MIT AI Risk Repository · Risk Category · 19.05.00

Ethical AI Risks

Open the MIT Risk Navigator All entries from Wirtz2022

Description

"In the context of ethical AI risks, two risks are of particular importance. First, AI systems may lack a legitimate ethical basis in establishing rules that greatly influence society and human relationships (Wirtz & Müller, 2019). In addition, AI-based discrimination refers to an unfair treatment of certain population groups by AI systems. As humans initially programme AI systems, serve as their potential data source, and have an impact on the associated data processes and databases, human biases and prejudices may also become part of AI systems and be reproduced (Weyerer & Langer, 2019, 2020

From Governance of artificial intelligence: A risk and guideline-based integrative framework (Wirtz2022), as extracted by the MIT AI Risk Repository (CC BY 4.0).

Classification

Domain: 1. Discrimination & Toxicity
Subdomain: 1.0
Causal entity: Other
Intent: Unintentional
Timing: Other

How other frameworks describe this risk

Stereotyping
A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)
Impacts of AI (Bias)
Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)
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A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)
Discrimination, Exclusion and Toxicity
Ethical and social risks of harm from language models (Weidinger2021)
Representation & Toxicity Harms
Sociotechnical Safety Evaluation of Generative AI Systems (Weidinger2023)
Discrimination/Bias (Discriminatory Activities)
AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies (Zeng2024)
Unfairness and Bias
SafetyBench: Evaluating the Safety of Large Language Models with Multiple Choice Questions (Zhang2023)

Other entries from Wirtz2022

Technological, Data and Analytical AI Risks Risk Category
Loss of control of autonomous systems and unforeseen behaviour due to lack of transparency and self-programming/ reprogramming Risk Sub-Category · 7.1
Programming error Risk Sub-Category · 7.0
Lack of data, poor data quality, and biases in training data Risk Sub-Category · 1.1
Vulnerability of AI systems to attacks and misuse Risk Sub-Category · 2.2
Lack of AI experts with comprehensive AI knowledge Risk Sub-Category · 6.5
Immaturity of AI technology can cause incorrect decisions Risk Sub-Category · 7.3
High investment costs of AI hinder integration Risk Sub-Category · 6.1
Informational and Communicational AI Risks Risk Category · 4.1
Manipulation and control of information provision (e.g., personalised adds, filtered news) Risk Sub-Category · 4.1
Disinformation and computational propaganda Risk Sub-Category · 4.1
Censorship of opinions expressed in the Internet restricts freedom of expression Risk Sub-Category · 5.2