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Abstract

Artificial Intelligence (AI) in Law has a long history of research dating back to at least 1958. Despite decades of work, Artificial Intelligence has not scaled out of academia to real-life courtrooms and mediation chambers. The reason, in our opinion is: theory of learning in computation has only recently caught up with adversarial inference or defeasible logic, a form of social learning widely used both in theory and practice of common law. We posit that Artificial Intelligence that uses Causal Inference models (a quantum leap from defeasible logic) approximate social learning very well. They provide a quantitative formulation for assignation of legal liability. We opine that mathematical formulations of non-zero-sum Game Theory could provide alternative dispute resolution (ADR) mechanisms for Consumer Law. The central theme of this paper is an analysis of the theory (logic & mathematics) of learning, i.e., epistemology in computation and jurisprudence, individually and at their intersection. In our analysis, we find mathematical models are finally approximating real-life dispute resolution. However, these require legal documents to be in standardized, formal language. The models cannot comprehend the wide variety, style and format of legal documents. We prescribe standardized document interchange and markup formats. Without these standardized inputs, Artificial Intelligence cannot automate negotiations & the decision process. It will fail to meet expected outcomes – provision of voluminous, consistent & speedy ‘access to justice’ in Consumer Law ODR.

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