MIT AI Risk Repository
Browse AI risks
4 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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39.19.00 · Risk Category
An essential feature of decision-making in humans, AI, and also HLI-based agents is accountability. Implementing this feature in machines is a difficult task because many challenges should be considered to organize an AI-based model that is accountable. It should be noted that this issue in human decision-making is not ideal, and many factors such as bias, diversity, fairness, paradox, and ambiguity may affect it. In addition, the human decision-making process is based on personal flexibility, context-sensitive paradigms, empathy, and complex moral judgments. Therefore, all of these challenges
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39.20.00 · Risk Category
an external entity of an AI-based ecosystem may want to know which parts of data affect the final decision in a learning model
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39.21.00 · Risk Category
How a learning model can be reproduced when it is obtained based on various sets of data and a large space of parameters. This problem becomes more challenging in data-driven learning procedures without transparent instructions
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39.25.00 · Risk Category
In many applications of AI-based systems such as medical healthcare and military services, the lack of verification of code may not be tolerable... due to some characteristics such as the non-linear and complex structure of AI-based solutions, existing solutions have been generally considered “black boxes”, not providing any information about what exactly makes them appear in their predictions and decision-making processes.
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.