Evaluating explainable artificial intelligence (XAI): algorithmic explanations for transparency and trustworthiness of ML algorithms and AI systems

Utsab Khakurel; Danda B. Rawat

doi:10.1117/12.2620598

6 June 2022 Evaluating explainable artificial intelligence (XAI): algorithmic explanations for transparency and trustworthiness of ML algorithms and AI systems

Utsab Khakurel, Danda B. Rawat

Author Affiliations +

Proceedings Volume 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV; 121130A (2022) https://doi.org/10.1117/12.2620598
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Explainable Artificial Intelligence (XAI) is the capability of explaining the reasoning behind the choices made by the machine learning (ML) algorithm which can help understand and maintain the transparency of the decision-making capability of the ML algorithm. Humans make thousands of decisions every day in their lives. Every decision an individual makes, they can explain the reasons behind why they made the choices that they made. Nonetheless, it is not the same in the case of ML and AI systems. Furthermore, XAI was not wideley researched until suddenly the topic was brought forward and has been one of the most relevant topics in AI for trustworthy and transparent outcomes. XAI tries to provide maximum transparency to a ML algorithm by answering questions about how models effectively came up with the output. ML models with XAI will have the ability to explain the rationale behind the results, understand the weaknesses and strengths the learning models, and be able to see how the models will behave in the future. In this paper, we investigate XAI for algorithmic trustworthiness and transparency. We evaluate XAI using some example use cases and by using SHAP (SHapley Additive exPlanations) library and visualizing the effect of features individually and cumulatively in the prediction process.

Conference Presentation

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Utsab Khakurel and Danda B. Rawat "Evaluating explainable artificial intelligence (XAI): algorithmic explanations for transparency and trustworthiness of ML algorithms and AI systems", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 121130A (6 June 2022); https://doi.org/10.1117/12.2620598

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