An explainable AI tool for operational risks evaluation of AI systems for SMEs

Han, T.A., Pandit, D., Joneidy, S., Hasan, M.M., Hossain, Julius, Tania, M. Hoque, Hossain, M.A. and Nourmohammadi, N. (2024) An explainable AI tool for operational risks evaluation of AI systems for SMEs. Geok, Tan Kim, Latiff, Nurul Mu’azzah Abdul and Sheikh, Usman Ullah (eds.) In 2023 15th International Conference on Software, Knowledge, Information Management and Applications (SKIMA). IEEE. pp. 69-74 . (doi:10.1109/SKIMA59232.2023.10387301).

Abstract

With the surge in artificial intelligence (AI) adoption by Small and Medium-sized Enterprises (SMEs), ensuring their safety, fairness, and operational assurance has become paramount. Since many SMEs operate with limited resources, they face unique challenges in ethically and securely deploying AI systems. This research delves into the core principles of AI governance, risk management, and testing, specifically tailored for SMEs, emphasising making these concepts accessible and understandable. Through collaborative efforts, including interactive workshops, meetings and surveys with twenty SME participants, we identified vital AI application areas and challenges and conceptualised an evaluation tool leveraging explainable AI. This tool assesses AI-driven systems' robustness, potential biases, and other software and hardware vulnerabilities It also addresses ethical considerations and legal compliance, emphasising establishing trust and accountability with stakeholders as a foundation for successful AI integration. In conclusion, the paper presents a pilot study that conducts a risk analysis of prevalent AI applications, specifically AI-driven language models, for SMEs. This study illustrates how the proposed evaluation tool will integrate risk levels across different application domains.

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Contributors

Author: Julius Hossain

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