Identifying fake conclusions of forensic medical examinations using an artificial intelligence technology based on the experience in the Republic of Kazakhstan: a Review

Abstract

This review discusses the legal aspects on the use of mathematical statistics and machine learning (hereinafter referred to as artificial intelligence) in forensic activities to identify both expert errors and fake expert opinions.

An attempt has been made to establish the criteria for evaluating conclusions of forensic examinations by determining their relevance, admissibility, reliability, and objectivity, as well as the objective possibility of distinguishing expert errors from deliberately false and fake expert opinions.

A SWOT analysis on the use of artificial intelligence was carried out to solve the issue of its application in the field under consideration, which revealed its advantages, and disadvantages.

The use of mathematical statistics and machine learning methods is not a universal method to identify fakes in expert opinions. However, given that this method can give both false-positive and false-negative results, its outcomes should be verified by independent experts. In addition, to effectively prevent the facts of falsification, comprehensive measures should be taken, including not only the detection of manipulations but also the prevention of the possibility of their occurrence, as well as the punishment of the perpetrators. Thus, this review proposed several amendments and additions to the current legislation of the Republic of Kazakhstan.

Keywords

expert opinion / artificial intelligence / forgery / falsification / expert errors

References

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