MIT AI Risk Repository · Risk Sub-Category · 73.05.01
Effects on the Workforce
Category: Socioeconomic Impacts of LLM May Be Highly Disruptive
Description
"Rapid advances in LLMs pose three distinct sets of challenges for workers’ incomes (Korinek and Stiglitz, 2019; Susskind, 2023). First, they are likely to accelerate the rate of job turnover and disruption —– affecting more workers, including more highly skilled workers, and making the adjustment process for society more difficult than what we were used to from prior technological advances...Second, although technological progress means that society may produce more wealth overall, there is a risk that the general-purpose nature of LLMs may lead to progress that is biased against labor, meani
From Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Causal entity
- Other
- Intent
- Unintentional
- 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 Anwar2024
- Agentic LLMs Pose Novel Risks
- Natural Language Underspecifies Goals
- Goal-Directedness Incentivizes Undesirable Behaviors
- Safety Risks from Affordances Provided to LLM-agents
- Multi-Agent Safety Is Not Assured by Single-Agent Safety
- Foundationality May Cause Correlated Failures
- Groups of LLM-Agents May Show Emergent Functionality
- Collusion between LLM-Agents
- Dual-Use Capabilities Enable Malicious Use and Misuse of LLMs
- Misinformation and Manipulation
- Cybersecurity
- Cybersecurity