Suspect Development Systems: Databasing Marginality and Enforcing Discipline

Algorithmic accountability law—focused on the regulation of data-driven systems like artificial intelligence (AI) or automated decision-making (ADM) tools—is the subject of lively policy debates, heated advocacy, and mainstream media attention. Concerns have moved beyond data protection and individual due process to encompass a broader range of group-level harms such as discrimination and modes of democratic participation. While a welcome and long overdue shift, the current discourse ignores systems like databases, which are viewed as technically “rudimentary” and often siloed from regulatory scrutiny and public attention. Additionally, burgeoning regulatory proposals like algorithmic impact assessments are not structured to surface important –yet often overlooked –social, organizational, and political economy contexts that are critical to evaluating the practical functions and outcomes of technological systems. This Article presents a new categorical lens and analytical framework that aims to address and overcome these limitations. “Suspect Development Systems” (SDS) refers to: (1) information technologies used by government and private actors, (2) to manage vague or often immeasurable social risk based on presumed or real social conditions (e.g. violence, corruption, substance abuse), (3) that subject targeted individuals or groups to greater suspicion, differential treatment, and more punitive and exclusionary outcomes. This framework includes some of the most recent and egregious examples of data-driven tools (such as predictive policing or risk assessments), but critically, it is also inclusive of a broader range of database systems that are currently at the margins of technology policy discourse. By examining the use of various criminal intelligence databases in India, the United Kingdom, and the United States, we developed a framework of five categories of features (technical, legal, political economy, organizational, and social) that together and separately influence how these technologies function in practice, the ways they are used, and the outcomes they produce. We then apply this analytical framework to welfare system databases, universal or ID number databases, and citizenship databases to demonstrate the value of this framework in both identifying and evaluating emergent or under-examined technologies in other sensitive social domains. Suspect Development Systems is an intervention in legal scholarship and practice, as it provides a much-needed definitional and analytical framework for understanding an ever-evolving ecosystem of technologies embedded and employed in modern governance. Our analysis also helps redirect attention toward important yet often under-examined contexts, conditions, and consequences that are pertinent to the development of meaningful legislative or regulatory interventions in the field of algorithmic accountability. The cross-jurisdictional evidence put forth across this Article illuminates the value of examining commonalities between the Global North and South to inform our understanding of how seemingly disparate technologies and contexts are in fact coaxial, which is the basis for building more global solidarity

A governance framework for algorithmic accountability and transparency

Algorithmic systems are increasingly being used as part of decision-making processes in both the public and private sectors, with potentially significant consequences for individuals, organisations and societies as a whole. Algorithmic systems in this context refer to the combination of algorithms, data and the interface process that together determine the outcomes that affect end users. Many types of decisions can be made faster and more efficiently using algorithms. A significant factor in the adoption of algorithmic systems for decision-making is their capacity to process large amounts of varied data sets (i.e. big data), which can be paired with machine learning methods in order to infer statistical models directly from the data. The same properties of scale, complexity and autonomous model inference however are linked to increasing concerns that many of these systems are opaque to the people affected by their use and lack clear explanations for the decisions they make. This lack of transparency risks undermining meaningful scrutiny and accountability, which is a significant concern when these systems are applied as part of decision-making processes that can have a considerable impact on people's human rights (e.g. critical safety decisions in autonomous vehicles; allocation of health and social service resources, etc.). This study develops policy options for the governance of algorithmic transparency and accountability, based on an analysis of the social, technical and regulatory challenges posed by algorithmic systems. Based on a review and analysis of existing proposals for governance of algorithmic systems, a set of four policy options are proposed, each of which addresses a different aspect of algorithmic transparency and accountability: 1. awareness raising: education, watchdogs and whistleblowers; 2. accountability in public-sector use of algorithmic decision-making; 3. regulatory oversight and legal liability; and 4. global coordination for algorithmic governance

