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Body Revolution in Comparative Perspective: Promoting Equality Through Adoption of New Theory of Bodiliness, 55 UIC L. Rev. 615 (2022)
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Can Congress Authorize Lawsuits against States for Discriminating against Servicemembers and Veterans Pursuant to Its War Powers?
No full textJudicial Errors: Fake Imaging and the Modern Law of Evidence, 21 UIC Rev. Intell. Prop. L. 82 (2022)
Get PDFCan a State Criminal Subpoena to a Sitting President Be Trumped?: Exploring Trump v. Vance, 55 UIC L. Rev. 727 (2022)
Get PDFAntitrust\u27s AI Revolution, 89 Tenn. L. Rev. 679 (2022)
No full textAntitrust law operates like an algorithm. Its lodestar, the rule of reason, is a black box. Unlike most other areas of the law, judges, not Congress, write the rules and sometimes in surprisingly capricious ways. These rules govern everything from Google and Facebook\u27s “killer acquisitions” to vaccine development agreements during a pandemic. Injecting artificial intelligence (AI) into antitrust analysis seems prosaic, but in fact, it is revolutionary. Courts routinely lean on ideology as a heuristic when they must interpret the rule of reason in light of economic theory and evidence. Chicago School conservatism reined in some excesses of earlier populist and structuralist movements, but it also hampered antitrust enforcement and systemically penalized plaintiffs. Scholars widely agree Chicagoan antitrust overshot its mark and failed to protect consumers against corporate hegemony. Widespread anxiety over excessive private power recently spawned a chorus of voices beckoning a return to antitrust law\u27s populist past. None seem to realize the fix lies in embracing new technology rather than new ideology. This Article bridges two dominant streams of antitrust scholarship for the first time. First, it shifts the focus from AI as a collusive threat to its potential as a forensic and predictive tool to build the rule of reason from the bottom-up based on big data and computing power rather than top-down with wonky ideology. Second, this new method of algorithmic adjudication presents a new normative paradigm to replace Chicagoan fears of judicial inaptitude and false positives with a truly evidence-based alternative, particularly when dealing with cases involving nascent acquisitions and intellectual property rights. In getting down to the brass tacks, this Article confronts well-known concerns with AI deployment--bias, accountability, and data availability. It explains that these concerns, while legitimate, can be significantly mitigated or, in some cases, comprehensively addressed. The Article concludes by reflecting on the broader implications of algorithmic adjudication beyond antitrust law by discussing atextualism in action, algocracy and the common law, and the implications of plaintiff success to the rule of law
