AI Trust & Evaluation

How can you establish that an AI system is suitable, reliable, and appropriately controlled for its intended purpose?

Trust depends on the task, operating conditions, potential consequences, and evidence from appropriate testing.

This decision tree examines system quality, reliability, risk, and oversight. It complements the technical methods in Evaluation and AI Verification and Validation.

Design Experiments and Select Benchmarks

What evidence would show that an AI approach works for my intended use?

Start with AI Experiments & Benchmarks Navigator, or choose a specific branch:

Start: What Is Your Primary Concern?

Concern	Continue
Is the system accurate enough?	A. Quality and Accuracy
Will it work reliably in unfamiliar situations?	B. Robustness and Generalization
Could it produce harmful or unfair outcomes?	C. Harm, Fairness, and Human Oversight
Could data or systems be compromised?	D. Security and Privacy
Can its behavior be understood and accounted for?	E. Explainability and Governance
How can we maintain trust after deployment?	F. Continuous Monitoring

First Check: What Happens If AI Is Wrong?

Question: What is the consequence of an incorrect or unintended result?

Minor inconvenience: Establish proportionate quality checks.
Significant financial or operational impact: Require stronger testing, monitoring, and human accountability.
Potential harm involving health, safety, rights, or major legal consequences: Require a rigorous domain-specific safety and governance assessment before deployment.

Continue: Risk, Compliance and Regulation → AI Governance

The risk level influences the amount and type of evidence needed throughout the remaining branches.

A. Quality and Accuracy

Question: What does a correct result mean for this application?

Predicting numerical values: Regression → Evaluation - Measures
Classifying items or decisions: Classification → Evaluation - Measures
Generating factual answers: AI Verification and Validation → Retrieval-Augmented Generation (RAG)
Producing creative output: Generative AI → Evaluation
Completing multi-step tasks: Agents/Assistants → Evaluation

Decision: Is there a reliable way to compare results with expected outcomes?

Yes: Construct representative test cases and establish measurable acceptance criteria.
Partially: Combine measurable checks with structured human evaluation.
No: Define operational requirements and an appropriate review process before depending on the system.

Checkpoint: Can you explain what counts as success, failure, and an unacceptable result?

B. Robustness and Generalization

Question: When does the system perform poorly?

On unfamiliar inputs: Out-of-Distribution (OOD) Generalization
On new data despite good training results: Overfitting Challenge → Cross-Validation
On particular subgroups or data ranges: Bias and Variances → Datasets
When information is missing or degraded: Data Quality → Data Preprocessing
When conditions change over time: Online Learning → Model Monitoring

Decision: Can these failures be detected before they cause unacceptable consequences?

Yes: Add detection, fallbacks, and monitoring.
No: Restrict the intended use, add human review, or redesign the system.

Continue: Plan ongoing monitoring →

C. Harm, Fairness, and Human Oversight

Question: Who could be affected by an incorrect or biased result?

Individuals receiving consequential decisions: Ethics → AI Governance
Workers, customers, or communities: Social Science → Bias and Variances
Patients or people in safety-sensitive environments: Healthcare → AI Verification and Validation
Users exposed to manipulative or deceptive content: Fake → Ethics
People affected by autonomous actions: Agents/Assistants → Human-in-the-Loop (HITL) Learning

Decision: Does the system need human approval, supervision, or an appeal mechanism?

Yes: Define authority, escalation, review criteria, and accountability.
No: Document why the remaining risk is acceptable for the intended use.
Uncertain: Conduct an impact assessment before deployment.

Checkpoint: Can affected users and system operators understand how decisions are reviewed or corrected?

D. Security and Privacy

Question: What could be exposed or misused?

Sensitive or confidential information: Privacy → Data Governance
AI responses manipulated by hostile instructions: Prompt Injection Attack
Adversarial inputs designed to cause incorrect behavior: Offense - Adversarial Threats/Attacks
Tools or systems accessed without authorization: Cybersecurity → Cybersecurity Frameworks, Architectures & Roadmaps
External AI services and data transfers: AI Governance → Risk, Compliance and Regulation

Decision: Can the system safely handle malicious or unexpected inputs?

Yes, with demonstrated controls: Continue penetration, misuse, and resilience testing appropriate to the application.
Not yet: Restrict capabilities or deployment until safeguards are verified.

Continue: Monitor operational security →

E. Explainability and Governance

Question: What must be understood or documented?

Which factors influenced predictions: Explainable / Interpretable AI → Feature Exploration/Learning
Why a model behaves unexpectedly: Representation Learning → Explainable / Interpretable AI
Whether a conclusion is causal: Causation vs. Correlation
Who approved an AI system and its intended uses: AI Governance
Which standards, rules, or obligations apply: Risk, Compliance and Regulation → Law
Whether requirements and tests have been satisfied: AI Verification and Validation → Checklists

Decision: Is the available explanation adequate for the intended decision and audience?

Yes: Record evidence and known limitations.
No: Improve interpretability, documentation, or human review.
Cannot be made adequate: Reconsider the model or intended use.

Key distinction: A fluent explanation generated by an AI system isn't necessarily a faithful account of its internal computations.

F. Continuous Monitoring

Question: What must remain true after deployment?

Performance remains acceptable: Benchmarks → Evaluation - Measures
Data remains representative: Data Quality → Datasets
Security controls remain effective: Cybersecurity
Model or workflow behavior hasn't drifted: Model Monitoring
Changes are appropriately tested: Train, Validate, and Test
Failures are investigated and corrected: AI Verification and Validation

Decision: What happens when a critical threshold is exceeded?

Define an appropriate response:

Detect the problem.
Limit or suspend affected functionality when necessary.
Alert the responsible operator.
Investigate the cause.
Correct and retest the system.
Resume normal operation only when acceptance criteria are met.

Final Decision: Deploy, Restrict, or Redesign?

Evidence	Decision
Requirements are met and residual risks are acceptable.	Proceed with appropriate controls and monitoring.
Requirements are partly met but use can be safely constrained.	Limit the application and add safeguards.
Critical risks or evaluation gaps remain.	Do not deploy for the intended use until resolved.
The AI system offers no demonstrated advantage over a simpler method.	Reconsider the need for AI.

Related dimensions: AI Solver · AI Builder · AI Technology Selector · AI Strategy & Adoption

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