A Bayesian Artificial Intelligence Model for Legal Fact-Finding
Accurate fact-finding is the premise and foundation of judicial fairness and justice.In judicial practice,the principle of discretionary evaluation of evidence is the proof system for fact-finding.However,discretionary evaluation of evidence faces a triple dilemma:difficulty in clarifying evidence,implicit subjective judgment,and fuzzy inner conviction,which hinder the accurate determination of the truth.The Bayesian Artificial Intelligence(AI)model,as a visual,transparent,and quantitative intelligent tool,can assist fact-finders in completing the process of discretionary evaluation,uncovering the black box of discretionary evaluation,and promoting the accuracy of fact-finding.Bayesian AI uses Bayesian network diagrams as the knowledge system to visually represent the inferential relationships between evidence and facts.It utilizes probability numbers as the data system,transparently displaying the fact-finder's subjective judgment on the degree of dependence between evidence and facts.Probabilistic algorithms are used as the inference system to intelligently calculate posterior probabilities,quantifying the strength of inner conviction in inference.The Bayesian AI model offers five advantages:First,Bayesian AI can not only use machine learning methods to process big data but also integrate smart data such as expert knowledge and judgments.When dealing with risky decisions with little or no data,such as court trials,Bayesian AI can combine human knowledge and experience to provide viable analytical models.Second,Bayesian network inference makes the propagation and updating of the inferential strength of evidence in continuous inference logical and scientific.It can not only handle corroborating evidence but also uniformly analyze conflicting evidence.Third,the application of software tools solves the dilemma of"weak mathematical calculation"faced by fact-finders and promotes the application of the Bayesian AI model in the practice of fact-finding.Fact-finders who are not proficient in probabilistic reasoning can effectively use software tools to construct Bayesian network models after simple training.Fourth,the Bayesian AI model helps to open the black box of discretionary evaluation,presenting the subjective judgment and reasoning process of fact-finders in a visual,transparent,and quantitative manner,thereby promoting the accuracy of fact-finding.Fifth,Bayesian AI is a probabilistic reasoning expert system based on human-computer collaboration.Its goal is to"assist people"and"enhance people"rather than"replace people."All judicial decisions are still made by humans,and the subject status of fact finders remains unchanged.However,the Bayesian AI model also faces challenges.First,it requires a large number of probability numbers as input,but fact-finders'subjective judgments on evidence are difficult to accurately transform into probability numbers,thus affecting the accuracy of the model's outputs to a certain extent.Second,while the Bayesian AI model can avoid logical fallacies in evidential reasoning and is a tool for the explicit expression of subjectivity,it cannot eliminate subjectivity.
Discretional evaluation of evidenceBayesian artificial intelligenceFact-finding
万方数据
维普
