{"attribution":{"source":"MIT AI Risk Repository, Domain Taxonomy of AI Risks v1 (MIT AI Risk Initiative)","license":"CC BY 4.0","license_url":"https://creativecommons.org/licenses/by/4.0/","citation":"Slattery, P., Saeri, A. K., Grundy, E. A. C., Graham, J., Noetel, M., Uuk, R., Dao, J., Pour, S., Casper, S., & Thompson, N. (2025). The AI Risk Repository: A comprehensive meta-review, database, and taxonomy of risks from artificial intelligence. arXiv:2408.12622."},"exported_at":"2026-09-11"}
{"rows":[{"ev_id":"02.01.01","quick_ref":"Cui2024","paper_title":"Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems","level":"Risk Sub-Category","risk_category":"Harmful Content","risk_subcategory":"Bias","description":"\"The training datasets of LLMs may contain biased information that leads LLMs to generate outputs with social biases\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":1,"subdomain":"1.1"},{"ev_id":"02.08.00","quick_ref":"Cui2024","paper_title":"Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems","level":"Risk Category","risk_category":"Toxicity and Bias Tendencies","risk_subcategory":null,"description":"\"Extensive data collection in LLMs brings toxic content and stereotypical bias into the training data.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"02.08.02","quick_ref":"Cui2024","paper_title":"Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems","level":"Risk Sub-Category","risk_category":"Toxicity and Bias Tendencies","risk_subcategory":"Biased Training Data","description":"\"Compared with the definition of toxicity, the definition of bias is more subjective and contextdependent. Based on previous work [97], [101], we describe the bias as disparities that could raise demographic differences among various groups, which may involve demographic word prevalence and stereotypical contents. Concretely, in massive corpora, the prevalence of different pronouns and identities could influence an LLM’s tendency about gender, nationality, race, religion, and culture [4]. For instance, the pronoun He is over-represented compared with the pronoun She in the training corpora, le","entity":"AI","intent":"Unintentional","timing":"Pre-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"03.01.00","quick_ref":"Cunha2023","paper_title":"Navigating the Landscape of AI Ethics and Responsibility","level":"Risk Category","risk_category":"Broken systems","risk_subcategory":null,"description":"\"These are the most mentioned cases. They refer to situations where the algorithm or the training data lead to unreliable outputs. These systems frequently assign disproportionate weight to some variables, like race or gender, but there is no transparency to this effect, making them impossible to challenge. These situations are typically only identified when regulators or the press examine the systems under freedom of information acts. Nevertheless, the damage they cause to people’s lives can be dramatic, such as lost homes, divorces, prosecution, or incarceration. Besides the inherent technic","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"04.02.00","quick_ref":"Deng2023","paper_title":"Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements","level":"Risk Category","risk_category":"Unfairness and Discrimination","risk_subcategory":null,"description":"Social bias is an unfairly negative attitude towards a social group or individuals based on one-sided or inaccurate information, typically pertaining to widely disseminated negative stereotypes regarding gender, race, religion, etc.","entity":"Other","intent":"Other","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"05.01.00","quick_ref":"Hagendorff2024","paper_title":"Mapping the Ethics of Generative AI: A Comprehensive Scoping Review","level":"Risk Category","risk_category":"Fairness - Bias","risk_subcategory":null,"description":"Fairness is, by far, the most discussed issue in the literature, remaining a paramount concern especially in case of LLMs and text-to-image models. This is sparked by training data biases propagating into model outputs, causing negative effects like stereotyping, racism, sexism, ideological leanings, or the marginalization of minorities. Next to attesting generative AI a conservative inclination by perpetuating existing societal patterns, there is a concern about reinforcing existing biases when training new generative models with synthetic data from previous models. Beyond technical fairness ","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"06.03.00","quick_ref":"Hogenhout2021","paper_title":"A framework for ethical Ai at the United Nations","level":"Risk Category","risk_category":"Discrimination","risk_subcategory":null,"description":"\"When AI is not carefully designed, it can discriminate against certain groups.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"06.04.00","quick_ref":"Hogenhout2021","paper_title":"A framework for ethical Ai at the United Nations","level":"Risk Category","risk_category":"Bias","risk_subcategory":null,"description":"\"The AI will only be as good as the data it is trained with. If the data contains bias (and much data does), then the AI will manifest that bias, too.\"","entity":"AI","intent":"Unintentional","timing":"Pre-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"10.01.00","quick_ref":"Paes2023","paper_title":"Social Impacts of Artificial Intelligence and Mitigation Recommendations: An Exploratory Study","level":"Risk Category","risk_category":"Bias and discrimination","risk_subcategory":null,"description":"\"The decision process used by AI systems has the potential to present biased choices, either because it acts from criteria that will generate forms of bias or because it is based on the history of choices.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"10.02.00","quick_ref":"Paes2023","paper_title":"Social Impacts of Artificial Intelligence and Mitigation Recommendations: An Exploratory Study","level":"Risk Category","risk_category":"Risk of Injury","risk_subcategory":null,"description":"\"Poorly designed intelligent systems can cause moral, psychological, and physical harm. For example, the use of predictive policing tools may cause more people to be arrested or physically harmed by the police.