Designing Human-Centered Algorithms for the Public Sector: A Case Study of the U.S. Child-Welfare System

The U.S. Child Welfare System (CWS) is increasingly seeking to emulate business models of the private sector centered in efficiency, cost reduction, and innovation through the adoption of algorithms. These data-driven systems purportedly improve decision-making, however, the public sector poses its own set of challenges with respect to the technical, theoretical, cultural, and societal implications of algorithmic decision-making. To fill these gaps, my dissertation comprises four studies that examine: 1) how caseworkers interact with algorithms in their day-to-day discretionary work, 2) the impact of algorithmic decision-making on the nature of practice, organization, and street-level decision-making, 3) how casenotes can help unpack patterns of invisible labor and contextualize decision-making processes, and 4) how casenotes can help uncover deeper systemic constraints and risk factors that are hard to quantify but directly impact families and street-level decision-making. My goal for this research is to investigate systemic disparities and design and develop algorithmic systems that are centered in the theory of practice and improve the quality of human discretionary work. These studies have provided actionable steps for human-centered algorithm design in the public sector

The Empirical Turn In Family Law

Historically, the legal system justified family law’s rules and policies through morality, common sense, and prevailing cultural norms. In a sharp departure, and consistent with a broader trend across the legal system, empirical evidence increasingly dominates the regulation of families. There is much to celebrate in this empirical turn. Properly used, empirical evidence in family law can help the state act more effectively and efficiently, unmask prejudice, and depoliticize contentious battles. But the empirical turn also presents substantial concerns. Beyond perennial issues of the quality of empirical evidence and the ability of legal actors to use it, there are more fundamental problems: Using empirical evidence focuses attention on the outcomes of legal rules, discouraging a debate about contested and competing values. Reliance on empirical evidence overlays a veneer of neutrality on normative judgments. And uncritically adopting evidence about present conditions without interrogating the role of historical discrimination that continues to disadvantage some families can replicate that discrimination. Given the promise and peril of the empirical turn in family law, this Essay proposes a framework to guide the use of this evidence. The framework preserves space for debating multiple values and advises decisionmakers when to use empirical evidence, with particular attention to the dangers for nondominant families. The framework also recommends strengthening evidentiary gatekeeping and elevating the potential for legal scholarship to serve as a bridge from the broader research base to the courts. With this guidance in place, empirical evidence can take its rightful place as a useful but cabined tool in the legal regulation of families

Rethinking "Risk" in Algorithmic Systems Through A Computational Narrative Analysis of Casenotes in Child-Welfare

Risk assessment algorithms are being adopted by public sector agencies to make high-stakes decisions about human lives. Algorithms model "risk" based on individual client characteristics to identify clients most in need. However, this understanding of risk is primarily based on easily quantifiable risk factors that present an incomplete and biased perspective of clients. We conducted a computational narrative analysis of child-welfare casenotes and draw attention to deeper systemic risk factors that are hard to quantify but directly impact families and street-level decision-making. We found that beyond individual risk factors, the system itself poses a significant amount of risk where parents are over-surveilled by caseworkers and lack agency in decision-making processes. We also problematize the notion of risk as a static construct by highlighting the temporality and mediating effects of different risk, protective, systemic, and procedural factors. Finally, we draw caution against using casenotes in NLP-based systems by unpacking their limitations and biases embedded within them

