AI incident #250 ·
Dutch City Court Defended Home Value Generated by Black-Box Algorithm
What happened
A home value generated by a black-box algorithm was reportedly defended by the Castricum court, which was criticized by a legal specialist for setting a dangerous precedent for accepting black-box algorithms as long as their results appear reasonable.
Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.
News reports (1)
Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.
Who was involved
- Alleged deployer
- Castricum Municipality
- Alleged developer
- Castricum Municipality
- Alleged harmed party
- Unnamed Property Owner
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- AI system safety, failures, and limitations
- Risk subdomain
- 7.4 Lack of transparency or interpretability
- Causal entity
- Human
- Intent
- Unintentional
- Timing
- Post-deployment
- Harm level
- —
- Sectors
- —
- Countries
- —
Risk entries describing this failure mode
Entries from the MIT AI Risk Repository coded to subdomain 7.4.
- Intelligibility
"How can we build agent’s whose decisions we can understand? Con- nects explainable decisions (Berkeley) and informed oversight (MIRI)."
- Attributing the responsibility for AI's failures
"This section, constituting almost 8% of the articles, addresses the implications arising from AI acting and learning without direct human supervision, encompassing two main issues: a responsibility gap and AI's moral st...
- Model Evaluations (Interpretability/Explainability)
- Model outputs inconsistent with chain-of-thought reasoning
"Chain-of-thought reasoning is sometimes employed to get a better understanding of the model’s output, where it encourages transparent reasoning in text form. However, in some cases, this reasoning is not consistent with...
- Lack of understanding of in-context learning in language models
"In-context learning allows the model to learn a new task or improve its perfor- mance by providing examples in the prompt, without changing its weights [101]. Even though this technique is highly effective, its working...
- General Evaluations (Difficulty of identification and measurement of capabilities)
"The capabilities of general-purpose AI systems can be difficult to measure, compared to the capabilities of more limited and fixed-purpose AI systems. This is in part due to a broader distribution of potential risks, a...
- Lack of transparency and interpretability
"Today's Frontier AI is difficult to interpret and lacks transparency. Contextual understanding of the training data is not explicitly embedded within these models. They can fail to capture perspectives of underrepresent...
- Opacity (the black box problem)
"Opacity surrounding the technical, internal decision-making processes of generative AI models is popularly known as the “black box problem.”277 Generative AI models, most ubiquitously built on deep neural networks with...
Incidents in the same risk subdomain
- Uber Launched Opaque Algorithm That Changes Drivers' Payments in the US
- Israeli Tax Authority Reportedly Used an Opaque Automated System to Issue a Fine, Declining to Explain or Disclose the Underlying Calculation
- Uber Allegedly Wrongfully Accused Drivers of Fraud via Automated Systems
- Houston ISD's EVAAS Teacher-Evaluation System Reportedly Put Teachers' Jobs at Risk Through Unverifiable Scores