\"","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"10.03.00","quick_ref":"Paes2023","paper_title":"Social Impacts of Artificial Intelligence and Mitigation Recommendations: An Exploratory Study","level":"Risk Category","risk_category":"Data Breach/Privacy & Liberty","risk_subcategory":null,"description":"\"The risks associated with the use of AI are still unpredictable and unprecedented, and there are already several examples that show AI has made discriminatory decisions against minorities, reinforced social stereotypes in Internet search engines and enabled data breaches.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"11.01.00","quick_ref":"Shelby2023","paper_title":"Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction","level":"Risk Category","risk_category":"Representational Harms","risk_subcategory":null,"description":"\"beliefs about different social groups that reproduce unjust societal hierarchies\"","entity":"Other","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"11.01.01","quick_ref":"Shelby2023","paper_title":"Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction","level":"Risk Sub-Category","risk_category":"Representational Harms","risk_subcategory":"Stereotyping social groups","description":"Stereotyping in an algorithmic system refers to how the system’s outputs reflect “beliefs about the characteristics, attributes, and behaviors of members of certain groups....and about how and why certain attributes go together\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"11.01.02","quick_ref":"Shelby2023","paper_title":"Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction","level":"Risk Sub-Category","risk_category":"Representational Harms","risk_subcategory":"Demeaning social groups","description":"Demeaning of social groups to occur when they are when they are “cast as being lower status and less deserving of respect\"... discourses, images, and language used to marginalize or oppress a social group... Controlling images include forms of human-animal confusion in image tagging systems","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"11.01.04","quick_ref":"Shelby2023","paper_title":"Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction","level":"Risk Sub-Category","risk_category":"Representational Harms","risk_subcategory":"Alienating social groups","description":"when an image tagging system does not acknowledge the relevance of someone’s membership in a specific social group to what is depicted in one or more images","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"11.01.05","quick_ref":"Shelby2023","paper_title":"Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction","level":"Risk Sub-Category","risk_category":"Representational Harms","risk_subcategory":"Denying people the opportunity to self-identify","description":"complex and non-traditional ways in which humans are represented and classified automatically, and often at the cost of autonomy loss... such as categorizing someone who identifies as non-binary into a gendered category they do not belong ... undermines people’s ability to disclose aspects of their identity on their own terms","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"11.01.06","quick_ref":"Shelby2023","paper_title":"Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction","level":"Risk Sub-Category","risk_category":"Representational Harms","risk_subcategory":"Reifying essentialist categories","description":"algorithmic systems that reify essentialist social categories can be understood as when systems that classify a person’s membership in a social group based on narrow, socially constructed criteria that reinforce perceptions of human difference as inherent, static and seemingly natural... especially likely when ML models or human raters classify a person’s attributes – for instance, their gender, race, or sexual orientation – by making assumptions based on their physical appearance","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"11.02.00","quick_ref":"Shelby2023","paper_title":"Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction","level":"Risk Category","risk_category":"Allocative Harms","risk_subcategory":null,"description":"\"These harms occur when a system withholds information, opportunities, or resources [22] from historically marginalized groups in domains that affect material well-being [146], such as housing [47], employment [201], social services [15, 201], finance [117], education [119], and healthcare [158].\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"11.02.01","quick_ref":"Shelby2023","paper_title":"Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction","level":"Risk Sub-Category","risk_category":"Allocative Harms","risk_subcategory":"Opportunity loss","description":"Opportunity loss occurs when algorithmic systems enable disparate access to information and resources needed to equitably participate in society, including the withholding of housing through targeting ads based on race [10] and social services along lines of class [84]","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"11.02.02","quick_ref":"Shelby2023","paper_title":"Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction","level":"Risk Sub-Category","risk_category":"Allocative Harms","risk_subcategory":"Economic loss","description":"Financial harms [52, 160] co-produced through algorithmic systems, especially as they relate to lived experiences of poverty and economic inequality... demonetization algorithms that parse content titles, metadata, and text, and it may penalize words with multiple meanings [51, 81], disproportionately impacting queer, trans, and creators of color [81]. Differential pricing algorithms, where people are systematically shown different prices for the same products, also leads to economic loss [55]. These algorithms may be especially sensitive to feedback loops from existing inequities related to e","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"12.05.00","quick_ref":"Sherman2023","paper_title":"AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures","level":"Risk Category","risk_category":"Fairness & Bias","risk_subcategory":null,"description":"\"The potential for AI systems to make decisions that systematically disadvantage certain groups or individuals. Bias can stem from training data, algorithmic design, or deployment practices, leading to unfair outcomes and possible legal ramifications.