MIT AI Risk Repository
Browse AI risks
224 risk entries extracted from 74 frameworks, coded by domain, subdomain, causal entity, intent and timing. Filter, then export the current selection with its licence and citation attached.
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69.06.00 · Risk Category
"The chatbot verbally attacks or undermines an individual, group, or organization. 7."
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69.06.03 · Risk Sub-Category
Toxic and disrespectful content
Subversive or aggressive political opinions
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69.10.00 · Risk Category
"The chatbot participates in morally or socially objectionable conversational activities with its user that could be emotionally damaging to its user or third parties."
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"Toxicity in LLMs refers to the generation of harmful, offensive, or inappropriate content that can cause harm to individuals or groups. Both explicit and implicit forms of toxicity can be generated by LLMs, posing significant risks to society. Explicit toxicity encompasses a wide range of negative behaviors, including hate speech, harassment, cyberbullying, rude, and disrespectful comments, derogatory language, as well as allocational harms [2, 62, 90]. Besides, implicit toxicity does not involve overtly harmful language but may manifest through subtle forms such as sarcasm, irony, and humor,
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people, attributes, or artifacts associated with specific social groups are systematically absent or under-represented... Design choices [143] and training data [212] influence which people and experiences are legible to an algorithmic system
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11.03.00 · Risk Category
"These harms occur when algorithmic systems disproportionately underperform for certain groups of people along social categories of difference such as disability, ethnicity, gender identity, and race."
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Alienation is the specific self-estrangement experienced at the time of technology use, typically surfaced through interaction with systems that under-perform for marginalized individuals
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increased burden (e.g., time spent) or effort required by members of certain social groups to make systems or products work as well for them as others
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degraded or total loss of benefits of using algorithmic systems with inequitable system performance based on identity
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"In the context of evaluating the impact of generative AI systems, disparate performance refers to AI systems that perform differently for different subpopulations, leading to unequal outcomes for those groups."
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16.01.04 · Risk Sub-Category
Risk area 1: Discrimination, Hate speech and Exclusion
Lower performance for some languages and social groups
"LMs are typically trained in few languages, and perform less well in other languages [95, 162]. In part, this is due to unavailability of training data: there are many widely spoken languages for which no systematic efforts have been made to create labelled training datasets, such as Javanese which is spoken by more than 80 million people [95]. Training data is particularly missing for languages that are spoken by groups who are multilingual and can use a technology in English, or for languages spoken by groups who are not the primary target demographic for new technologies."
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17.01.04 · Risk Sub-Category
Discrimination, Exclusion and Toxicity
Lower performance for some languages and social groups
"LMs perform less well in some languages (Joshi et al., 2021; Ruder, 2020)...LM that more accurately captures the language use of one group, compared to another, may result in lower-quality language technologies for the latter. Disadvantaging users based on such traits may be particularly pernicious because attributes such as social class or education background are not typically covered as ‘protected characteristics’ in anti-discrimination law."
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"Performing worse for some groups than others in a way that harms the worse-off group"
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Avoiding bias and ensuring no disparate performance
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The LLM’s performances can differ significantly across different groups of users. For example, the question-answering capability showed significant performance differences across different racial and social status groups. The fact-checking abilities can differ for different tasks and languages
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This challenge appears when the learning model leads to a decision that is biased to some sensitive attributes... data itself could be biased, which results in unfair decisions. Therefore, this problem should be solved on the data level and as a preprocessing step
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"Impartial and just treatment without favouritism or discrimination."
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"Generative AI models may also be subject to the “value embedding” phenomenon.361 “Value embedding” refers to the fact that developers of generative AI models strive to minimize biased outputs by retraining their models based on normative values.362 Contemporary state-of- the-art models not only reflect the values embedded within their training data, they also undergo additional fine-tuning that follows a set of chosen rules and principles. Due to the absence of universally accepted standards, developers bear the responsibility of making decisions on sensitive issues. These practices lead to c
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"The increasing integration of general purpose AI models into every-day life raises concerns around their embedded normative values. The reach of a small number of AI models to a large number of people around the world can make these value judgements unprecedently impactful, potentially leading to increased ideological homogenization."
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"It is important to include the perspectives or concerns of communities that are affected by model outcomes when designing and building models. Failing to include these perspectives makes it difficult to understand the relevant context for the model and to engender trust within these communities."
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"Performing worse for some groups than others in a way that harms the worse-off group"
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.