Cannabis Banking: What Marijuana Can Learn from Hemp

Marijuana-related businesses have banking problems. Many banks explain that, because marijuana is illegal under federal law, they will not serve the industry. Even when marijuana-related businesses can open bank accounts, they still have trouble accepting credit cards and getting loans. Some hope to fix marijuana\u27s banking problems with changes to federal law. Proposals range from broad reforms removing marijuana from the list of controlled substances to narrower legislation prohibiting banking regulators from punishing banks that serve the marijuana industry. But would these proposals solve marijuana\u27s banking problems? In 2018, Congress legalized another variant of the Cannabis plant species: hemp. Prior to legalization, hemp-related businesses, like marijuana-related businesses, struggled with banking. Some hoped legalization would solve hemp\u27s banking problems. It did not. By analyzing the hemp banking experience, this Article provides three insights. First, legalization does not necessarily lead to inexpensive, widespread banking services. Second, regulatory uncertainty hampers access to banking services. When banks were unsure what state and federal law required of hemp businesses and were unclear about bank regulators\u27 compliance expectations for hemp-related accounts, they were less likely to serve the hemp industry. Regulatory structures that allow banks to easily identify who can operate cannabis businesses and verify whether the business is compliant with the law are more conducive to banking. Finally, even with clear law and favorable regulatory structures, the emerging cannabis industry will still present credit, market, and other risks that make some banks hesitant to lend

Approaching algorithmic power

Contemporary power manifests in the algorithmic. Emerging quite recently as an object of study within media and communications, cultural research, gender and race studies, and urban geography, the algorithm often seems ungraspable. Framed as code, it becomes proprietary property, black-boxed and inaccessible. Framed as a totality, its becomes overwhelmingly complex, incomprehensible in its operations. Framed as a procedure, it becomes a technique to be optimised, bracketing out the political. In struggling to adequately grasp the algorithmic as an object of study, to unravel its mechanisms and materialities, these framings offer limited insight into how algorithmic power is initiated and maintained. This thesis instead argues for an alternative approach: firstly, that the algorithmic is coordinated by a coherent internal logic, a knowledge-structure that understands the world in particular ways; second, that the algorithmic is enacted through control, a material and therefore observable performance which purposively influences people and things towards a predetermined outcome; and third, that this complex totality of architectures and operations can be productively analysed as strategic sociotechnical clusters of machines. This method of inquiry is developed with and tested against four contemporary examples: Uber, Airbnb, Amazon Alexa, and Palantir Gotham. Highly profitable, widely adopted and globally operational, they exemplify the algorithmic shift from whiteboard to world. But if the world is productive, it is also precarious, consisting of frictional spaces and antagonistic subjects. Force cannot be assumed as unilinear, but is incessantly negotiated—operations of parsing data and processing tasks forming broader operations that strive to establish subjectivities and shape relations. These negotiations can fail, destabilised by inadequate logics and weak control. A more generic understanding of logic and control enables a historiography of the algorithmic. The ability to index information, to structure the flow of labor, to exert force over subjects and spaces— these did not emerge with the microchip and the mainframe, but are part of a longer lineage of calculation. Two moments from this lineage are examined: house-numbering in the Habsburg Empire and punch-card machines in the Third Reich. Rather than revolutionary, this genealogy suggests an evolutionary process, albeit uneven, linking the computation of past and present. The thesis makes a methodological contribution to the nascent field of algorithmic studies. But more importantly, it renders algorithmic power more intelligible as a material force. Structured and implemented in particular ways, the design of logic and control construct different versions, or modalities, of algorithmic power. This power is political, it calibrates subjectivities towards certain ends, it prioritises space in specific ways, and it privileges particular practices whilst suppressing others. In apprehending operational logics, the practice of method thus foregrounds the sociopolitical dimensions of algorithmic power. As the algorithmic increasingly infiltrates into and governs the everyday, the ability to understand, critique, and intervene in this new field of power becomes more urgent

Attracting Commercial Artificial Intelligence Firms to Support National Security through Collaborative Contracts

The United States Department of Defense (‘DoD’) has determined it is not ready to compete in the Artificial Intelligence (‘AI’) era without significant changes to how it acquires AI. Unlike other military technologies driven by national security needs and developed with federal funding, this ubiquitous technology enabler is predominantly funded and advanced by commercial industry for civilian applications. However, there is a lack of understanding of the reasons commercial AI firms decide to work with the DoD or choose to abstain from the defence market. Although there are several challenges to attracting commercial AI firms to support national security, this thesis argues that the DoD’s contract law and procurement framework are among the most significant obstacles. This research indicates that the commercial AI industry actually views the DoD as an attractive customer. However, this attraction is despite the obstacles presented by traditional contract law and procurement practices used to solicit and award contracts. Drawing on social exchange theory, this thesis introduces a theoretical framework – ‘optimal buyer theory’ – to understand the factors that influence a commercial AI firm’s decision to engage with the DoD. It develops evidence-based best practices in contract law that reveal how the DoD can become a more attractive customer to commercial AI firms. This research builds upon research at the nexus of national security and defence contracts as it studies business decision-makers from AI firms through an explanatory sequential mixed methods design. In the study’s first phase, participants are surveyed to discover the perceptions, opinions, and preferences at AI firms of all sizes, maturity, location, and experience within the DoD marketplace. In the second phase of the study, interviews from a sample of the participants explain why the AI industry holds such perceptions, opinions, and preferences about contracts generally and the DoD, specifically, in its role as a customer. This thesis concludes that commercial AI firms are attracted to contracts that are consistent with their business and technology considerations. These considerations align with contractual relationships that are collaborative, flexible, negotiated, iterative, and awarded promptly as opposed to those with fixed requirements and driven by regulations foreign to the commercial market. Additionally, it develops best practices for leveraging existing contract law, primarily other transaction authority, to align the DoD’s contracting practices with commercial preferences and the machine learning development and deployment lifecycle. Armed with this understanding, the DoD can better attract commercial AI firms to support its national security objectives.Thesis (Ph.D.) -- University of Adelaide, Law School, 202