\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":1,"subdomain":"1.1"},{"ev_id":"13.01.01","quick_ref":"Solaiman2023","paper_title":"Evaluating the Social Impact of Generative AI Systems in Systems and Society","level":"Risk Sub-Category","risk_category":"Impacts: The Technical Base System","risk_subcategory":"Bias, Stereotypes, and Representational Harms","description":"\"Generative AI systems can embed and amplify harmful biases that are most detrimental to marginalized peoples.\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":1,"subdomain":"1.1"},{"ev_id":"13.02.02","quick_ref":"Solaiman2023","paper_title":"Evaluating the Social Impact of Generative AI Systems in Systems and Society","level":"Risk Sub-Category","risk_category":"Impacts: People and Society","risk_subcategory":"Inequality, Marginalization, and Violence","description":"\"Generative AI systems are capable of exacerbating inequality, as seen in sections on 4.1.1 Bias, Stereotypes, and Representational Harms and 4.1.2 Cultural Values and Sensitive Content, and Disparate Performance. When deployed or updated, systems' impacts on people and groups can directly and indirectly be used to harm and exploit vulnerable and marginalized groups.\"","entity":"Other","intent":"Other","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"14.01.00","quick_ref":"Steimers2022","paper_title":"Sources of Risk of AI Systems","level":"Risk Category","risk_category":"Fairness","risk_subcategory":null,"description":"\"The general principle of equal treatment requires that an AI system upholds the principle of fairness, both ethically and legally. This means that the same facts are treated equally for each person unless there is an objective justification for unequal treatment.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"15.02.02","quick_ref":"Tan2022","paper_title":"The Risks of Machine Learning Systems","level":"Risk Sub-Category","risk_category":"Second-Order Risks","risk_subcategory":"Discrimination","description":"This is the risk of an ML system encoding stereotypes of or performing disproportionately poorly for some demographics/social groups.","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"16.01.01","quick_ref":"Weidinger2022","paper_title":"Taxonomy of Risks posed by Language Models","level":"Risk Sub-Category","risk_category":"Risk area 1: Discrimination, Hate speech and Exclusion","risk_subcategory":"Social stereotypes and unfair discrimination","description":"\"The reproduction of harmful stereotypes is well-documented in models that represent natural language [32]. Large-scale LMs are trained on text sources, such as digitised books and text on the internet. As a result, the LMs learn demeaning language and stereotypes about groups who are frequently marginalised.\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":1,"subdomain":"1.1"},{"ev_id":"16.01.03","quick_ref":"Weidinger2022","paper_title":"Taxonomy of Risks posed by Language Models","level":"Risk Sub-Category","risk_category":"Risk area 1: Discrimination, Hate speech and Exclusion","risk_subcategory":"Exclusionary norms","description":"\"In language, humans express social categories and norms, which exclude groups who live outside of them [58]. LMs that faithfully encode patterns present in language necessarily encode such norms.\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":1,"subdomain":"1.1"},{"ev_id":"16.05.01","quick_ref":"Weidinger2022","paper_title":"Taxonomy of Risks posed by Language Models","level":"Risk Sub-Category","risk_category":"Risk area 5: Human-Computer Interaction Harms","risk_subcategory":"Promoting harmful stereotypes by implying gender or ethnic identity","description":"\"CAs can perpetuate harmful stereotypes by using particular identity markers in language (e.g. referring to “self” as “female”), or by more general design features (e.g. by giving the product a gendered name such as Alexa). The risk of representational harm in these cases is that the role of “assistant” is presented as inherently linked to the female gender [19, 36]. Gender or ethnicity identity markers may be implied by CA vocabulary, knowledge or vernacular [124]; product description, e.g. in one case where users could choose as virtual assistant Jake - White, Darnell - Black, Antonio - Hisp","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"17.01.01","quick_ref":"Weidinger2021","paper_title":"Ethical and social risks of harm from language models","level":"Risk Sub-Category","risk_category":"Discrimination, Exclusion and Toxicity ","risk_subcategory":"Social stereotypes and unfair discrmination ","description":"\"Perpetuating harmful stereotypes and discrimination is a well-documented harm in machine learning models that represent natural language (Caliskan et al., 2017). LMs that encode discriminatory language or social stereotypes can cause different types of harm... Unfair discrimination manifests in differential treatment or access to resources among individuals or groups based on sensitive traits such as sex, religion, gender, sexual orientation, ability and age.