Suspect Development Systems: Databasing Marginality and Enforcing Discipline

Algorithmic accountability law—focused on the regulation of data-driven systems like artificial intelligence (AI) or automated decision-making (ADM) tools—is the subject of lively policy debates, heated advocacy, and mainstream media attention. Concerns have moved beyond data protection and individual due process to encompass a broader range of group-level harms such as discrimination and modes of democratic participation. While a welcome and long overdue shift, the current discourse ignores systems like databases, which are viewed as technically “rudimentary” and often siloed from regulatory scrutiny and public attention. Additionally, burgeoning regulatory proposals like algorithmic impact assessments are not structured to surface important –yet often overlooked –social, organizational, and political economy contexts that are critical to evaluating the practical functions and outcomes of technological systems. This Article presents a new categorical lens and analytical framework that aims to address and overcome these limitations. “Suspect Development Systems” (SDS) refers to: (1) information technologies used by government and private actors, (2) to manage vague or often immeasurable social risk based on presumed or real social conditions (e.g. violence, corruption, substance abuse), (3) that subject targeted individuals or groups to greater suspicion, differential treatment, and more punitive and exclusionary outcomes. This framework includes some of the most recent and egregious examples of data-driven tools (such as predictive policing or risk assessments), but critically, it is also inclusive of a broader range of database systems that are currently at the margins of technology policy discourse. By examining the use of various criminal intelligence databases in India, the United Kingdom, and the United States, we developed a framework of five categories of features (technical, legal, political economy, organizational, and social) that together and separately influence how these technologies function in practice, the ways they are used, and the outcomes they produce. We then apply this analytical framework to welfare system databases, universal or ID number databases, and citizenship databases to demonstrate the value of this framework in both identifying and evaluating emergent or under-examined technologies in other sensitive social domains. Suspect Development Systems is an intervention in legal scholarship and practice, as it provides a much-needed definitional and analytical framework for understanding an ever-evolving ecosystem of technologies embedded and employed in modern governance. Our analysis also helps redirect attention toward important yet often under-examined contexts, conditions, and consequences that are pertinent to the development of meaningful legislative or regulatory interventions in the field of algorithmic accountability. The cross-jurisdictional evidence put forth across this Article illuminates the value of examining commonalities between the Global North and South to inform our understanding of how seemingly disparate technologies and contexts are in fact coaxial, which is the basis for building more global solidarity

AIDS among older children and adolescents in Southern Africa: projecting the time course and magnitude of the epidemic.

OBJECTIVE: An AIDS epidemic among older children and adolescents is clinically apparent in Southern Africa. We estimated the likely scale and time course of the epidemic in older survivors of vertical HIV infection. DESIGN: We modelled demographic, HIV prevalence, mother-to-child transmission and child survival data to project HIV burden among older children in two Southern African countries at different stages of severe HIV epidemics. Using measured survival data for children, we estimate that 64% of HIV-infected infants are fast progressors with median survival 0.64 years and 36% are slow progressors with median survival 16.0 years. We confirmed model validity by comparing model predictions to available epidemiological data. FINDINGS: Without treatment, HIV prevalence among 10-year-olds in South Africa is expected to increase from 2.1% in 2008 to 3.3% in 2020, whereas in Zimbabwe, it will decrease from 3.2% in 2008 to 1.6% in 2020. Deaths among untreated slow progressors will increase in South Africa from 7000/year in 2008 to 23 000/year in 2030, and in Zimbabwe from 8000/year in 2008 to peak at 9700/year in 2014. Drugs to prevent mother-to-child transmission could reduce death rate in 2030 to 8700/year in South Africa and to 2800/year in Zimbabwe in 2014. CONCLUSIONS: A substantial epidemic of HIV/AIDS in older survivors of mother-to-child transmission is emerging in Southern Africa. The lack of direct observations of survival in slow progressors has resulted in failure to anticipate the magnitude of the epidemic and to adequately address the clinical needs of HIV-infected older children and adolescents. Better HIV diagnostic and care services for this age group are urgently required

The Unfaithful Male in Monogamous and Non-Monogamous Marriage: A Phenomenological Case Study

A comparative case-study method grounded in phenomenology with the use of interpretive phenomenological analysis (IPA) demonstrated similarities and differences between two men’s experiences with engaging in extramarital affairs who were married to women in either a monogamous or a non-monogamous union. Despite the difference in their marital arrangements, both men were unfaithful to their wives and demonstrated similar themes in their lives, which included religious obligations, communication conflicts, loss of connection in marriage, deception, sexual restriction, absent fathers, compartmentalization of sexual behavior, guilt, and addiction. Differences included how men created boundaries, emotional connections with affair partners, power differentials, and sexual experiences