MIT AI Risk Repository · Risk Category · 19.06.00
Legal AI Risks
On this page 5 sections
Description
"Legal and regulatory risks comprise in particular the unclear definition of responsibilities and accountability in case of AI failures and autonomous decisions with negative impacts (Reed, 2018; Scherer, 2016). Another great risk in this context refers to overlooking the scope of AI governance and missing out on important governance aspects, resulting in negative consequences (Gasser & Almeida, 2017; Thierer et al., 2017)."
From Governance of artificial intelligence: A risk and guideline-based integrative framework (Wirtz2022), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Subdomain
- 6.5 Governance failure
- Causal entity
- Other
- Intent
- Unintentional
- Timing
- Other
Subdomain definition: 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.
Real-world incidents in this subdomain
- Nippon Life Alleged ChatGPT Practiced Law Without a License in Illinois Disability Case
- OpenAI Allegedly Did Not Alert RCMP After ChatGPT Flagged Violent Chats Before British Columbia School Shooting
- Remotely Operated Taser-Armed Drones Proposed by Taser Manufacturer as Defense for School Shootings in the US
How other frameworks describe this risk
- Liability issues in case of accidents
- Mobility
- Faster scientific progress makes it harder for governance to keep pace with development
- Type 1: Diffusion of responsibility
- Products Liability Law
- Difficult to develop metrics for evaluating benefits or harms caused by AI assistants
- Institutional responsibilities
- General Evaluations (Biased evaluations of encoded human values)
Other entries from Wirtz2022
- Technological, Data and Analytical AI Risks
- Loss of control of autonomous systems and unforeseen behaviour due to lack of transparency and self-programming/ reprogramming
- Programming error
- Lack of data, poor data quality, and biases in training data
- Vulnerability of AI systems to attacks and misuse
- Lack of AI experts with comprehensive AI knowledge
- Immaturity of AI technology can cause incorrect decisions
- High investment costs of AI hinder integration
- Informational and Communicational AI Risks
- Manipulation and control of information provision (e.g., personalised adds, filtered news)
- Disinformation and computational propaganda
- Censorship of opinions expressed in the Internet restricts freedom of expression