Explainable AI Update : Self-Reasoning AI
Ten months ago Jean-Michel Cambot and myself crafted a Lytn blog(see below) on Explainable AI (XAI) and the options available to gain human trust.
We noted at the time that the computational complexity of GenAI engines meant explanation was limited. Solutions like Counterfactual Explanation, involved the AI engine presenting alternative outcomes under slightly modified conditions. By showing what could have happened if certain input factors were different, it can provide valuable context for understanding the AI's decision process. It tried to build trust.
But time waits for no man and now we have Self-Reasoning AI, designed to critically evaluate its own responses and use self-assessment to make iterative improvements.
Whilst the success of any AI engine primarily revolves around the veracity of the data used, ?end user trust comes with accuracy and explanation, so what are the key benefits of Self-Reasoning AI?
First, if we increase the reliability through self-audit it enhances the trust between engine and human by explaining why a certain response is more accurate or reliable after self-assessment.
Second, it adapts to new tasks by applying learned principles to novel scenarios, providing insights into its decision-making logic, hence making it more understandable. Third, by continuous learning and improvement based on self-reflection it ensures that models stay relevant and efficient. This improvement can be documented and shared, offering a clear trail of how decisions evolve over time and why they were made.
Finally, as AI systems become more complex, traditional oversight becomes impractical. Self-reasoning models can provide an internal form of oversight, checking their own processes and decisions. This internal oversight mechanism can be shared with external observers, providing a scalable way to understand and verify AI behaviour.
领英推荐
Before Self-Reasoning an AI engine would present a human with its results. The issue, especially when it differed from the human was explaining that result.
By implementing Self-Reasoning not only can the engine critique and improve over time but produce a human readable explanation of its logic and actions to reach its conclusion.
?