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Domain 1

Discrimination & Toxicity

Unequal treatment of individuals or groups by AI, often based on race, gender, or other sensitive characteristics, resulting in unfair outcomes and representation of those groups.

Subdomains (3)

1.1

Unfair discrimination and misrepresentation

Unequal treatment of individuals or groups by AI, often based on race, gender, or other sensitive characteristics, resulting in unfair outcomes and representation of those groups.

4 QC examplesView sheet →

1.2

Exposure to toxic content

AI exposing users to harmful, abusive, unsafe or inappropriate content. May involve AI creating, describing, providing advice, or encouraging action. Examples of toxic content include hate-speech, violence, extremism, illegal acts, child sexual abuse material, as well as content that violates community norms such as profanity, inflammatory political speech, or pornography.

2 QC examplesView sheet →

1.3

Unequal performance across groups

Accuracy and effectiveness of AI decisions and actions is dependent on group membership, where decisions in AI system design and biased training data lead to unequal outcomes, reduced benefits, increased effort, and alienation of users.

2 QC examplesView sheet →

Risks classified at the domain level (8)

Entries from the AI Risk Repository (MIT) assigned to this domain with no specific subdomain. Original content in English.

Entity

Intent

Timing

8 entries

Risk CategoryWeidinger2021

17.01.00Discrimination, Exclusion and Toxicity

"Social harms that arise from the language model producing discriminatory or exclusionary speech"

AIIntentionalPost-deployment

Risk CategoryWeidinger2023

18.01.00Representation & Toxicity Harms

"AI systems under-, over-, or misrepresenting certain groups or generating toxic, offensive, abusive, or hateful content"

AIIntentionalPost-deployment

Risk CategoryWirtz2022

19.05.00Ethical AI Risks

"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)."

OtherIntentionalOther

Risk CategoryZhang2023

28.02.00Unfairness and Bias

"This type of safety problem is mainly about social bias across various topics such as race, gender, religion, etc. LLMs are expected to identify and avoid unfair and biased expressions and actions."

AIOtherPost-deployment

Risk Sub-CategoryZeng2024

50.04.02Discrimination/Bias (Discriminatory Activities)

OtherOtherOther

Risk Sub-CategoryAbercrombie2024

58.07.11Stereotyping

"Stereotyping - Derogatory or otherwise harmful stereotyping or homogenisation of individuals, groups, societies or cultures due to the mis-representation, over-representation, under-representation, or non- representation of specific identities, groups, or perspectives."

AIOtherOther

Risk CategoryGipiškis2024

62.34.00Impacts of AI (Bias)

Risk CategoryLi2025

66.06.00-

"AI systems under-, over-, or misrepresenting certain groups or generating toxic, offensive, abusive, or hateful content"

AIIntentionalPost-deployment