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1. Automated decision support technologies and the legal profession

   https://doi.org/10.2139/ssrn.3443063  

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

   null (Ed.)

   A quiet revolution is afoot in the field of law. Technical systems employing algorithms are shaping and displacing professional decision making, and they are disrupting and restructuring relationships between law firms, lawyers, and clients. Decision-support systems marketed to legal professionals to support e-discovery—generally referred to as “technology assisted review” (TAR)—increasingly rely on “predictive coding”: machine-learning techniques to classify and predict which of the voluminous electronic documents subject to litigation should be withheld or produced to the opposing side. These systems and the companies offering them are reshaping relationships between lawyers and clients, introducing new kinds of professionals into legal practice, altering the discovery process, and shaping how lawyers construct knowledge about their cases and professional obligations. In the midst of these shifting relationships—and the ways in which these systems are shaping the construction and presentation of knowledge—lawyers are grappling with their professional obligations, ethical duties, and what it means for the future of legal practice. Through in-depth, semi-structured interviews of experts in the e-discovery technology space—the technology company representatives who develop and sell such systems to law firms and the legal professionals who decide whether and how to use them in practice—we shed light on the organizational structures, professional rules and norms, and technical system properties that are shaping and being reshaped by predictive coding systems. Our findings show that AI-supported decision systems such as these are reconfiguring professional work practices. In particular, they highlight concerns about potential loss of professional agency and skill, limited understanding and thereby both over- and under reliance on decision-support systems, and confusion about responsibility and accountability as new kinds of technical professionals and technologies are brought into legal practice. The introduction of predictive coding systems and the new professional and organizational arrangements they are ushering into legal practice compound general concerns over the opacity of technical systems with specific concerns about encroachments on the construction of expert knowledge, liability frameworks, and the potential (mis)alignment of machine reasoning with professional logic and ethics. Based on our findings, we conclude that predictive coding tools—and likely other algorithmic systems lawyers use to construct knowledge and reason about legal practice— challenge the current model for evaluating whether and how tools are appropriate for legal practice. As tools become both more complex and more consequential, it is unreasonable to rely solely on legal professionals—judges, law firms, and lawyers—to determine which technologies are appropriate for use. The legal professionals we interviewed report relying on the evaluation and judgment of a range of new technical experts within law firms and, increasingly, third-party vendors and their technical experts. This system for choosing technical systems upon which lawyers rely to make professional decisions—e.g., whether documents are responsive, or whether the standard of proportionality has been met—is no longer sufficient. As the tools of medicine are reviewed by appropriate experts before they are put out for consideration and adoption by medical professionals, we argue that the legal profession must develop new processes for determining which algorithmic tools are fit to support lawyers’ decision making. Relatedly, because predictive coding systems are used to produce lawyers’ professional judgment, we argue they must be designed for contestability— providing greater transparency, interaction, and configurability around embedded choices to ensure decisions about how to embed core professional judgments, such as relevance and proportionality, remain salient and demand engagement from lawyers, not just their technical experts. 

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2. “It’s Like the Value System in the Loop”: Domain Experts’ Values Expectations for NLP Automation

   https://doi.org/10.1145/3532106.3533483  

   Showkat, Dilruba; Baumer, Eric P. (June 2022, DIS '22: ACM Conference on Designing Interactive Systems Conference)

   The rise of automated text processing systems has led to the development of tools designed for a wide variety of application domains. These technologies are often developed to support non-technical users such as domain experts and are often developed in isolation of the tools primary user. While such developments are exciting, less attention has been paid to domain experts’ expectations about the values embedded in these automated systems. As a step toward addressing that gap, we examined values expectations of journalists and legal experts. Both these domains involve extensive text processing and place high importance on values in professional practice. We engaged participants from two non-profit organizations in two separate co-speculation design workshops centered around several speculative automated text processing systems. This study makes three interrelated contributions. First, we provide a detailed investigation of domain experts’ values expectations around future NLP systems. Second, the speculative design fiction concepts, which we specifically crafted for these investigative journalists and legal experts, illuminated a series of tensions around the technical implementation details of automation. Third, our findings highlight the utility of design fiction in eliciting not-to-design implications, not only about automated NLP but also about technology more broadly. Overall, our study findings provide groundwork for the inclusion of domain experts values whose expertise lies outside of the field of computing into the design of automated NLP systems. 

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3. The Paradox of Automation as Anti-Bias Intervention

   Ajunwa, Ifeoma (April 2020, Cardozo law review)

   null (Ed.)

