MIT AI Risk Repository · domain 6: Socioeconomic & Environmental
6.5 Governance failure
Inadequate regulatory frameworks and oversight mechanisms failing to keep pace with AI development, leading to ineffective governance and the inability to manage AI risks appropriately.
- 61
- 12
- 3
- 3
| Label | Value |
|---|---|
| Human | 31 |
| Other | 16 |
| AI | 11 |
| Not coded | 3 |
| Label | Value |
|---|---|
| Unintentional | 31 |
| Other | 25 |
| Not coded | 3 |
| Intentional | 2 |
| Label | Value |
|---|---|
| Pre-deployment | 23 |
| Other | 19 |
| Post-deployment | 16 |
| Not coded | 3 |
| Label | Value |
|---|---|
| 2022 | 1 |
| 2026 | 2 |
| Label | Value |
|---|---|
| Risk Category | 12 |
| Risk Sub-Category | 49 |
Risk entries
Browse and export all- Lack of training data transparency
"Without accurate documentation on how a model's data was collected, curated, and used to train a model, it might be harder to satisfactorily explain the behavior of the model with respect to the data...
- Uncertain data provenance
"Data provenance refers to tracing history of data, which includes its ownership, origin, and transformations. Without standardized and established methods for verifying where the data came from, ther...
- Legal accountability
"Determining who is responsible for an AI model is challenging without good documentation and governance processes."
- Lack of system transparency
"Insufficient documentation of the system that uses the model and the model’s purpose within the system in which it is used."
- Unrepresentative risk testing
"Testing is unrepresentative when the test inputs are mismatched with the inputs that are expected during deployment."
- Incomplete usage definition
"Since foundation models can be used for many purposes, a model’s intended use is important for defining the relevant risks of that model. As the use changes, the relevant risks might correspondingly...
- Lack of data transparency
"Lack of data transparency is due to insufficient documentation of training or tuning dataset details. "
- Incorrect risk testing
"A metric selected to measure or track a risk is incorrectly selected, incompletely measuring the risk, or measuring the wrong risk for the given context."
- Lack of testing diversity
"AI model risks are socio-technical, so their testing needs input from a broad set of disciplines and diverse testing practices."
- Erosion of international law and global governance architectures;
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- Inadequate management of AGI
"The capabilities of current risk management and legal processes in the context of the development of an AGI."
- AI jurisprudence
"When considering legal frameworks, we note that at present no such framework has been identified in literature which would apply blame and responsibility to an autonomous agent for its actions. (Thou...
- Liability and negligence
"Liability and negligence are legal gray areas in artificial intelligence. If you leave your children in the care of a robotic nanny, and it malfunctions, are you liable or is the manufacturer [45]? W...
- Regulations and policy challenges
"Given that generative AI, including ChatGPT, is still evolving, relevant regulations and policies are far from mature. With generative AI creating different forms of content, the copyright of these c...
- Governance
"Generative AI can create new risks as well as unintended consequences. Different entities such as corporations (Mäntymäki et al., 2022), universities, and governments (Taeihagh, 2021) are facing the...
- Lack of accountability and liability
"Determining responsibility when EAI causes harm requires new accountability and liability frameworks that address the complexities of highly autonomous physical systems. Human users may disagree with...
- Transformative effects
"EAI deployment could fundamentally reshape society, particularly if the speed of technological development outpaces society’s ability to adapt [103, 120]. For example, EAI systems could provide physi...
- Responsibility
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 cr...
- Compliance
"The potential for AI systems to violate laws, regulations, and ethical guidelines (including copyrights). Non-compliance can lead to legal penalties, reputation damage, and loss of trust.While other...
- Liability
"When it causes harm to others the losses caused by the harm will be sustained by the injured victims themselves and not by the manufacturers, operators or users of the system, as appropriate."
- Governance
"The complex and rapidly evolving nature of AI makes them inherently difficult to govern effectively, leading to systemic regulatory and oversight failures."
- Challenges in perceiving, measuring, and recognizing harm
"Harm from AI often manifests subtly or over the long term, making it difficult to identify, measure, and address effectively."
- Combination failures
"Harms could result from a combination of regulatory, management, and operational failures."
- Complex attribution and responsibility
"When multiple actors are involved in AI development and deployment, it becomes difficult to assign responsibility for harm, complicating accountability."
- Rapid development outpacing regulation
"The fast pace of AI development may outstrip regulatory and legal frameworks."