Develop Explainable AI (XAI) Solutions For Data Engineers

Abstract

Investigating the lack of XAI solutions tailored for data engineers' guidance provides methods and better interpretability of AI models. With a growing dependence on AI systems making high-stakes decisions, there is a great demand for efficient and interpretable models. This paper discusses several XAI methods, both the model-agnostic and the model-specific ones, and provides real-life use cases and simulated output to prove the efficiency of the approaches. In this regard, the paper aims to help data engineers enhance both the efficiency and reliability of the AI models leveraging these techniques. Some areas of concern include the extent to which model interpretability can be maximized while maximizing the model's predictive accuracy and how these tools can fit into the overall data analysis pipeline. The importance of the graphical representation and user experience thinking elements is also emphasized to make the XAI tools more understandable and usable. In summary, the paper offers practical advice on approaching the design and adoption of XAI solutions, thus emphasizing explainability's central role in building trust, improving decision-making, and broadening the practical applications of AI.

Published Date: 2 March 2021