   A received wisdom is that automated decision-making serves as an anti-bias intervention. The conceit is that removing humans from the decision-making process will also eliminate human bias. The paradox, however, is that in some instances, automated decision-making has served to replicate and amplify bias. With a case study of the algorithmic capture of hiring as heuristic device, this Article provides a taxonomy of problematic features associated with algorithmic decision-making as anti-bias intervention and argues that those features are at odds with the fundamental principle of equal opportunity in employment. To examine these problematic features within the context of algorithmic hiring and to explore potential legal approaches to rectifying them, the Article brings together two streams of legal scholarship: law and technology studies and employment & labor law. Counterintuitively, the Article contends that the framing of algorithmic bias as a technical problem is misguided. Rather, the Article’s central claim is that bias is introduced in the hiring process, in large part, due to an American legal tradition of deference to employers, especially allowing for such nebulous hiring criterion as “cultural fit.” The Article observes the lack of legal frameworks that take into account the emerging technological capabilities of hiring tools which make it difficult to detect disparate impact. The Article thus argues for a re-thinking of legal frameworks that take into account both the liability of employers and those of the makers of algorithmic hiring systems who, as brokers, owe a fiduciary duty of care. Particularly related to Title VII, the Article proposes that in legal reasoning corollary to extant tort doctrines, an employer’s failure to audit and correct its automated hiring platforms for disparate impact could serve as prima facie evidence of discriminatory intent, leading to the development of the doctrine of discrimination per se. The article also considers other approaches separate from employment law such as establishing consumer legal protections for job applicants that would mandate their access to the dossier of information consulted by automated hiring systems in making the employment decision. 

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4. An Auditing Imperative for Automated Hiring

   Ajunwa, Ifeoma (April 2021, Harvard journal of law technology)

   null (Ed.)

   The high bar of proof to demonstrate either a disparate treatment or disparate impact cause of action under Title VII of the Civil Rights Act, coupled with the “black box” nature of many automated hiring systems, renders the detection and redress of bias in such algorithmic systems difficult. This Article, with contributions at the intersection of administrative law, employment & labor law, and law & technology, makes the central claim that the automation of hiring both facilitates and obfuscates employment discrimination. That phenomenon and the deployment of intellectual property law as a shield against the scrutiny of automated systems combine to form an insurmountable obstacle for disparate impact claimants. To ensure against the identified “bias in, bias out” phenomenon associated with automated decision-making, I argue that the employer’s affirmative duty of care as posited by other legal scholars creates “an auditing imperative” for algorithmic hiring systems. This auditing imperative mandates both internal and external audits of automated hiring systems, as well as record-keeping initiatives for job applications. Such audit requirements have precedent in other areas of law, as they are not dissimilar to the Occupational Safety and Health Administration (OSHA) audits in labor law or the Sarbanes-Oxley Act audit requirements in securities law. I also propose that employers that have subjected their automated hiring platforms to external audits could receive a certification mark, “the Fair Automated Hiring Mark,” which would serve to positively distinguish them in the labor market. Labor law mechanisms such as collective bargaining could be an effective approach to combating the bias in automated hiring by establishing criteria for the data deployed in automated employment decision-making and creating standards for the protection and portability of said data. The Article concludes by noting that automated hiring, which captures a vast array of applicant data, merits greater legal oversight given the potential for “algorithmic blackballing,” a phenomenon that could continue to thwart many applicants’ future job bids. 

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5. What drives cities to adopt groundwater banking?: a cross-case analysis of US cities

   https://doi.org/10.1007/s13412-025-01019-2  

   Bartels, Lauren; Koebele, Elizabeth_A (May 2025, Journal of Environmental Studies and Sciences)

   Abstract As climate change increases water supply variability, urban water utilities must adopt innovative strategies to enhance water system sustainability. Groundwater banking (GWB), or the storage of water in aquifers for later use, is a relatively novel water management strategy that can help utilities adapt to such challenges while providing several benefits over more typical resilience actions. However, its slow and unevenly distributed adoption suggests a need to better understand the drivers of and barriers to GWB adoption. We use a mixed-methods approach to analyze conditions that may promote, or hinder, GWB adoption in 16 urban water systems in the United States in order to draw lessons for other systems. We find that specific environmental and legal conditions are necessary to facilitate GWB adoption, though they must coincide with context-dependent policy, economic, social, and/or technical conditions. We also identify several potential barriers to GWB adoption, which may be more easily overcome by water utilities with access to financial and technical resources. These findings can help resource managers assess the viability of adopting GWB and similar innovative water resilience strategies in their unique management contexts. 

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