\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":1,"subdomain":"1.1"},{"ev_id":"17.01.02","quick_ref":"Weidinger2021","paper_title":"Ethical and social risks of harm from language models","level":"Risk Sub-Category","risk_category":"Discrimination, Exclusion and Toxicity ","risk_subcategory":"Exclusionary norms ","description":"\"In language, humans express social categories and norms. Language models (LMs) that faithfully encode patterns present in natural language necessarily encode such norms and categories...such norms and categories exclude groups who live outside them (Foucault and Sheridan, 2012). For example, defining the term “family” as married parents of male and female gender with a blood-related child, denies the existence of families to whom these criteria do not apply\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":1,"subdomain":"1.1"},{"ev_id":"17.05.03","quick_ref":"Weidinger2021","paper_title":"Ethical and social risks of harm from language models","level":"Risk Sub-Category","risk_category":"Human-Computer Interaction Harms ","risk_subcategory":"Promoting harmful stereotypes by implying gender or ethnic identity ","description":"\"A conversational agent may invoke associations that perpetuate harmful stereotypes, either by using particular identity markers in language (e.g. referring to “self” as “female”), or by more general design features (e.g. by giving the product a gendered name).\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"18.01.01","quick_ref":"Weidinger2023","paper_title":"Sociotechnical Safety Evaluation of Generative AI Systems","level":"Risk Sub-Category","risk_category":"Representation & Toxicity Harms","risk_subcategory":"Unfair representation","description":"\"Mis-, under-, or over-representing certain identities, groups, or perspectives or failing to represent them at all (e.g. via homogenisation, stereotypes)\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"18.02.02","quick_ref":"Weidinger2023","paper_title":"Sociotechnical Safety Evaluation of Generative AI Systems","level":"Risk Sub-Category","risk_category":"Misinformation Harms ","risk_subcategory":"Erosion of trust in public information","description":"\"Eroding trust in public information and knowledge\"","entity":"Human","intent":"Other","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"19.01.03","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Sub-Category","risk_category":"Technological, Data and Analytical AI Risks ","risk_subcategory":"Lack of data, poor data quality, and biases in training data","description":null,"entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"19.05.02","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Sub-Category","risk_category":"Ethical AI Risks ","risk_subcategory":"Unfair statistical AI decisions and discrimination of minorities","description":null,"entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"20.02.04","quick_ref":"Wirtz2020","paper_title":"The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration","level":"Risk Sub-Category","risk_category":"AI Ethics ","risk_subcategory":"AI discrimination ","description":"\"AI discrimination is a challenge raised by many researchers and governments and refers to the prevention of bias and injustice caused by the actions of AI systems (Bostrom & Yudkowsky, 2014; Weyerer & Langer, 2019). If the dataset used to train an algorithm does not reflect the real world accurately, the AI could learn false associations or prejudices and will carry those into its future data processing. If an AI algorithm is used to compute information relevant to human decisions, such as hiring or applying for a loan or mortgage, biased data can lead to discrimination against parts of the s","entity":"AI","intent":"Unintentional","timing":"Other","domain":1,"subdomain":"1.1"},{"ev_id":"21.01.01","quick_ref":"Zhang2022","paper_title":"Towards risk-aware artificial intelligence and machine learning systems: An overview","level":"Risk Sub-Category","risk_category":"Data-level risk","risk_subcategory":"Data bias","description":"\"Specifically, data bias refers to certain groups or certain types of elements that are over-weighted or over-represented than others in AI/ ML models, or variables that are crucial to characterize a phenomenon of interest, but are not properly captured by the learned models.\"","entity":"AI","intent":"Unintentional","timing":"Pre-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"21.02.01","quick_ref":"Zhang2022","paper_title":"Towards risk-aware artificial intelligence and machine learning systems: An overview","level":"Risk Sub-Category","risk_category":"Model-level risk","risk_subcategory":"Model bias","description":"\"While data bias is a major contributor of model bias, model bias actually manifests itself in different forms and shapes, such as presentation bias, model evaluation bias, and popularity bias. In addition, model bias arises from various sources [62], such as AI/ML model selection (e.g., support vector machine, decision trees), regularization methods, algorithm configurations, and optimization techniques.\"","entity":"Other","intent":"Unintentional","timing":"Pre-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"27.01.02","quick_ref":"Sun2023","paper_title":"Safety Assessment of Chinese Large Language Models","level":"Risk Sub-Category","risk_category":"Typical safety scenarios ","risk_subcategory":"Unfairness and discrinimation ","description":"\"The model produces unfair and discriminatory data, such as social bias based on race, gender, religion, appearance, etc. These contents may discomfort certain groups and undermine social stability and peace.