MIT AI Risk Repository · Risk Sub-Category · 54.01.01
Under-recognized work
Category: Negative impacts of AI use
Description
"Without training data, ML cannot take place. Much of this data comes from paid clickwork (also called “platform work” [170] or “microwork” [558]), unpaid crowdsourcing, and unpaid user behavior capture. Clickworkers, mainly in the global south, perform repetitive data-labeling tasks for use in the training of ML models [558]. The market value of such annotations “is projected to reach $13.7 billion by 2030” [228] and the annotation industry is widely reported to have little concern for workers’ rights. Besides welfare and rights, the invisibility of this contribution arguably contributes to a
From Ten Hard Problems in Artificial Intelligence We Must Get Right (Leech2024 ), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Causal entity
- Other
- Intent
- Other
- 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 Leech2024
- Negative impacts of AI use
- Environmental cost
- Discrimination, toxicity, and bias
- Privacy
- Security
- Harm caused by incompetent systems
- Harm caused by unaligned competent systems
- Specification gaming
- Emergent goals
- Deceptive alignment
- Within-country issues: domestic inequality
- Demographic diversity of researchers