About Us

Decision Accuracy company was founded as an avenue to commercialize Decision Theory and AI advances made by Jamil Kazoun. Some of the theoretical results were previously published in books for both academic and general audiences. 

These works introduced original applications never before practiced, such as in measuring a vote’s accuracy and error, grounding the evaluation of a vote by a jury, a congress, or a parliament in pure mathematical precision devoid of politics.

Much of the work in “performance and decision evaluation” of a machine, a person, or a group is proprietary, and Google’s own assessment tools showed our metrics as THE BEST in many central and practical industrial and general domains, adding that the improvements from our metrics are not small or medium, but “a big improvement” over currently used metrics. Other top companies gave similar evaluation results of our products. What more could a start-up company want coming out of the gate? So we are hoping for big things to come.

Application to AI Governance and Model Evaluation

Artificial intelligence systems are, at their core, decision systems. Whether classifying, predicting, recommending, approving, denying, ranking, or flagging, AI models continuously make YES/NO or selection decisions that carry real-world consequences. As AI systems are deployed at scale, the quality of these decisions becomes a governance issue, not merely a technical one.

The current problem in AI evaluation

Most AI models are evaluated using metrics derived from the truncated accuracy definition: success frequency, precision, recall, or variants thereof. While useful for optimization, these metrics share a common limitation: they underweight or obscure error as a negative contributor to decision quality.

As a result:

• models with high reported “accuracy” may still produce unacceptable harm,
  
  

• tradeoffs between false positives and false negatives are handled heuristically,
  
  

• and governance bodies struggle to translate technical metrics into risk, liability, or policy terms.
  
  

This gap between technical performance metrics and decision accountability is one of the central challenges in AI governance today.

What the Accuracy Equation adds

The Accuracy Equation provides a unifying evaluative framework that treats AI decisions the same way organizations already treat financial outcomes: as net performance under error and uncertainty.

By assigning equal and opposite weight to correct and incorrect outcomes, the equation:

• makes error explicit rather than incidental,
  
  

• allows comparison across models, thresholds, and deployment contexts,
  
  

• and supports direct translation from model behavior to decision impact.
  
  

This is particularly valuable in high-stakes AI systems, where error is not symmetrical and cannot be dismissed as noise.

Governance-level benefits

For boards, risk committees, and compliance teams, the framework offers several immediate advantages:

1. Clear accountability language
   Decision quality can be reported as net correctness relative to an explicit target, rather than as a collection of partially interpretable metrics.
   
   

2. Comparable evaluation across systems
   Human and machine decision-makers can be evaluated under the same framework, simplifying governance and audit processes.
   
   

3. Explicit error-cost discussion
   The framework encourages organizations to quantify not just how often a model is right, but how costly it is when it is wrong—and to whom.
   
   

4. Policy-aligned evaluation
   Regulatory concepts such as fairness, proportionality, and harm minimization map naturally to target selection and error weighting within the Accuracy framework, without requiring new metrics for each concern.
   
   

Reliability and uncertainty are already integrated

A frequent concern in AI evaluation is reliability: whether model performance is stable under repeated sampling, distribution shift, or limited data. In the full Accuracy Equation, uncertainty and repeatability are not afterthoughts. They are incorporated through statistical terms that account for variability and sample size.

This means AI systems can be evaluated not only on point estimates of performance, but on confidence-adjusted correctness, which is essential for responsible deployment in uncertain environments.

Why this matters now

AI governance is moving rapidly from voluntary guidelines to formal accountability. Organizations are being asked not only what their models do, but why those decisions should be trusted. Trust, in this context, cannot rest on opaque metrics or expert assurances alone. It requires measurable correctness, bounded error, and transparent cost.

The Accuracy Equation provides a general foundation for this requirement. It does not replace domain-specific metrics; it organizes them around a single evaluative core that governance bodies can understand, audit, and defend.

Summary for AI leadership

AI systems will increasingly be judged not by how impressive they appear, but by how well their decisions withstand scrutiny under error and consequence. A governance framework grounded in net decision accuracy allows organizations to deploy AI systems with greater confidence, lower risk, and clearer accountability—without constraining innovation.

With this in mind, the company is tiny, and we hope our efforts to educate others about our products will earn your attention.

Jamil Kazoun

President