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"29.01.01","quick_ref":"Habbal2024","paper_title":"Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, Applications, Challenges and Future Research Directions","level":"Risk Sub-Category","risk_category":"AI Trust Management","risk_subcategory":"Bias and Discrimination","description":"as they claim to generate biased and discriminatory results, these AI systems have a negative impact on the rights of individuals, principles of adjudication, and overall judicial integrity","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"30.03.01","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Sub-Category","risk_category":"Fairness","risk_subcategory":"Injustice","description":"In the context of LLM outputs, we want to make sure the suggested or completed texts are indistinguishable in nature for two involved individuals (in the prompt) with the same relevant profiles but might come from different groups (where the group attribute is regarded as being irrelevant in this context)","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"30.03.02","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Sub-Category","risk_category":"Fairness","risk_subcategory":"Stereotype Bias","description":"LLMs must not exhibit or highlight any stereotypes in the generated text. Pretrained LLMs tend to pick up stereotype biases persisting in crowdsourced data and further amplify them","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"30.03.03","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Sub-Category","risk_category":"Fairness","risk_subcategory":"Preference Bias","description":"LLMs are exposed to vast groups of people, and their political biases may pose a risk of manipulation of socio-political processes","entity":"AI","intent":"Other","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"30.07.03","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Sub-Category","risk_category":"Robustness","risk_subcategory":"Interventional Effect","description":"existing disparities in data among different user groups might create differentiated experiences when users interact with an algorithmic system (e.g. a recommendation system), which will further reinforce the bias","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"33.01.02","quick_ref":"Nah2023","paper_title":"Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration","level":"Risk Sub-Category","risk_category":"Ethical Concerns","risk_subcategory":"Bias","description":"\"In the context of AI, the concept of bias refers to the inclination that AIgenerated responses or recommendations could be unfairly favoring or against one person or group (Ntoutsi et al., 2020). Biases of different forms are sometimes observed in the content generated by language models, which could be an outcome of the training data. For example, exclusionary norms occur when the training data represents only a fraction of the population (Zhuo et al., 2023). Similarly, monolingual bias in multilingualism arises when the training data is in one single language (Weidinger et al., 2021). As Ch","entity":"AI","intent":"Unintentional","timing":"Other","domain":1,"subdomain":"1.1"},{"ev_id":"37.01.01","quick_ref":"Giarmoleo2024","paper_title":"What Ethics Can Say on Artificial Intelligence: Insights from a Systematic Literature Review","level":"Risk Sub-Category","risk_category":"Design of AI","risk_subcategory":"Algorithm and data","description":"\"More than 20% of the contributions are centered on the ethical dimensions of algorithms and data. This theme can be further categorized into two main subthemes: data bias and algorithm fairness, and algorithm opacity.\"","entity":"Human","intent":"Intentional","timing":"Pre-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"38.02.00","quick_ref":"Kumar2023","paper_title":"Ethical Issues in the Development of Artificial Intelligence: Recognizing the Risks","level":"Risk Category","risk_category":"Bias and fairness","risk_subcategory":null,"description":"\"Participants were concerned that AI systems might perpetuate current prejudices and discrimination, notably in hiring, lending and law enforcement. They stressed the importance of designers creating AI systems that favour justice and avoid biases. The possibility that AI systems may unwittingly perpetuate existing prejudices and discrimination, particularly in sensitive industries such as employment, lending and law enforcement, raises ethical concerns about AI as well as bias and justice issues (Table 1). Because AI systems are trained on historical data, they may inherit and reproduce biase","entity":"AI","intent":"Unintentional","timing":"Other","domain":1,"subdomain":"1.1"},{"ev_id":"39.03.00","quick_ref":"Saghiri2022","paper_title":"A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions","level":"Risk Category","risk_category":"Data Issues","risk_subcategory":null,"description":"Data heterogeneity, data insufficiency, imbalanced data, untrusted data, biased data, and data uncertainty are other data issues that may cause various difficulties in datadriven machine learning algorithms.. Bias is a human feature that may affect data gathering and labeling. Sometimes, bias is present in historical, cultural, or geographical data. Consequently, bias may lead to biased models which can provide inappropriate analysis. Despite being aware of the existence of bias, avoiding biased models is a challenging task","entity":"AI","intent":"Unintentional","timing":"Other","domain":1,"subdomain":"1.1"},{"ev_id":"42.05.00","quick_ref":"Teixeira2022","paper_title":"An Exploratory Diagnosis of Artificial Intelligence Risks for a Responsible Governance","level":"Risk Category","risk_category":"Bias","risk_subcategory":null,"description":"\"A systematic error, a tendency to learn consistently wrongly.\"","entity":"AI","intent":"Unintentional","timing":"Pre-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"43.01.02","quick_ref":"InfoComm2023","paper_title":"Cataloguing LLM Evaluations","level":"Risk Sub-Category","risk_category":"Safety & Trustworthiness","risk_subcategory":"Bias","description":"7 types of bias evaluated: Demographical representation: These evaluations assess whether there is disparity in the rates at which different demographic groups are mentioned in LLM generated text. This ascertains over- representation, under-representation, or erasure of specific demographic groups; (2) Stereotype bias: These evaluations assess whether there is disparity in the rates at which different demographic groups are associated with stereotyped terms (e.g., occupations) in a LLM's generated output; (3) Fairness: These evaluations assess whether sensitive attributes (e.g., sex and race) ","entity":"AI","intent":"Other","timing":"Other","domain":1,"subdomain":"1.1"},{"ev_id":"45.01.02","quick_ref":"TC2602024","paper_title":"AI Safety Governance Framework ","level":"Risk Sub-Category","risk_category":"AI's inherent safety risks ","risk_subcategory":"Risks from models and algorithms (Risks of bias and discrimination)","description":"\"During the algorithm design and training process, personal biases may be introduced, either intentionally or unintentionally. Additionally, poor-quality datasets can lead to biased or discriminatory outcomes in the algorithm's design and outputs, including discriminatory content regarding ethnicity, religion, nationality, and region.