MIT AI Risk Repository
Browse AI risks
14 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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11.01.00 · Risk Category
"beliefs about different social groups that reproduce unjust societal hierarchies"
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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"
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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
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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
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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
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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
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11.02.00 · Risk Category
"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]."
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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]
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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
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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
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.