Empathy in the Digital Administrative State

Humans make mistakes. Humans make mistakes especially while filling out tax returns, benefit applications, and other government forms, which are often tainted with complex language, requirements, and short deadlines. However, the unique human feature of forgiving these mistakes is disappearing with the digitalization of government services and the automation of government decision-making. While the role of empathy has long been controversial in law, empathic measures have helped public authorities balance administrative values with citizens’ needs and deliver fair and legitimate decisions. The empathy of public servants has been particularly important for vulnerable citizens (for example, disabled individuals, seniors, and underrepresented minorities). When empathy is threatened in the digital administrative state, vulnerable citizens are at risk of not being able to exercise their rights because they cannot engage with digital bureaucracy. This Article argues that empathy, which in this context is the ability to relate to others and understand a situation from multiple perspectives, is a key value of administrative law deserving of legal protection in the digital administrative state. Empathy can contribute to the advancement of procedural due process, the promotion of equal treatment, and the legitimacy of automation. The concept of administrative empathy does not aim to create arrays of exceptions, nor imbue law with emotions and individualized justice. Instead, this concept suggests avenues for humanizing digital government and automated decision-making through a more complete understanding of citizens’ needs. This Article explores the role of empathy in the digital administrative state at two levels: First, it argues that empathy can be a partial response to some of the shortcomings of digital bureaucracy. At this level, administrative empathy acknowledges that citizens have different skills and needs, and this requires the redesign of pre-filled application forms, government platforms, algorithms, as well as assistance. Second, empathy should also operate ex post as a humanizing measure which can help ensure that administrative mistakes made in good faith can be forgiven under limited circumstances, and vulnerable individuals are given second chances to exercise their rights. Drawing on comparative examples of empathic measures employed in the United States, the Netherlands, Estonia, and France, this Article’s contribution is twofold: first, it offers an interdisciplinary reflection on the role of empathy in administrative law and public administration for the digital age, and second, it operationalizes the concept of administrative empathy. These goals combine to advance the position of vulnerable citizens in the administrative state

Trauma-Informed Education Toolkit for Screening Pediatric Victims of Sexual Abuse and Maltreatment

The complex challenges facing the sexual assault nurse examiners program in a midwest state are underreporting, late reporting, and poor coordination of care for pediatric victims of child maltreatment with sexual abuse. The main objective of this quality improvement project was the identification of necessary practice-related approaches to care to decrease barriers associated with reporting suspicions of abuse or neglect. An evidence-based, multidisciplinary assessment clinical toolkit that followed clinical components of trauma-sensitive, child-centered screenings triggering a coordinated response to conduct a forensic medical exam within 96 hours of the alleged incident was evaluated. During 3 rounds of surveys following the Delphi technique, 10 members of an expert panel agreed upon critical success indicators were used for the review and final decision for adoption of the toolkit. The final consensus obtained, with an intraclass correlation of 0.924 with a 95% confidence interval, supported implementation of this trauma-informed toolkit which would ensure that medical care and throughput through the system of care addressed the physical and mental needs of the patient and caregivers as well as improvement in the forensic investigative data collection. A child-centered, trauma-sensitive approach to screening and evaluation by healthcare professionals will help decrease the delay to evaluation and to curtail long-term adverse impacts on survivors. This family-based primary prevention effort is a framework for healthcare practitioners to use and includes strategies (i.e., health history, mental health evaluation, family dynamics evaluation) that are child and family centered contributing significantly to positive social change

2020 Toolkit for Centering Racial Equity

Societal “progress” is often marked by the construction of new infrastructure that fuels change and innovation. Just as railroads and interstate highways were the defining infrastructure projects of the 1800 and 1900s, the development of data infrastructure is a critical innovation of our century. Railroads and highways were drivers of development and prosperity for some investors and sites. Yet other individuals and communities were harmed, displaced, bypassed, ignored, and forgotten by those efforts. As railroads and highways both developed and decimated communities, so too can data infrastructure. At this moment in our history, we can co-create data infrastructure to promote racial equity and the public good, or we can invest in data infrastructure that disregards the historical, social, and political context—reinforcing racial inequity that continues to harm communities. Building data infrastructure without a racial equity lens and understanding of historical context will exacerbate existing inequalities along the lines of race, gender, class, and ability. Instead, we commit to contextualize our work in the historical and structural oppression that shapes it, and organize stakeholders across geography, sector, and experience to center racial equity throughout data integration