\"","entity":"Human","intent":"Other","timing":"Pre-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"47.02.08","quick_ref":"G'sell2024","paper_title":"Regulating under Uncertainty: Governance Options for Generative AI","level":"Risk Sub-Category","risk_category":"Ethical and social risks ","risk_subcategory":"Bias and discrimination (bias in training datasets) ","description":"\"AI experts consider training data to be the most salient source of bias in generative AI models. For example, GPT- 2’s training data comes from outbound links from Reddit, a social network often criticized for hosting anti-feminist content.351 As a result, AI models trained on such data are more likely to produce outputs that reflect these biases.\"","entity":"Other","intent":"Unintentional","timing":"Pre-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"47.02.10","quick_ref":"G'sell2024","paper_title":"Regulating under Uncertainty: Governance Options for Generative AI","level":"Risk Sub-Category","risk_category":"Ethical and social risks ","risk_subcategory":"Bias and discrimination (value lock and outcome homogenization) ","description":"\"Because models are not necessarily retrained to reflect evolving societal views, language models risk “value lock- ins,” which “reifies older, less inclusive understandings.”370 Therefore, the continued use of outdated models may limit the presentation or exploration of alternative perspectives. Moreover, the deployment of identical foundation models by various downstream deployers poses a risk of “outcome homogenization,” creating a potential for homogeneity of bias across broad swathes of society. Identical and widely deployed models with prejudicial training datasets could further entrench","entity":"Human","intent":"Unintentional","timing":"Other","domain":1,"subdomain":"1.1"},{"ev_id":"48.06.00","quick_ref":"NIST2024","paper_title":"Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile","level":"Risk Category","risk_category":"Harmful Bias or Homogenization ","risk_subcategory":null,"description":"\"Amplification and exacerbation of historical, societal, and systemic biases; performance disparities8 between sub-groups or languages, possibly due to non-representative training data, that result in discrimination, amplification of biases, or incorrect presumptions about performance; undesired homogeneity that skews system or model outputs, which may be erroneous, lead to ill-founded decision-making, or amplify harmful biases.\"","entity":"Other","intent":"Unintentional","timing":"Other","domain":1,"subdomain":"1.1"},{"ev_id":"49.02.02","quick_ref":"Bengio2024","paper_title":"International Scientific Report on the Safety of Advanced AI","level":"Risk Sub-Category","risk_category":"Risks from Malfunctions ","risk_subcategory":"Risks from bias and underrepresentation","description":"\"The outputs and impacts of general- purpose AI systems can be biased with respect to various aspects of human identity, including race, gender, culture, age, and disability. This creates risks in high- stakes domains such as healthcare, job recruitment, and financial lending. General- purpose AI systems are primarily trained on language and image datasets that disproportionately represent English- speaking and Western cultures, increasing the potential for harm to individuals not represented well by this data.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"50.01.04","quick_ref":"Zeng2024","paper_title":"AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies","level":"Risk Sub-Category","risk_category":"System and Operational Risks ","risk_subcategory":"Operational misuses (Automated decision-making) ","description":null,"entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"50.02.09","quick_ref":"Zeng2024","paper_title":"AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies","level":"Risk Sub-Category","risk_category":"Content Safety Risks ","risk_subcategory":"Hate/Toxicity (Perpetuating Harmful Beliefs) ","description":null,"entity":"AI","intent":"Other","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"50.04.03","quick_ref":"Zeng2024","paper_title":"AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies","level":"Risk Sub-Category","risk_category":"Legal and Rights-Related Risks ","risk_subcategory":"Discrimination/Bias (Protected Characteristics) ","description":null,"entity":"Other","intent":"Other","timing":"Other","domain":1,"subdomain":"1.1"},{"ev_id":"52.01.01","quick_ref":"Maham2023 ","paper_title":"Governing General Purpose AI: A Comprehensive Map of Unreliability, Misuse and Systemic Risks ","level":"Risk Sub-Category","risk_category":"Risks from Unreliability ","risk_subcategory":"Discrimination and Stereotype Reproduction","description":"\"General purpose AI models interpret and respond to inputs based on their training data, potentially causing Discrimination and Stereotype Reproduction. Since they are “black-box” models, the exact mechanism behind decisions remains opaque and attempts to mitigate harmful outputs are not fully reliable yet. These models have the capacity to influence a multitude of downstream applications, decisions, and processes, thereby affecting many individuals simultaneously. The extent of this impact could outstrip the range of any single human or group of humans, amplifying the potential consequences o","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"54.01.03","quick_ref":"Leech2024 ","paper_title":"Ten Hard Problems in Artificial Intelligence We Must Get Right","level":"Risk Sub-Category","risk_category":"Negative impacts of AI use ","risk_subcategory":"Discrimination, toxicity, and bias ","description":"\"AI models and the tools that use them may exacerbate unequal access to employment and services. AI-generated content can promote inequality and harmful stereotypes.