This is a research prototype. The data and analyses are preliminary and not yet validated — we'd welcome your .
An independent academic report (the Corner report) commissioned by the Australian Department of Home Affairs found that the VERA2R and Radar risk assessment tools lacked empirical validity and reliability. The tools were used to justify post-sentence detention for offenders, despite the report finding that autism was incorrectly included as a risk factor and that the tools were 'extremely poor' at predicting risk. The government continued to use these tools for years after receiving the report without disclosing its findings to legal stakeholders.
An independent report found that the Vera-2R tool, designed to predict the risk of future terrorist activities, considered autism as a risk factor despite lacking empirical evidence to support this claim. The report called into question the tool's overall validity and reliability, stating it was "extremely poor" at accurately predicting risk. The inclusion of autism as a risk factor had potentially serious implications for the tool's use and credibility.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
Accuracy and effectiveness of AI decisions and actions are dependent on group membership, where decisions in AI system design and biased training data lead to unequal outcomes, reduced benefits, increased effort, and alienation of users.
Also classified as
Governments continued to rely on the tool to justify ongoing detention despite knowing it lacked empirical validity.
The independent report found the tools had a weak empirical basis and were extremely poor at predicting risk overall, representing a major capability failure.
The VERA2R tool performed unequally across demographic groups by systematically overestimating the risk of offending for individuals with autism spectrum disorders without any empirical basis.
AI system
Due to a decision or action made by an AI system
Unintentional
Due to an unexpected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed
The risk stems from the VERA2R and Radar algorithmic tools producing unreliable and biased risk assessments post-deployment, which was an unintentional failure of the system's predictive capabilities.
Added categories peak at 3: Substantial (Epistemic Harm, Inferred)
The Australian government's multi-year use of the unvalidated VERA2R and Radar tools to justify post-sentence detention compromised counter-terrorism decision-making and infringed on civil liberties, particularly for autistic individuals. While presenting minor national security risks, it highlights significant governance and human rights concerns in state-deployed AI.
The VERA2R and Radar systems are described as risk assessment instruments or tools used by practitioners to predict risk based on specific input factors. They do not act as autonomous agents or interactive assistants, but rather as static diagnostic tools.
High Risk: The system is used in law enforcement and justice contexts to assess recidivism and justify ongoing detention. This directly aligns with the EU AI Act's classification of high-risk AI systems used in law enforcement and administration of justice.
The VERA2R and Radar tools were designed to predict the risk of future terrorist offending and radicalisation.
The report provides clear, detailed information about the academic review of the VERA2R and Radar tools, their specific flaws (such as the unscientific inclusion of autism), and how they were used by the Australian government. The facts are well-documented through freedom of information releases.
None
Technology
Flawed Predictive Algorithm
The tool relied on unverified risk factors to predict recidivism.
Poor Prediction Accuracy
The instrument was extremely poor at predicting actual risk.
Data Inputs
Unscientific Risk Factors
Autism and non-compliance were included without empirical evidence.
Non-Empirical Cited Evidence
Almost 60 percent of the cited evidence base was not empirical.
Inaccurate Citations
Less than half of cited works accurately reflected source texts.
Human Factors
Practitioner Over-reliance
Practitioners relied on the tool despite its weak evidence base.
Developer Overclaim
Developers made unsupported claims of strong reliability.
Process and Methods
Lack of Empirical Evaluation
The tool was deployed without thorough empirical evaluation.
Flawed Factor Development
The process for developing risk factors lacked scientific rigor.
Management
Nondisclosure of Critical Report
The government withheld the critical report from lawyers and experts.
Continued Use Despite Warnings
Governments continued using the tool after receiving the report.
Lack of Transparency
The government hid the report under national security claims.
Regulatory Environment
Lack of Independent Oversight
No regulatory body evaluated the tool before its adoption.
AI system involved
Identified by automated extraction; can differ from the alleged developers above.
