AI in accountancy isn’t fully autonomous, nor is it a fantasy. The middle path of augmenting, rather than replacing a human works best when the human understands what the AI is doing – which needs explainability.
Explainable AI (XAI) emphasizes the role of the algorithm not just for providing an output, but for also sharing with the user, supporting information on how it reached that conclusion. XAI approaches aim to shine a light on the algorithm’s inner workings and/or to reveal some insight on what factors influenced its output. Furthermore, the idea is for this information to be available in a human-readable way, rather than being hidden within code.
The purpose of this report is to address explainability from the perspective of accountancy and finance practitioners. Explainability can improve the ability to assess vendor solutions in the market, enhance value capture from AI that’s already in use, boost ROI from AI investments; and augment audit and assurance capabilities, where data is managed using AI tools. The report also includes some examples for market practitioners to illustrate current developments.