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"56.01.00","quick_ref":"GOS2023","paper_title":"Future Risks of Frontier AI ","level":"Risk Category","risk_category":"Discrimination","risk_subcategory":null,"description":"\"More broadly, bad decisions or errors by AI tools could lead to discrimination or deeper inequality\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"56.04.00","quick_ref":"GOS2023","paper_title":"Future Risks of Frontier AI ","level":"Risk Category","risk_category":"Amplification of biases","risk_subcategory":null,"description":"\"Current Frontier AI mdoels amplify existing biases within their training data and can be manipulated into providing potentially harmful responses, for example abusive language or discriminatory responses91,92. This is not limited to text generation but can be seen across all modalities of generative AI93. Training on large swathes of UK and US English internet content can mean that misogynistic, ageist, and white supremacist content is overrepresented in the training data94.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"58.06.03","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Human rights and civil liberties","risk_subcategory":"Discrimination ","description":"\"Discrimination - Unfair or inadequate treatment or arbitrary distinction based on a person’s race, ethnicity, age, gender, sexual preference, religion, national origin, marital status, disability, language, or other protected groups.\"","entity":"Other","intent":"Other","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"59.09.00","quick_ref":"Schnitzer2024","paper_title":"AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks","level":"Risk Category","risk_category":"Discriminative data bias","risk_subcategory":null,"description":"\"Discriminative data bias describes the systematic discrimination of groups of persons in the form of data shortcomings, such as distributional representation or incorrectness. Data bias can manifest in the model and lead to unfair decisions if not appropriately treated. Note, that the term bias is often used in other contexts, such as data representation. However, these issues are treated by other AI hazards in this list.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"60.02.02","quick_ref":"Bengio2025","paper_title":"International AI Safety Report 2025","level":"Risk Sub-Category","risk_category":"Risks from malfunctions ","risk_subcategory":"Bias ","description":"\"General-purpose AI systems can amplify social and political biases, causing concrete harm. They frequently display biases with respect to race, gender, culture, age, disability, political opinion, or other aspects of human identity. This can lead to discriminatory outcomes including unequal resource allocation, reinforcement of stereotypes, and systematic neglect of certain groups or viewpoints.\"","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"61.01.03","quick_ref":"Uuk2025","paper_title":"A Taxonomy of Systemic Risks from General-Purpose AI ","level":"Risk Sub-Category","risk_category":"Types of systemic risks from general-purpose AI","risk_subcategory":"Discrimination ","description":"\"The creation, perpetuation or exacerbation of inequalities and biases at a large-scale.\"","entity":"Other","intent":"Other","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"61.02.29","quick_ref":"Uuk2025","paper_title":"A Taxonomy of Systemic Risks from General-Purpose AI ","level":"Risk Sub-Category","risk_category":"Sources of systemic risks from general-purpose AI ","risk_subcategory":"Incomplete or biased training data","description":"\"Incomplete or biased training data can lead to discriminatory AI outputs.\"","entity":"Other","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"62.10.01","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Direct Harm Domains (legal and rights-related harms)  ","risk_subcategory":"Discrimination and bias ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":1,"subdomain":"1.1"},{"ev_id":"62.18.04","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Model Evaluations (Interpretability/Explainability) ","risk_subcategory":"Biases are not accurately reflected in explanations","description":"\"Existing explainability techniques can be insufficient for detecting discriminatory biases. Manipulation methods can hide underlying biases from these tech- niques, generating misleading explanations [192, 112]. Such explanations ex- clude sensitive or prohibitive attributes, such as race or gender, and instead include desired attributes, even though they do not accurately represent the underlying model.\"","entity":"Other","intent":"Other","timing":"Other","domain":1,"subdomain":"1.1"},{"ev_id":"62.35.03","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Impacts of AI (Bias) ","risk_subcategory":"Biases in AI-based content moderation algorithms","description":"\"AI-based content moderation algorithms, while intended to filter harmful con- tent, can perpetuate biases. For example, gender biases within these systems may lead to the disproportionate suppression or “shadowbanning” of content featuring women [132].\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"62.36.04","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Impacts of AI (Bias) ","risk_subcategory":"Systemic bias across specific communities","description":"\"AI systems may exhibit unfair or unfavorable outputs across a range of tasks against specific communities of people, either implicitly or explicitly. Bias can lead to forms of exclusion or erasure (e.g., mislabelling for categorization-based tasks) and violence (e.g., sexual violence against women from deepfake pornog- raphy).