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1. "Procurement As Policy: Administrative Process for Machine Learning."

   https://doi.org/10.2139/ssrn.3464203  

   Mulligan, Deirdre K. (February 2020, Berkeley technology law journal)

   null (Ed.)

   At every level of government, officials contract for technical systems that employ machine learning-systems that perform tasks without using explicit instructions, relying on patterns and inference instead. These systems frequently displace discretion previously exercised by policymakers or individual front-end government employees with an opaque logic that bears no resemblance to the reasoning processes of agency personnel. However, because agencies acquire these systems through government procurement processes, they and the public have little input into-or even knowledge about-their design or how well that design aligns with public goals and values. This Article explains the ways that the decisions about goals, values, risk, and certainty, along with the elimination of case-by-case discretion, inherent in machine-learning system design create policies-not just once when they are designed, but over time as they adapt and change. When the adoption of these systems is governed by procurement, the policies they embed receive little or no agency or outside expertise beyond that provided by the vendor. Design decisions are left to private third-party developers. There is no public participation, no reasoned deliberation, and no factual record, which abdicates Government responsibility for policymaking. This Article then argues for a move from a procurement mindset to policymaking mindset. When policy decisions are made through system design, processes suitable for substantive administrative determinations should be used: processes that foster deliberation reflecting both technocratic demands for reason and rationality informed by expertise, and democratic demands for public participation and political accountability. Specifically, the Article proposes administrative law as the framework to guide the adoption of machine learning governance, describing specific ways that the policy choices embedded in machine learning system design fail the prohibition against arbitrary and capricious agency actions absent a reasoned decision-making process that both enlists the expertise necessary for reasoned deliberation about, and justification for, such choices, and makes visible the political choices being made. Finally, this Article sketches models for machine-learning adoption processes that satisfy the prohibition against arbitrary and capricious agency actions. It explores processes by which agencies might garner technical expertise and overcome problems of system opacity, satisfying administrative law's technocratic demand for reasoned expert deliberation. It further proposes both institutional and engineering design solutions to the challenge of policymaking opacity, offering process paradigms to ensure the "political visibility" required for public input and political oversight. In doing so, it also proposes the importance of using "contestable design"-design that exposes value-laden features and parameters and provides for iterative human involvement in system evolution and deployment. Together, these institutional and design approaches further both administrative law's technocratic and democratic mandates. 

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2. NIST's Software Un-Standards

   Choi, Bryan H (March 2024, Lawfare)

   The National Institute of Standards and Technology (NIST) has become a beacon of hope for those who trust in federal standards for software and AI safety. Moreover, lawmakers and commentators have indicated that compliance with NIST standards ought to shield entities from liability. With more than a century of expertise in scientific research and standard-setting, NIST would seem to be uniquely qualified to develop such standards. But as I argue in this paper, this faith is misplaced. NIST’s latest forays in risk management frameworks disavow concrete metrics or outcomes, and solicit voluntary participation instead of providing stable mandates. That open-ended approach can be attributed to the reversal of NIST’s prior efforts to promulgate federal software standards during the 1970s and 1980s. The failure of those federal regulatory efforts highlights fundamental challenges inherent in software development that continue to persist today. Policymakers should draw upon the lessons of NIST’s experience and recognize that federal standards are unlikely to be the silver bullet. Instead, they should heed NIST’s admonition that the practice of software development remains deeply fragmented for other intrinsic reasons. Any effort to establish a universal standard of care must grapple with the need to accommodate the broad heterogeneity of accepted practices in the field. 

