This is a research prototype. The data and analyses are preliminary and not yet validated — we'd welcome your .
Developing cutting-edge AI technologies requires significant computational power, expertise, financial resources, and datasets. As such, there is a risk that the most influential and valuable AI technologies, along with their political and competitive benefits, could be monopolized by a handful of powerful entities, such as major technology corporations or governments. If AI is primarily controlled by a few entities, its instructions and data could reflect their narrow perspectives, experiences, and priorities. Without inputs from diverse parties, AI systems may operate in ways that systematically favor the controlling entity and fail to serve the needs of the broader population.
Current AI systems suffer from global inequities in performance and access that disproportionately impact historically disadvantaged groups. These inequities often relate to language, culture, knowledge, paywalls, and access to hardware or the internet. As the integration of AI systems into a wider range of applications and services becomes simpler, these existing disparities could be entrenched and broadened.
In situations where AI is embedded in essential services (e.g., social security and welfare, tax filing, insurance, hospital infrastructure), many more people, including those who are currently disenfranchised, may be denied appropriate access to critical resources and benefits. The centralization of AI systems and their authoritative power could also enable governments or other empowered actors to pursue overly aggressive forms of censorship, oppression, and surveillance. Over time, these measures may become normalized, weakening or eliminating the checks and balances that prevent the abuse of power.
Excerpt from the MIT AI Risk Repository full report
AI-driven concentration of power and resources within certain entities or groups, especially those with access to or ownership of powerful AI systems, leading to inequitable distribution of benefits and increased societal inequality.
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
McDonald's was sued in a federal class-action lawsuit alleging that the company used an AI-powered pricing engine to illegally coordinate and fix menu prices across its corporate and franchised restaurants.
Developers: Tiger Analytics, McDonald's, Deloitte, Algorithmic pricing system developers
Deployers: Restaurants, McDonald's franchisees, McDonald's
UDR, Inc. faces a class-action lawsuit in San Diego for allegedly using RealPage's YieldStar algorithmic pricing software to unlawfully inflate rental prices based on nonpublic competitor data.
Developers: Rental pricing algorithm developers, RealPage, Property management software developers
Deployers: UDR, Residential property management companies, Landlords
Coupang, a major South Korean e-commerce company, faced investigations and regulatory fines for allegedly manipulating its search algorithms to prioritize its own private-label products over those of third-party suppliers. The Korea Fair Trade Commission (KFTC) found that the company engaged in unfair business practices, including coercing suppliers into unfavorable pricing and advertising agreements. This incident highlights the use of algorithmic ranking systems to distort market competition and disadvantage smaller vendors.
Developers: Coupang
Deployers: Coupang
of the 272 AI experts surveyed named power centralization and unfair distribution of benefits 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 power centralization and unfair distribution of benefits reaching catastrophic levels (>1M deaths, >$100B loss, or civilization-scale damage) within five years, falling to 11.4% with pragmatic mitigations.
With pragmatic mitigations
Average severity falls from severe to substantial.
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.
48 shared governance docs
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.
45 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.
42 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.
41 shared governance docs
Establishes the Texas Responsible Artificial Intelligence Governance Act to regulate AI systems. Prohibits certain manipulative behaviors and uses of biometric data by AI systems. Requires consumer disclosure when interacting with AI. Creates the Texas Artificial Intelligence Council for oversight and recommendations. Establishes a regulatory sandbox program for innovative AI systems. Enforces compliance through the Attorney General.
Establishes the Artificial Intelligence Council to regulate AI, preventing harm, discrimination, and privacy infringement, and requires disclosures of AI use to consumers. Establishes the AI Council and Sandbox Program for testing AI systems and authorizes the attorney general to enforce compliance and impose penalties.
Supports the 23 Asilomar AI Principles as guiding values for AI development and policy. Emphasizes safety, transparency, value alignment, shared benefit, and privacy. Urges cooperation among AI researchers and policymakers, and promotes avoiding an AI arms race.
