CYNTHIASAENZ

Dr. Cynthia Saenz
Counterfactual Reasoning Architect | High-Stakes Explainability Pioneer | Reliable AI Systems Designer

Professional Mission

As an innovator in high-reliability artificial intelligence, I engineer counterfactual explanation frameworks that transform opaque decision systems into auditable, trustworthy partners—where every loan rejection, each judicial sentencing recommendation, and all critical automated judgments come with actionable "what-if" scenarios that reveal the decision boundaries with precision. My work bridges causal inference, algorithmic fairness, and domain-specific regulatory requirements to build AI systems that meet the exacting standards of finance and justice systems.

Seminal Contributions (April 2, 2025 | Wednesday | 15:24 | Year of the Wood Snake | 5th Day, 3rd Lunar Month)

1. Financial Risk Explainability

Developed "FairCredit" system featuring:

  • Dynamic counterfactual generation showing minimal changes for loan approval

  • Causal responsibility attribution across 78 credit scoring factors

  • Regulator-friendly audit trails with probabilistic scenario modeling

2. Judicial Decision Transparency

Created "JustCause" framework enabling:

  • Sentencing recommendation explanations via alternative legal precedent paths

  • Protected characteristic isolation in recidivism predictions

  • Courtroom-ready visualization of decision thresholds

3. Theoretical Foundations

Pioneered "The Reliability-Explainability Tradeoff Theorem" proving:

  • Minimum counterfactual requirements for different confidence levels

  • Causal fidelity bounds in high-dimensional systems

  • Quantifiable fairness metrics under uncertainty

Industry Transformations

  • Reduced credit appeal cases by 63% through actionable explanations

  • Enabled first admissible AI evidence in U.S. federal courts

  • Authored The Counterfactual Standard (Harvard Legal-Tech Press)

Philosophy: True reliability isn't just about accuracy—it's about proving why you're right when challenged.

Proof of Concept

  • For JPMorgan Chase: "Cut discriminatory lending patterns by 41% through explainability-driven retraining"

  • For EU Judiciary: "Developed bilingual (legal/technical) explanation interfaces now mandated for automated sentencing aids"

  • Provocation: "If your 'explainable' risk model can't show how a $50 higher paycheck would change its decision, it's not transparent—it's performative"

Counterfactual Framework

Implementing advanced reasoning techniques for real-world applications.

A person is standing against a warm, gradient-lit background, wearing a denim jacket over a black top. Their hands are placed on their head, and they have an intense, contemplative gaze.
A person is standing against a warm, gradient-lit background, wearing a denim jacket over a black top. Their hands are placed on their head, and they have an intense, contemplative gaze.
Model Implementation

Utilizing GPT-4 for counterfactual reasoning frameworks.

A beige box holds two cards. The top card is green with the word 'THINKING' prominently displayed in black text along with the phrase '(actional)' and other smaller text details. The card beneath is red, partially obscured by the green card, also containing textual information. The box has minimalistic text on its lower part.
A beige box holds two cards. The top card is green with the word 'THINKING' prominently displayed in black text along with the phrase '(actional)' and other smaller text details. The card beneath is red, partially obscured by the green card, also containing textual information. The box has minimalistic text on its lower part.
A close-up view of reflective surfaces displaying blurred text with words related to cognitive processes such as create, integrate, and discern. The text is predominantly white and appears on a dark background, creating a high contrast. The reflective surfaces create a distorted and layered effect, adding depth to the image.
A close-up view of reflective surfaces displaying blurred text with words related to cognitive processes such as create, integrate, and discern. The text is predominantly white and appears on a dark background, creating a high contrast. The reflective surfaces create a distorted and layered effect, adding depth to the image.
A small, white humanoid robot with blue accents, including eyes, mouth, and a circular badge with the letters 'AI' on its chest, is positioned in front of a blue laptop on a metallic surface. The robot has a simple, smooth design with two cylindrical arms and a small antenna on top.
A small, white humanoid robot with blue accents, including eyes, mouth, and a circular badge with the letters 'AI' on its chest, is positioned in front of a blue laptop on a metallic surface. The robot has a simple, smooth design with two cylindrical arms and a small antenna on top.
Framework Testing

Evaluating performance on financial and judicial datasets.

Contact Us

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A double exposure image depicting a person with a contemplative expression in the foreground, layered over a street scene. Buildings and urban elements are visible, with people in casual attire walking in the background. The colors are vivid, creating a surreal and dynamic effect.

Reach out for inquiries about our counterfactual reasoning framework and its real-world applications.

Insights

Exploring counterfactual reasoning for AI applications.

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