Indian Journal of Artificial Intelligence and Law
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1172). Volokh cites an example of how an AI judge may end up relying on irrelevant facts, just because it uses particular words in a fact pattern that are somehow correlated to successful litigation outcomes (Volokh, 2019). Since it is difficult for AI to identify bias in its data sets, it is important that AI systems can be reviewed and audited by humans (Satell, et al., 2019) (Marshall, et al., 2018 p. 106). This will facilitate the process of debugging and fine-tuning glitches found within the AI model. As such, the AI system should include an in-built forensics and security logging function. Firstly, it enables human experts to trace specific events, which are recorded as non-repudiable evidence in the algorithm metadata (Marshall, et al., 2018 p. 106). Also, it allows for a close examination into the state of specific classifiers which contributed to an inaccurate decision (Marshall, et al., 2018). Beyond the detection of glitches, the algorithm can also spot existing hacks and hidden backdoors within the AI system (Volokh, 2019 p. 1175) (in relation to Part IV(d)). After the faulty nodes within the AI system are detected and isolated, the AI system can then be fine-tuned accordingly through targeted updates. (f) Development of AI system by the statutory agencies On a final note, the development, testing and maintenance of the AI judge should be spearheaded by a public agency like GovTech Singapore. The main concern of relying on a private company stems from their possible conflicts of interest. There are strong concerns that a company may create a backdoor within the AI system, and seek to sell such an access for profit (Volokh, 2019 p. 1172). Furthermore, if the private company is involved in high-stakes litigation, it is feared that