[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-135138-en":3,"doc-seo-135138-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},135138,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","You Might Be a Robot - Definitional Challenges and Policy Strategies","As robots and artificial intelligence (AI) expand their societal influence, regulation is accelerating—but meaningful governance depends on whether policymakers can define robots and AI in the first place. The article argues that definitional efforts struggle with rapid technological change and with legal definitions that can misclassify people and “dumb” machines. It proposes shifting from object-based rules to behavior-based regulation, using case-by-case tools rooted in Turing’s approach and brief, contingent standards.","You Might Be a Robot1  \nMark A. Lemley2 & Bryan Casey3  \nAbstract  \nAs robots and artificial intelligence (AI) increase their influence over society, policymakers are increasingly regulating them. But to regulate these technologies, we first need to know what they are. And here we come to a problem. No one has been able to offer a decent definition of robots and AI—not even experts. What’s more, technological advances make it harder and harder each day to tell people from robots and robots from “dumb” machines. We’ve already seen disastrous legal definitions written with one target in mind inadvertently affecting others. Infact, if you’re reading this you’re (probably) not a robot, but certain laws might already treat you as one.  \nDefinitional challenges like these aren’t exclusive to robots and AI. But today, all signs indicate we’re approaching an inflection point. Whether it’s citywide bans of “robot sex brothels”or nationwide efforts to crack down on “ticket scalping bots,” we’re witnessing an explosion of interest in regulating robots, human enhancement technologies, and all things in between. And that, in turn, means that typological quandaries once confined to philosophy seminars can no longer be dismissed as academic. Want, for example, to crack down on foreign “influence campaigns” by regulating social media bots? Be careful not to define “bot” too broadly (like the California legislature recently did), or the supercomputer nestled in your pocket might just make you one. Want, instead, to promote traffic safety by regulating drivers? Be careful not topresume that only humans can drive (as our Federal Motor Vehicle Safety Standards do), or you may soon exclude the best drivers on the road  \nIn this Article, we suggest that the problem isn’t simply that we haven’t hit upon the right definition. Instead, there may not be a “right” definition for the multifaceted, rapidly evolving technologies we call robots or AI. As we’ll demonstrate, even the most thoughtful of definitions risk being overbroad, underinclusive, or simply irrelevant in short order. Rather than trying in vainto find the perfect definition, we instead argue that policymakers should do as the great computer scientist, Alan Turing, did when confronted with the challenge of defining robots: embrace their ineffable nature. We offer several strategies to do so. First, whenever possible, laws should regulate behavior, not things (or as we put it, regulate verbs, not nouns). Second, where we must distinguish robots from other entities, the law should apply what we call Turing’s Razor, identifying robots on a case-by-case basis. Third, we offer six functional criteria for making these types of “I know it when I see it” determinations and argue that courts are generally better positioned than legislators to apply such standards. Finally, we argue that if we must have  \n1 © 2019 Mark A. Lemley and Bryan Casey.  \n2 William H. Neukom Professor, Stanford Law School; partner, Durie Tangri LLP.  \n3 Fellow, Center for Automotive Research at Stanford (CARS); Lecturer, Stanford Law School. Thanks to Ryan Calo and Rose Hagan for comments on an earlier draft.  \nElectronic copy available at: [https://ssrn.com/abstract=3327602](https://ssrn.com/abstract=3327602)  \ndefinitions rather than apply standards, they should be as short-term and contingent as possible. That, in turn, suggests regulators—not legislators—should play the defining role.  \nIntroduction  \n“If it looks like a duck, and quacks like a duck, we have at least to consider the possibility that we have a small aquatic bird of the family Anatidae on our hands.”  \n—DOUGLAS ADAMS4  \n“If it looks like a duck and quacks like a duck but it needs batteries, you probably have the wrong abstraction.”  \n—DERICK BAILEY5  \nIn the heat of the 2018 midterms, robots seemed poised to intervene in a second consecutive election cycle. Noticing an odd pattern of communications from Twitter accounts supporting Ted Cruz, an enterprising","cbCaikQ5rII1Tq7O","https://ap.wps.com/l/cbCaikQ5rII1Tq7O","pdf",806095,3,1,55,"English","en",105,"# Abstract\n# Introduction\n## Definitional problem for robots and AI\n## Examples of bot detection in political communications\n## Limits of broad legal definitions\n# Proposed strategies for policymakers\n## Regulate behavior, not things\n## Turing’s Razor: case-by-case identification\n## Functional criteria and judicial role\n## Prefer short-term, contingent definitions","[{\"question\":\"Why are definitional challenges central to regulating robots and AI?\",\"answer\":\"Because policymakers cannot reliably define robots and AI despite experts struggling, and technology advances make it increasingly hard to distinguish humans, robots, and “dumb” machines for legal purposes.\"},{\"question\":\"What is the article’s main argument about finding the “right” definition?\",\"answer\":\"There may be no durable “right” definition for multifaceted, fast-evolving technologies, since even thoughtful definitions can become overbroad, underinclusive, or obsolete.\"},{\"question\":\"What policy strategies does the article recommend instead of fixed definitions?\",\"answer\":\"It recommends regulating behavior rather than entities, using Turing’s Razor for case-by-case identification, applying functional criteria that courts can better use, and keeping standards short-term and contingent with regulators playing the defining role.\"}]","You Might Be a Robot - 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