A novel framework for trustworthy and transparent AI expert systems

In the burgeoning realm of Artificial Intelligence (AI), the integrity of Expert Systems remains paramount. This research introduces a novel framework geared towards bolstering the trustworthiness and transparency of AI Expert Systems. Anchored in the dual imperatives of ethical considerations and functional efficiency, the study’s primary objective was to devise a robust mechanism that demystifies the decision-making processes within these systems. Our methodology melded a rigorous review of existing systems with iterative development and testing of our proposed framework. Findings indicate that our model not only enhances the interpretability of AI Expert Systems but also bolsters user trust, bridging the gap between complex computations and end-users. The implications are profound, offering the potential for widespread adoption in diverse sectors, ensuring AI decisions are both understandable and reliable. Objective The primary objective was multifaceted. Firstly, the author sought to address the opacity often inherent in AI Expert Systems, making their decision-making processes more comprehensible to end-users. Concurrently, the author aimed to reinforce the trustworthiness of these systems, ensuring their decisions not only made sense to users but were also rooted in robust and ethical computational practices. Methodology Authors’ approach was twofold. He began with a comprehensive review of existing systems, assessing their transparency levels, trust metrics, and any associated challenges. This review provided insights into prevalent gaps and set the stage for the development phase. Drawing from this analysis, the author embarked on crafting the framework, ensuring it was anchored in principles of ethical AI and user-centric design. To validate the model, the author conducted a series of controlled experiments, comparing the system’s outputs with traditional Expert Systems in a variety of simulated real-world scenarios. Main Findings The results were illuminating. The novel framework consistently outperformed traditional models in terms of transparency. Users, ranging from AI experts to laypersons, reported a significantly better understanding of decision-making processes when interacting with the system. Moreover, trust metrics, evaluated through user surveys and objective criteria like error rates and consistency, indicated a marked improvement in trustworthiness. Notably, the system demonstrated an adeptness at offering clear, concise explanations