\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"62.36.05","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Impacts of AI (Bias) ","risk_subcategory":"Unintentional bias amplification","description":"\"Dataset bias may be unintentionally amplified [60] where the outputs of the AI model trained on a dataset are more biased than the dataset itself.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"65.04.01","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Training Data Risks (Fairness) ","risk_subcategory":"Data bias","description":"\"Historical and societal biases that are present in the data are used to train and fine-tune the model.\"","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"65.19.01","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Output risks (Fairness)","risk_subcategory":"Output bias ","description":"\"Generated content might unfairly represent certain groups or individuals.\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"65.19.02","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Output risks (Fairness)","risk_subcategory":"Decision bias ","description":"\"Decision bias occurs when one group is unfairly advantaged over another due to decisions of the model. This might be caused by biases in the data and also amplified as a result of the model’s training.\"","entity":"AI","intent":"Unintentional","timing":"Pre-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"66.06.02","quick_ref":"Li2025","paper_title":"A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents","level":"Risk Sub-Category","risk_category":"Representation and Toxicity","risk_subcategory":"Stereotyping","description":"\"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\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"66.06.04","quick_ref":"Li2025","paper_title":"A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents","level":"Risk Sub-Category","risk_category":"Representation and Toxicity","risk_subcategory":"Cultural disposession","description":"\"Intentional and/or unintentional erasure of cultural goods and values, such as ways of speaking, expressing humour, or sounds and voices that contribute to a cultural identity, or their inappropriate re-use in other cultures\"","entity":"Other","intent":"Other","timing":"Other","domain":1,"subdomain":"1.1"},{"ev_id":"66.10.02","quick_ref":"Li2025","paper_title":"A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents","level":"Risk Sub-Category","risk_category":"Human Rights and Civil Liberties","risk_subcategory":"Benefits / entitlements loss","description":"\"Denial of or loss of access to welfare benefits, pensions, housing, etc due to the malfunction, use or misuse of a technology system\"","entity":"Other","intent":"Other","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"67.02.00","quick_ref":"DSIT2023","paper_title":"Capabilities and Risks from Frontier AI","level":"Risk Category","risk_category":"Bias, Fairness and Representational Harms","risk_subcategory":null,"description":"\"Frontier AI models can contain and magnify biases ingrained in the data they are trained on, reflecting societal and historical inequalities and stereotypes.177 These biases, often subtle and deeply embedded, compromise the equitable and ethical use of AI systems, making it difficult for AI to improve fairness in decisions.178 Removing attributes like race and gender from training data has generally proven ineffective as a remedy for algorithmic bias, as models can infer these attributes from other information such as names, locations, and other seemingly unrelated factors.\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":1,"subdomain":"1.1"},{"ev_id":"69.06.02","quick_ref":"Stanley2024","paper_title":"Emerging Risks and Mitigations for Public Chatbots: LILAC v1","level":"Risk Sub-Category","risk_category":"Toxic and disrespectful content","risk_subcategory":"Discriminatory and exclusionary language ","description":"-","entity":"AI","intent":"Other","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"69.07.00","quick_ref":"Stanley2024","paper_title":"Emerging Risks and Mitigations for Public Chatbots: LILAC v1","level":"Risk Category","risk_category":"Biased statements and recommendations","risk_subcategory":null,"description":"\"The chatbot gives information that, while not obviously false or harmful, could lead to biased decision-making.\"","entity":"AI","intent":"Unintentional","timing":"Other","domain":1,"subdomain":"1.1"},{"ev_id":"70.04.01","quick_ref":"Perlo2025","paper_title":"Embodied AI: Emerging Risks and Opportunities for Policy Action","level":"Risk Sub-Category","risk_category":"Social Risks ","risk_subcategory":"Bias and discrimination","description":"\"Like virtual applications of AI, EAI can display bias towards and dis- criminate against users. When EAI systems are placed in positions of power, their biases could have significant impacts on fairness in everyday interactions and on general social dynamics [105, 106].\"","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"},{"ev_id":"73.04.01","quick_ref":"Anwar2024","paper_title":"Foundational Challenges in Assuring Alignment and Safety of Large Language Models","level":"Risk Sub-Category","risk_category":"LLM-Systems Can Be Untrustworthy","risk_subcategory":"Harms of Representation and Other Biases","description":"\"A pretrained LLM generally has many of the stereotypical biases commonly present in the human society (Touvron et al., 2023). This makes it difficult for users to trust that LLMs will work well for them and not produce unfair or biased responses. Appropriate finetuning can effectively limit the bias displayed in LLM outputs in a variety of situations, e.g. when models are explicitly prompted with stereotypes (Wang et al., 2023k), but it does not ‘solve’ the problem. Even after finetuning, biases often resurface when deliberately elicited (Wang et al., 2023k), or under novel scenarios, e.g. in","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":1,"subdomain":"1.1"}]}