MIT AI Risk Repository
Browse AI risks
5 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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38.02.00 · Risk Category
"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
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38.01.00 · Risk Category
"Participants expressed worry about AI systems' possible misuse of personal information. They emphasized the importance of strong data security safeguards and increased openness in how AI systems acquire, store and use data. The increasing dependence on AI systems to manage sensitive personal information raises ethical questions about AI, data privacy and security. As AI technologies grow increasingly integrated into numerous areas of society, there is a greater danger of personal data exploitation or mistreatment. Participants in research frequently express concerns about the effectiveness of
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38.04.00 · Risk Category
"Several participants mentioned how AI systems could influence human agency and decision-making. They emphasized the need of striking a balance between using the benefits of AI and protecting human autonomy and control. The increasing integration of AI systems into various aspects of our lives, which can have a significant impact on human agency and decision-making, has raised ethical concerns about AI and human–AI interaction. As AI systems advance, they will be able to influence, if not completely replace, IJOES human decision-making in some fields, prompting concerns about the loss of human
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38.03.00 · Risk Category
"A recurring complaint among participants was a lack of knowledge about how AI systems made judgements. They emphasized the significance of making AI systems more visible and explainable so that people may have confidence in their outputs and hold them accountable for their activities. Because AI systems are typically opaque, making it difficult for users to understand the rationale behind their judgements, ethical concerns about AI, as well as issues of transparency and explainability, arise. This lack of understanding can generate suspicion and reluctance to adopt AI technology, as well as m
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38.05.00 · Risk Category
"The participants of the study emphasized the importance of trustworthiness and reliability in AI systems. The authors emphasized the importance of preserving precision and objectivity in the outcomes produced by AI systems, while also ensuring transparency in their decision-making procedures. The significance of reliability and credibility in AI systems is escalating in tandem with the proliferation of these technologies across diverse domains of society. This underscores the importance of ensuring user confidence. The concern regarding the dependability of AI systems and their inherent biase
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