MAB: Improving trust in data and algorithms in the medium of AI


Aditya Vasan Srinivasan, Mona de Boer

Video: Aditya Vasan Srinivasan about Artificial Intelligence and Internal Audit 

Artificial Intelligence (AI) has great potential to solve a wide spectrum of real-world business problems, but the lack of trust from the perspective of potential users, investors, and other stakeholders towards AI is preventing them from adoption. To build and strengthen trust in AI, technology creators should ensure that the data which is acquired, processed and being fed into the algorithm is accurate, reliable, consistent, relevant, bias-free, and complete. Similarly, the algorithm that is selected, trained, and tested should be explainable, interpretable, transparent, bias-free, reliable, and useful. Most importantly, the algorithm and its outcomes should be auditable and properly governed.

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The identification of essential trust factors of data and algorithms in the medium of AI, and the presentation of a trusted AI model incorporating such factors with detailed indicators, would be one of the prime contributions from this research. It would aid technology developers in assessing those trust factors upfront and thereby providing a seal of trust to potential users, stakeholders over the resulting AI solutions.

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