How do you secure your explainable AI systems?

Your data is the power source for AI and deserves top-notch protection. Tools like encryption and authentication are your safety nets, with access control as your guiding compass. Auditing mechanisms constantly oversee everything, ensuring the smooth operation. Privacy and confidentiality are the unsung champions here, providing a secure layer over your data. They use techniques like anonymization and synthetic data to preserve integrity. In this dynamic world of data and AI, your valued information stays safe and ready to fuel innovation. So, relax and watch as your data securely powers the magic of AI.

Protecting the data used by the AI system is crucial. This includes implementing strong access controls, encryption techniques, and secure storage mechanisms. Employing techniques such as differential privacy can help ensure individual data privacy while maintaining the utility of the data for analysis.

In my experience, its just not about explaining the model, but also what went in, why did you choose a particular set of data, what will be its impact, does it covers edge cases and is diverse, what does the model, rationale behind choice of model, choice of KPIs, error analysis, how will the error of model impact an individual, prediction quantile analysis and lastly counterfactuals

- Not all AI techniques are equal, the "black box" feature is more prevalent when using sophisticated deep learning and LLMs models compared to modest supervised learning. - The bigger and more complex the model is, the more difficult it becomes to explain. - Some heavily regulated sectors such as financial services, require companies to explain the outcomes of their models. - Under some regulations such as GDPR EU citizen have the right to demand companies to explain how their algorithms derived outcomes if they have been impacted by the decision. - Techniques to explain AI models outcomes such as: counterfactual method, rationalisation, layer wise relevance propagation - Dividing big models into a set of smaller models helps.

Monitoring and evaluating XAI performance necessitates a comprehensive strategy. Accuracy, precision, and recall are core metrics for assessing predictions. Assessments also hinge on data quality, model stability, and latency. For XAI, explainability, credibility, and consistency are key. Tools like LIME and SHAP provide interpretable explanations. Crucially, human verification and user feedback are invaluable, shaping a more effective AI. Automated monitoring via APIs, logs, dashboards complements these, ensuring robust XAI evaluation.

Securing explainable AI systems is a challenging task that calls for creative problem-solving. We have a great opportunity as content creators to rewrite the story surrounding these technologies, emphasizing the value of security while keeping them understandable. We can unleash the full potential of AI while assuring a secure and reliable digital future by striking the appropriate balance between accessibility and protection.

It is important to take the time to work on what "Explainability" actually mean within your AI use case. For example, if you are using Machine Learning to predict home prices, you can add Confidence Intervals to the output and instead of output displaying a simple $150K, you may want to show $150,000 +/- $5000 or $150,000 +/- $100,000. It is a way for humans to judge how much confidence does the AI/ML have in that output. For LLM, the same Confidence Intervals can be applied based on Hallucination Rates. How much confidence does AI have in its text-based response from an LLM that a given response is truthful? Every response should include a CI (of sorts) to allow humans to better judge if the response should be trusted or not.

I have been working on securing AI for some time and one thing is clear - Cybersecurity is not enough to secure AI systems. Why? CIA is the triad Cybersecurity sits on, Confidentiality, Integrity and Availability. Are these enough o secure AI? No.