Cutting in on the \u3ci\u3eChevron\u3c/i\u3e Two-Step

Consider the following scenario: an ambiguous statutory provision could plausibly mean A or B—which could in fact be the opposite of A. A federal agency, drawing upon its scientific and/or experiential expertise, either has or could develop policy-based reasons backed by fact-intensive evidence to prefer one interpretation over the other. But instead of developing and setting forth its policy reasons and subjecting them to vetting in a notice-andcomment rulemaking, the agency instead justifies its interpretive choice in a rule, setting forth its legal analysis of statutory text, perhaps legislative history, and the purpose and structure of the statute as a whole. Subsequently, in a dispute over how the statutory provision should be interpreted, the agency claims that its interpretive view merits judicial deference. In statutory interpretation cases, courts typically invoke the Chevron Two-Step framework and, given that the agency has promulgated a rule, assuming the court agrees that the statutory provision is ambiguous at Step One, the agency is all but assured deference at Step Two. What is wrong with this scenario? First, from a comparative institutionalist perspective, deference to agencies’ statutory interpretations should be premised upon the agencies’ policy-based expertise; thus, it should be withheld where agencies have not provided policy based rationales for their interpretive choices. Second, the “reasoned decisionmaking” element of judicial review drops out of the picture altogether and thus judicial oversight of agencies is diminished. In other words, it should not be “per se” reasonable when an agency chooses—based on unarticulated and thus unvetted policy variables—between two permissible statutory interpretations. This Article proposes a doctrinal solution: the incorporation of State Farm hard look review into the Chevron Two-Step framework. The main goal is to extend the domain of State Farm “reasoned decisionmaking” review, widening the scope of agency rules subject to hard look review. By incorporating this hard look review within the Chevron framework, the model highlights the extent to which agency statutory interpretations are driven by underlying policy choices. And by collapsing the conceptual acoustic separation of Chevron and State FarmV, the model makes it difficult for an agency to evade hard look review by convincing a court that it is a Chevron, not State Farm, case. Moreover, where the Chevron interpretive issue arises between private parties when the agency is not a party, and litigants accordingly cannot raise a direct State Farm challenge to the rulemaking, the model would open the door to an indirect State Farm challenge. This Article explores how this new doctrinal approach, one of hard look review of agency policy decisions at Chevron Step Two, will affect courts and agency decision-making. Finally, the U.S. Supreme Court seems to have reached a critical juncture for Chevron. This particular form of Chevron retreat—widening the space for the application of State Farm—is fundamentally distinct from, and preferable to, setting Chevron aside. Whereas knocking down the Chevron pillar deals a blow to overexuberant regulators and promises to stem the tide of overregulation of the economy and health and safety, heightened judicial scrutiny of the Chevron-State Farm variety will force the agency’s hand in the context of deregulation as well

The coming robot crime wave

Rapid advances in robotics technology for the battlefield and policing could promote a new breed of copycat "garden shed" robot criminals

Unstable, Concurrent Modalities for Sensor Networks

In recent years, much research has been devoted to the development of e-commerce; contrarily, few have visualized the visualization of wide-area networks. In this paper, authors verify the exploration of SMPs, demonstrates the confusing importance of machine learning. In this paper, we concentrate our efforts on proving that spreadsheets can be made symbiotic, replicated, and homogeneous