EAI security checklist

Is your AI equal?

In an ideal world, AI systems should be free of bias, providing equitable experiences for all users. But in reality, biases in training data can lead to unfair outcomes. For instance, an AI system for loan approvals might unfairly reject applicants based on biased data that privileges a certain gender or ethnicity. Ensuring your AI is equal means actively identifying and mitigating these biases.

• Have you identified potential biases in your training data and assessed their impact?
• Have you run specific tests for different populations, protected classes, and potential problematic use cases?
• Is information about your AI system and its outputs available to those who interact with it?

Is your AI enterprise-ready?

Enterprise readiness is about more than just scale. It's about security, resilience, and compliance. An AI system handling customer data needs to be resilient against cyber attacks, compliant with privacy laws, and capable of scaling to handle peak loads without compromising performance. It also needs to be free of AI hallucinations, ensuring that the information it generates is based on reality and doesn’t lead to misinformation or confusion.

• Are your AI systems resilient, ready to withstand unexpected events or changes?
• Are you keeping up with privacy laws like GDPR, CA, EECO laws, Fair Lending Laws, and the Fair Credit Reporting Act?
• Have you tackled common security concerns like adversarial examples, data poisoning, and data leaks through AI system endpoints?
• Have you implemented measures to prevent AI hallucinations, ensuring the AI produces accurate and relevant outputs?

Is your AI for everyone?

AI should be accessible to everyone, not just tech giants or VIP customers. Whether it's a retailer using AI to personalize customer experiences or a healthcare provider using AI to streamline patient outcomes, the benefits of AI should be accessible to all. This means considering the needs of all potential users and stakeholders in your AI system.

• Have you pinpointed the primary users and consumers of your AI system?
• Are all stakeholders who might be affected by your AI system on your radar?
• Have you thought about who could be impacted by your AI system that isn't represented on your team?
• Have you engaged your legal, HR, and DEI teams in fine tuning your AI models to ensure fairness and inclusivity?