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:
- Who developed the model? What are their incentives?
- Is the model open-source or proprietary?
- What problems is it designed to solve?
How to Assess:
- Check the developer’s website, mission statements, or funding sources.
- Look for open-source repositories on GitHub or AI research papers.
- Compare stated objectives with real-world applications.
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:
- Does the model perform as expected in real-world use cases?
- How does it compare to similar AI models?
- Are performance benchmarks reliable and verifiable?
How to Assess:
- Test the model yourself using diverse prompts.
- Look for third-party performance reviews.
- Compare with similar AI models using benchmarking tools.
Example: If one AI outperforms others in coding but struggles with reasoning, factor that into your evaluation.
3. Identifying Bias & Censorship
Key Questions:
- Does the model show political, cultural, or ideological bias?
- Are its answers consistent across different prompts?
- Can it be manipulated to favor certain viewpoints?
How to Assess:
- Ask politically neutral and charged questions to check consistency.
- Compare results with responses from multiple AI models.
- Analyze if model outputs change based on phrasing or source material.
Example: If an AI model avoids controversial topics inconsistently, it may have built-in bias or moderation filters.
4. Evaluating Security & Risks
Key Questions:
- Can the model be easily jailbroken or exploited?
- What are the risks of misuse (e.g., fraud, misinformation)?
- How transparent are the developers about vulnerabilities?
How to Assess:
- Research known vulnerabilities or past security breaches.
- Try known jailbreak methods or adversarial prompts.
- Read technical documentation and developer disclosures.
Example: If a model has no safeguards against deepfake creation, it could pose ethical risks.
5. Benchmarking & Transparency
Key Questions:
- Are performance metrics publicly available and replicable?
- Has the model been independently tested or audited?
- Do researchers highlight flaws, or is information suppressed?
How to Assess:
- Look for independent AI audits or third-party reviews.
- Check if research papers disclose both strengths and weaknesses.
- Assess if the company updates benchmarks after criticism.
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:
- Who benefits from this model? Who might be harmed?
- Does it reinforce existing biases or inequalities?
- Are there clear mechanisms for oversight and accountability?
How to Assess:
- Review ethical AI guidelines set by organizations like the IEEE or AI Now Institute.
- Consider societal impacts, especially on marginalized communities.
- Investigate whether the company has policies for addressing harmful outputs.
Example: If an AI-powered hiring tool disproportionately filters out certain demographics, it requires critical scrutiny.