MIT AI Risk Repository
Browse AI risks
28 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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39.03.00 · Risk Category
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
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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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Users’ data, including location, personal information, and navigation trajectory, are considered as input for most data-driven machine learning methods
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every piece of software, including learning systems, may be hacked by malicious users
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39.10.00 · Risk Category
HLI-based systems such as self-driving drones and vehicles will act autonomously in our world. In these systems, a challenging question is “who is liable when a self-driving system is involved in a crash or failure?”.
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39.02.00 · Risk Category
Some learning algorithms, including deep learning, utilize iterative learning processes [23]. This approach results in high energy consumption.
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39.11.00 · Risk Category
In the era of superintelligence, the agents will be difficult to control for humans... this problem is not solvable considering safety issues, and will be more severe by increasing the autonomy of AI-based agents. Therefore, because of the assumed properties of HLI-based agents, we might be prepared for machines that are definitely possible to be uncontrollable in some situations
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The actions of a learning model may easily hurt humans in both explicit and implicit manners...several algorithms based on Asimov’s laws have been proposed that try to judge the output actions of an agent considering the safety of humans
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39.05.00 · Risk Category
may appear from intelligent agents such as HLI-based agents... Since HLI-based agents are going to mimic the behavior of humans, they may learn these behaviors accidentally from human-generated data. It should be noted that deception and cheating maybe appear in the behavior of every computer agent because the agent only focuses on optimizing some predefined objective functions, and the mentioned behavior may lead to optimizing the objective functions without any intention
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39.04.00 · Risk Category
The robustness of an AI-based model refers to the stability of the model performance after abnormal changes in the input data... The cause of this change may be a malicious attacker, environmental noise, or a crash of other components of an AI-based system... This problem may be challenging in HLI-based agents because weak robustness may have appeared in unreliable machine learning models, and hence an HLI with this drawback is error-prone in practice.
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39.12.00 · Risk Category
whether the decision of an AI-based agent can be predicted in every situation or not
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39.27.00 · Risk Category
Nowadays, we are faced with systems that utilize numerous learning models in their modules for their perception and decision-making processes... One aspect of an AI-based system that leads to increasing the complexity of the system is the parameter space that may result from multiplications of parameters of the internal parts of the system
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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.
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39.01.00 · Risk Category
There is a set of problems that cannot be formulated in a well-defined format for humans, and therefore there is uncertainty as to how we can organize HLI-based agents to face these problems
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39.09.00 · Risk Category
in this field, a set of tools and processes may be used to bring explainability to a learning model. With such capability, humans may trust the decisions made by the models
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39.13.00 · Risk Category
the accuracy of the learning model goes down because of changes in the data and environment of the model. Therefore, the learning process should be changed using new methods to support continual and lifelong learning
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39.14.00 · Risk Category
Memory is an important part of all AI-based systems. A limited memory AI-based system is one of the most widely and commonly used types of intelligent systems [83]. In this type, historical observations are used to predict some parameters about the trend of changes in data. In this approach, some data-driven and also statistical analyses are used to extract knowledge from data.
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39.15.00 · Risk Category
From semantic web techniques to linguistic analysis and natural language processing may be related to semantic computations in AI-based systems [87,88,89]. On the other hand, communication among intelligent agents leads to flowing information in a population of agents resulting in increasing knowledge and intelligence in that population... We know that defining or determining a shared ontology among intelligent entities in an AI-based system is possible because of maturing some parts of knowledge in ontology manipulations and defining some tools in semantic web techniques
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39.16.00 · Risk Category
Ethics are considered as the set of moral principles that guide a person’s behavior. From a perspective of morality issue, it is preserving the privacy of data within learning processes [93]. In this perspective, the engineers and social interactions of humans are the subjects of morality. From another perspective, implementing the concepts related to morality in a cognitive engine can be seen as a goal of AI designers. This is because we expect to see morality in an agent designated based on AGI and also HLI.
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39.17.00 · Risk Category
The concept of rational agency has long been considered as a critical role in defining intelligent agents. Rationality computation plays a key role in distributed machine learning, multi-agent systems, game theory, and also AGI... Unfortunately, a lack of required information prevents the creation of an agent with perfect rationality
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Theory of mind... constructing some algorithms and machines that can implement mind computations and also mental states
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AI models can be improved during the evolution of generations without human aid
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39.23.00 · Risk Category
A beneficial AI system is designated to behave in such a way that humans are satisfied with the results.
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39.24.00 · Risk Category
Exploration and exploitation decisions refer to trading off the benefits of exploring unknown opportunities to learn more about them, by exploiting known opportunities
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39.28.00 · Risk Category
trustworthiness in AI will feed societies, economies, and sustainable development to bring the ultimate benefits of AI to individuals, organizations, and societies.... From a social perspective, trustworthiness has a close relationship with ethics and morality
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.