[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119091-en":3,"doc-seo-119091-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119091,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Evolutionary Interpretation - Law and Machine Learning","Approaches to interpretability in artificial intelligence and law are developed through evolutionary theory, treating evolution as blind or mindless “direct fitting” in which system and environment iteratively co-constitute and align. Natural selection grounds the account, while legal reasoning is modeled as variation, selection, and retention within its social context. Machine learning is linked to backpropagation-driven error correction and long-run dynamics of legal change, without assuming reliable prediction of case outcomes. Individual-case interpretation is instead tied to natural language’s generative ability to extrapolate from precedents to novel facts, favoring forward-looking reasoning.","Evolutionary interpretation: law and machine learning  \nSimon Deakin* Christopher Markou†  \nAbstract  \nWe approach the issue of interpretability in artificial intelligence and law through the lens of evolutionary theory. Evolution is understood as a form of blind or mindless ‘direct fitting’, an iterative process through which a system and its environment are mutually constituted and aligned. The core case is natural selection as described in biology but it is not the only one. Legal reasoning can be understood as a step in the ‘direct fitting’ of law, through a cycle of variation, selection and retention, to its social context. Machine learning, insofar as it relies on error correction through backpropagation, is a version of the same process. It may therefore have value for understanding the long-run dynamics of legal and social change. This is distinct, however, from any use it may have in predicting case outcomes. Legal interpretation in the context of the individual or instant case depends upon the generative power of natural language to extrapolate from existing precedents to novel fact situations. This type of prospective or forward-looking reasoning is unlikely to be well captured by machine learning approaches.  \nKeywords: legal evolution, precedent, artificial intelligence, machine learning, interpretability Replier: Masha Medvedeva, [University of Groningen. m.medvedeva@rug.nl](University of Groningen. m.medvedeva@rug.nl).  \nJournal of Cross-disciplinary Research in Computational Law © 2022 Simon Deakin and Christopher Markou DOI: pending  \nLicensed under a Creative Commons BY-NC 4.0 license [www.journalcrcl.org](www.journalcrcl.org)  \n* Professor of Law, [University of Cambridge. s.deakin@cbr.cam.ac.uk](University of Cambridge. s.deakin@cbr.cam.ac.uk).  \n†Affiliated lecturer, University of Cambridge. [cpm49@cam.ac.uk](cpm49@cam.ac.uk).  \nIntroduction  \nArtificial intelligence (AI) promises to either replicate, emulate or simulate legal reasoning through a suite of statistical learning and inference-making techniques referred to as machine learning (ML) .1 While the short-term aim of AI advocates involves leveraging these techniques to complement, enhance or extend the capabilities of judges and legal practitioners, it appears that the long-term goal is replacing them altogether.2 Thus, the rise of ML and automated decision-making is self-evidently a significant challenge to legal modes of thought and action. While this current generation of ‘connectionist’ AI is rich in data and wields increasingly ferocious computational horsepower with which to crunch it, the same explanatory gapsand ‘penumbras of doubt’ that led to the stagnation of an earlier generation of AI-leveraging models—referred to at the end of the 20th century as ‘legal expert systems’—remain largely unaddressed or explained away as irrelevant.3  \nIt helps to keep this not-too-distant history in mind as anew generation of ‘legal tech’ start-ups and their tools are unleashed upon law firms and legal systems worldwide.4 This is particularly so with respect to those aspects of legal tech that are framing current debates around ‘explainable AI’ and what some call the ‘seductive diversion’ of solving the black box problem: finding a way to have elaborate and opaque algorithms not just show their work but jus-  \ntify their methods, whether this comes in the form of a‘decision tree’ which allows the causal antecedents in a model to be isolated and assessed—most often in terms of the ‘weight’ given to a particular statistical variable—or alternative strategies such as model-agnostic explanators that can identify rules that give insights into why a model provides a specific outcome for a specific input.5  \nTranslating non-linear equations and probabilistic inference into clearly defined tributaries of ‘reason’ is both a technical and epistemic problem. It is also a paradox that limits the very ‘power and promise of computers that learn by example’.6 This has pr","cbCaiuiIdvrCJ8sw","https://ap.wps.com/l/cbCaiuiIdvrCJ8sw","pdf",303510,1,18,"English","en",105,"# Introduction\n## Interpretability, explainable AI, and the black box problem\n## Legal reasoning, natural language, and generative capacity\n## Evolutionary “direct fitting” applied to law and machine learning","[{\"question\":\"How does the document explain interpretability between AI and law?\",\"answer\":\"It uses evolutionary theory to frame interpretability as an evolutionary-style “direct fitting” process linking system and environment through iterative alignment.\"},{\"question\":\"What role does legal reasoning play in the evolutionary interpretation?\",\"answer\":\"Legal reasoning is presented as a cycle of variation, selection, and retention that fits law to its social context.\"},{\"question\":\"Why does the document distinguish using machine learning for understanding change versus predicting case outcomes?\",\"answer\":\"It argues machine learning may help explain long-run legal and social dynamics through error correction, but prospective forward-looking interpretation of individual cases relies on natural language extrapolation rather than ML predictions.\"}]","Evolutionary Interpretation - 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