre-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 Weidinger2023
- Representation & Toxicity Harms
- Unfair representation
- Unfair capability distribution
- Toxic content
- Misinformation Harms
- Propagating misconceptions/ false beliefs
- Erosion of trust in public information
- Pollution of information ecosystem
- Information & Safety Harms
- Privacy infringement
- Dissemination of dangerous information
- Malicious Use