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3. Not Whether to Coordinate, But How: Concerns and Mechanism Choice Under a Mandate for Inter-Agency Coordination

   https://doi.org/10.1093/ppmgov/gvae003  

   Milman, Anita; Roberts, Michael; Walsh, Amber; Blomquist, William (March 2024, Perspectives on Public Management and Governance)

   A critical question in relation to inter-agency coordination is not only whether, but how, to coordinate. This question is particularly salient when agencies are subject to a top-down mandate. While inter-agency coordination can provide multiple benefits, agencies frequently have concerns about the potential risks of coordination. Differing coordination mechanisms may reduce or exacerbate those concerns. Depending on their coordination concerns, agencies will be inclined to favor certain mechanisms over others. Examination of the implementation of California’s Sustainable Groundwater Management Act, which mandates local agency coordination, indicates that coordination mechanism selection is influenced by which combination of concerns agencies hold, with autonomy considerations taking priority over other concerns. These findings suggest opportunities to improve the explanatory power of theories of inter-agency coordination by incorporating potential hierarchies of concerns, their distribution across the multiple agencies tasked with coordinating, and configurational effects. To this end, we propose a contingency theory of agency concerns and coordination mechanism choice under a mandate to coordinate. 

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4. Disaster Management and Information Transmission Decision-Making in Public Safety Systems

   https://doi.org/10.1109/GLOBECOM38437.2019.9013440  

   Fragkos, Georgios; Tsiropoulou, Eirini Eleni; Papavassiliou, Symeon (December 2019, 2019 IEEE Global Communications Conference (GLOBECOM))

   This paper introduces a Multi-Agency DisAster Management (MADAM) framework for Unmanned Aerial Vehicle (UAV)-assisted public safety systems, based on the principles of game theory and reinforcement learning. Initially, the information quality and criticality (IQC) provided by each agency to an UAV-assisted public safety network is introduced and quantified, and the concept of Value of Information (VoI) that measures each agency’s positive contribution to the overall disaster management process is defined. Based on these, a holistic cost function is adopted by each agency, reflecting its relative abstention from the information provisioning process. Each agency aims at minimizing its personal cost function in order to better contribute to the disaster management. This optimization problem is formulated as a non-cooperative game among the agencies and it is proven to be an exact potential game, thus guaranteeing the existence of at least one Pure Nash Equilibrium (PNE). We propose a binary log-linear reinforcement learning algorithm that converges to the optimal PNE. The performance of the proposed approach is evaluated through modeling and simulation under several scenarios, and its superiority compared to other approaches is demonstrated. 

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5. Examining the Communication Pattern of Transportation and Transit Agencies on Twitter: A Longitudinal Study in the Emergence of COVID-19 on Twitter

   https://doi.org/10.1177/03611981221082564  

   Alam, Md_Rakibul; Sadri, Arif_Mohaimin (March 2022, Transportation Research Record: Journal of the Transportation Research Board)

   Social media can be a significant tool for transportation and transit agencies providing passengers with real-time information on traffic events. Moreover, COVID-19 and other limitations have compelled the agencies to engage with travelers online to promote public knowledge about COVID-related issues. It is, therefore, important to understand the agencies’ communication patterns. In this original study, the Twitter communication patterns of different transportation actors—types of message, communication sufficiency, consistency, and coordination—were examined using a social media data-driven approach applying text mining techniques and dynamic network analysis. A total of 850,000 tweets from 395 different transportation and transit agencies, starting in 2018 and the periods before, during and after the pandemic, were studied. Transportation agencies (federal, state, and city) were found to be less active on Twitter and mostly discussed safety measures, project management, and so forth. By contrast, the transit agencies (local bus and light, heavy, and commuter rail) were more active on Twitter and shared information about crashes, schedule information, passenger services, and so forth. Moreover, transportation agencies shared minimal pandemic safety information than transit agencies. Dynamic network analysis reveals interaction patterns among different transportation actors that are poorly connected and coordinated among themselves and with different health agencies (e.g., Centers for Disease Control and Prevention [CDC] and the Federal Emergency Management Agency [FEMA]). The outcome of this study provides understanding to improve existing communication plans, critical information dissemination efficacy, and the coordination of different transportation actors in general and during unprecedented health crises. 

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