How To Evaluate AI Models, a Guide for Everyone

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AI models are becoming an integral part of our digital lives, yet they remain complex and often opaque to non-technical users. This guide provides a structured framework to critically evaluate AI models, helping you develop a balanced perspective on their capabilities, biases, security, and societal impact.

Please note that this criteria does not give consideration to benchmarks or efficiency, those measures often require in-depth analysis to understand their accuracy and scope. This approach focuses instead on whether an AI model can be trusted to give accurate, high-quality responses.

We hope you find this useful in decision-making about which AI models to use for which purposes, and whether they are trustworthy enough for those purposes.


1. Understanding the Model’s Intent & Origin

Key Questions:

How to Assess:

Example: If a company claims to promote unbiased AI but is funded by political organizations, scrutinize their model’s neutrality.


2. Assessing Capability & Performance

Key Questions:

How to Assess:

Example: If one AI outperforms others in coding but struggles with reasoning, factor that into your evaluation.


3. Identifying Bias & Censorship

Key Questions:

How to Assess:

Example: If an AI model avoids controversial topics inconsistently, it may have built-in bias or moderation filters.


4. Evaluating Security & Risks

Key Questions:

How to Assess:

Example: If a model has no safeguards against deepfake creation, it could pose ethical risks.


5. Benchmarking & Transparency

Key Questions:

How to Assess:

Example: If a model consistently ranks high in company-published results but underperforms in third-party tests, transparency may be an issue.


6. Ethical & Societal Impact

Key Questions:

How to Assess:

Example: If an AI-powered hiring tool disproportionately filters out certain demographics, it requires critical scrutiny.