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Incident 137: Israeli Tax Authority Employed Opaque Algorithm to Impose Fines, Reportedly Refusing to Provide an Explanation for Amount Calculation to a Farmer

Description: An Israeli farmer was imposed a computer generated fine by the tax authority, who allegedly were not able to explain its calculation, and refused to disclose the program and its source code.

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Alleged: Israeli Tax Authority developed and deployed an AI system, which harmed Moshe Har Shemesh and Israeli people having tax fines.

Incident Stats

Incident ID
137
Report Count
1
Incident Date
2021-01-11
Editors
Sean McGregor, Khoa Lam
Applied Taxonomies
GMF, CSETv1, MIT

CSETv1 Taxonomy Classifications

Taxonomy Details

Incident Number

The number of the incident in the AI Incident Database.
 

137

GMF Taxonomy Classifications

Taxonomy Details

Known AI Goal Snippets

One or more snippets that justify the classification.
 

(Snippet Text: The story began in 2014 when the farm Har Shemesh, which is not part of any community, asked the Tax Authority to explain how it had calculated a fine they were required to pay., Related Classifications: Financial Processing)

Known AI Goal Classification Discussion

Free text with comments justifying the chosen classification (e.g. based on information on selected snippets and technical analysis), if needed.
 

Financial Processing: The system probably does not use AI at all.

MIT Taxonomy Classifications

Machine-Classified
Taxonomy Details

Risk Subdomain

A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
 

7.4. Lack of transparency or interpretability

Risk Domain

The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
 
  1. AI system safety, failures, and limitations

Entity

Which, if any, entity is presented as the main cause of the risk
 

AI

Timing

The stage in the AI lifecycle at which the risk is presented as occurring
 

Post-deployment

Intent

Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
 

Intentional

Incident Reports

Reports Timeline

+1
When an Israeli Farmer Declared War on an Algorithm
When an Israeli Farmer Declared War on an Algorithm

When an Israeli Farmer Declared War on an Algorithm

haaretz.com

When an Israeli Farmer Declared War on an Algorithm
haaretz.com · 2021

A tax dispute between a ranch in Israel’s southern Negev desert and the country’s tax authority has given rise to an issue which could have far reaching repercussions on the public’s ability to oversee and even understand government acts.

T…

Variants

A "variant" is an incident that shares the same causative factors, produces similar harms, and involves the same intelligent systems as a known AI incident. Rather than index variants as entirely separate incidents, we list variations of incidents under the first similar incident submitted to the database. Unlike other submission types to the incident database, variants are not required to have reporting in evidence external to the Incident Database. Learn more from the research paper.

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