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AI systems are increasingly automating many human tasks, potentially leading to significant job losses. If AI is able to provide large-scale labor that is less expensive and more effective than human labor, it could take over major industries (e.g., manufacturing, crowdwork platforms, software engineering), causing mass unemployment. This displacement of labor could worsen existing social and economic inequalities, as those most vulnerable to automation are likely to currently occupy positions of disadvantage. New disparities may also arise between those who are able to adapt their skills to complement AI systems and those who are not.
Aside from the availability of jobs, AI automation may negatively impact job quality and security. The roles that remain after widespread automation could be more monotonous and less engaging as AI takes on more complex tasks. Furthermore, the threat of replacement by AI could result in exploitative dependencies between human workers and their employers. In order to remain competitive with faster, more knowledgeable AI assistants, human workers may be pressured to accept lower wages, fewer benefits, and poorer working conditions. Generative AI companies have a history of exploiting dispensable workers (e.g., refugees, prisoners, low-income individuals) for crowdwork that is fraught and unfair.
Excerpt from the MIT AI Risk Repository full report
Social and economic inequalities caused by widespread use of AI, such as by automating jobs, reducing the quality of employment, or producing exploitative dependencies between workers and their employers.
Incident volume relative to governance coverage; each dot is one of 24 subdomains
Entity
Who or what caused the harm
Intent
Whether the harm was intentional or accidental
Timing
Whether the risk is pre- or post-deployment
Montefiore Hospital in the Bronx reportedly issued layoff notices to 12 utilization-review nurses, citing a transition to AI-powered software for insurance denial processing. The nurses' union, NYSNA, alleges this violates contractual AI safeguards and threatens patient care by replacing human clinical judgment with untested technology. Montefiore disputes these claims, characterizing the technology as part of a nonclinical paperwork program.
Developers: Healthcare administrative AI developers, AI-powered utilization-review software developers
Deployers: Montefiore Medical Center, Montefiore Health System, Hospitals, Healthcare employers
Uber introduced an opaque 'Upfront Fares' algorithm in 24 U.S. cities to determine driver pay, replacing traditional time-and-distance calculations. Drivers have reported significant confusion, decreased earnings on long trips, and a lack of transparency regarding how the algorithm determines pay. Critics and researchers have raised concerns about the use of 'black box' algorithms in gig work, noting that the lack of clear criteria makes it difficult for drivers to verify fair compensation.
Developers: Uber
Deployers: Uber
OpenAI contracted with Sama, an outsourcing firm in Kenya, to have workers label thousands of graphic text snippets—including child sexual abuse, torture, and murder—to train safety filters for ChatGPT and GPT-3. Workers reported receiving low pay (as little as $1.32/hour) and suffering from severe psychological trauma and PTSD due to the nature of the content. The contract was terminated early by Sama following public scrutiny and internal concerns regarding the working conditions and the nature of the tasks.
Developers: OpenAI
Deployers: OpenAI
of the 272 AI experts surveyed named increased inequality and decline in employment quality one of the three AI risks they are most concerned about over the next five years (2025–2030).
average probability experts placed on harm from increased inequality and decline in employment quality reaching catastrophic levels (>1M deaths, >$100B loss, or civilization-scale damage) within five years, falling to 10.9% with pragmatic mitigations.
With pragmatic mitigations
Average severity stays severe.
24 classified mentions from 12 companies.
Browse all 24 mentions →AI systems that memorize and leak sensitive personal data or infer private information about individuals without their consent. Unexpected or unauthorized sharing of data and information can compromise user expectation of privacy, assist identity theft, or cause loss of confidential intellectual property.
95 shared governance docs
Vulnerabilities that can be exploited in AI systems, software development toolchains, and hardware, resulting in unauthorized access, data and privacy breaches, or system manipulation causing unsafe outputs or behavior.
84 shared governance docs
AI systems that fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences, especially in critical applications or areas that require moral reasoning.
84 shared governance docs
Challenges in understanding or explaining the decision-making processes of AI systems, which can lead to mistrust, difficulty in enforcing compliance standards or holding relevant actors accountable for harms, and the inability to identify and correct errors.
83 shared governance docs
Amends laws to require New York government agencies to disclose, assess, and submit reports on their use of AI decision-making tools. Ensures human oversight, prohibits biased outcomes, and protects employee rights regarding AI's impact on employment and duties.
Establishes the North Carolina AI Leadership Council to advise on AI strategy and policy. Creates the AI Accelerator for AI governance, research, and training. Requires Cabinet agencies to propose AI use cases. Develops statewide AI literacy and fraud prevention training program.
Requires the Superintendent to convene a working group to develop AI usage guidelines for educational settings. Instructs the group to assess AI use in schools, develop model policies, and report findings by 2027. Specifies transparency, privacy, and academic integrity provisions.
