Jenifer Sunrise Winter in Digital Policy, Regulation and Governance: “This paper aims to assess the increasing challenges to governing the personal health information (PHI) essential for advancing artificial intelligence (AI) machine learning innovations in health care. Risks to privacy and justice/equity are discussed, along with potential solutions….
This paper argues that these characteristics of machine learning will overwhelm existing data governance approaches such as privacy regulation and informed consent. Enhanced governance techniques and tools will be required to help preserve the autonomy and rights of individuals to control their PHI. Debate among all stakeholders and informed critique of how, and for whom, PHI-fueled health AI are developed and deployed are needed to channel these innovations in societally beneficial directions.
Health data may be used to address pressing societal concerns, such as operational and system-level improvement, and innovations such as personalized medicine. This paper informs work seeking to harness these resources for societal good amidst many competing value claims and substantial risks for privacy and security….(More).
Chapter by Ronald C. Kessler et al: “…reviews the long history of using electronic medical records and other types of big data to predict suicide. Although a number of the most recent of these studies used machine learning (ML) methods, these studies were all suboptimal both in the features used as predictors and in the analytic approaches used to develop the prediction models. We review these limitations and describe opportunities for making improvements in future applications.
We also review the controversy among clinical experts about using structured suicide risk assessment tools (be they based on ML or older prediction methods) versus in-depth clinical evaluations of needs for treatment planning. Rather than seeing them as competitors, we propose integrating these different approaches to capitalize on their complementary strengths. We also emphasize the distinction between two types of ML analyses: those aimed at predicting which patients are at highest suicide risk, and those aimed at predicting the treatment options that will be best for individual patients. We explain why both are needed to optimize the value of big data ML methods in addressing the suicide problem….(More)”.
See also How Search Engine Data Enhance the Understanding of Determinants of Suicide in India and Inform Prevention: Observational Study.
Paper by Anthony Simonofski, Monique Snoeck and Benoît Vanderose: “As citizens have more and more opportunities to participate in public life, it is essential that administrations integrate this participation in their e-government processes. A smarter, more participatory, governance is a well-recognized and essential part of any city that wants to become “Smart” and generate public value. In this chapter, we will focus on the impact of this participatory approach on the development of e-government services by the city. Therefore, the goal of this chapter is to identify which methods administrations can apply to co-create their egovernment services with citizens and to understand the gap between the methods used in practice and citizens’ preferences.
As citizens have more and more opportunities to participate in public life, it is essential that administrations integrate this participation in their e-government processes. A smarter, more participatory, governance is a well-recognized and essential part of any city that wants to become “Smart” and generate public value. In this chapter, we will focus on the impact of this participatory approach on the development of e-government services by the city. Therefore, the goal of this chapter is to identify which methods administrations can apply to co-create their e-government services with citizens and to understand the gap between the methods used in practice and citizens’ preferences.
This chapter contributes to research and practice in different ways. First, the literature review allows the identification of eight participation methods to co-create e-government services. Second, we further examine these methods by means of 28 in-depth interviews, a questionnaire sent to public servants and a questionnaire sent to citizens. This multi-method approach allows identifying the barriers and drivers of public servants regarding the co-creation of e-government services but also the citizens’ perception of these methods. By contrasting the identified methods with their implementation, we better understand the discrepancies between literature and practice. At the same time, this chapter will give practitioners a repository of participation methods as well as information about the perception public servants and citizens have of them. Finally, we expect the insights provided in this chapter will stimulate research on the practical use of all these different methods…(More)”
Chapter by Claire Borsenberger, Mathilde Hoang and Denis Joram: “Thanks to appropriate data algorithms, firms, especially those on-line, are able to extract detailed knowledge about consumers and markets. This raises the question of the essential facility character of data. Moreover, the features of digital markets lead to a concentration of this core input in the hands of few big “superstars” and arouse legitimate economic and societal concerns. In a more and more data-driven society, one could ask if data openness is a solution to deal with power derived from data concentration. We conclude that only a case-by-case approach should be followed. Mandatory open data policy should be conditioned on an ex-ante cost-benefit analysis proving that the benefits of disclosure exceed its costs….(More)”.
Paper by C. Jara-Figueroa, Amy Z. Yu, and César A. Hidalgo: “Communication technologies, from printing to social media, affect our historical records by changing the way ideas are spread and recorded. Yet, finding statistical evidence of this fact has been challenging. Here we combine a common causal inference technique (instrumental variable estimation) with a dataset on nearly forty thousand biographies from Wikipedia (Pantheon 2.0), to study the effect of the introduction of printing in European cities on Wikipedia’s digital biographical records.
By using a city’s distance to Mainz as an instrument for the adoption of the movable type press, we show that European cities that adopted printing earlier were more likely to become the birthplace of a famous scientist or artist during the years following the invention of printing. We bring these findings to recent communication technologies by showing that the number of radios and televisions in a country correlates with the number of globally famous performing artists and sports players born in that country, even after controlling for GDP, population, and including country and year fixed effects. These findings support the hypothesis that the introduction of communication technologies can bias historical records in the direction of the content that is best suited for each technology….(More)”.
Sofia Ranchordas in the International Review of Law, Computers & Technology: “In the last decade, several smart cities throughout the world have started employing Internet of Things, big data, and algorithms to nudge citizens to save more water and energy, live healthily, use public transportation, and participate more actively in local affairs. Thus far, the potential and implications of data-driven nudges and behavioral insights in smart cities have remained an overlooked subject in the legal literature. Nevertheless, combining technology with behavioral insights may allow smart cities to nudge citizens more systematically and help these urban centers achieve their sustainability goals and promote civic engagement. For example, in Boston, real-time feedback on driving has increased road safety and in Eindhoven, light sensors have been used to successfully reduce nightlife crime and disturbance. While nudging tends to be well-intended, data-driven nudges raise a number of legal and ethical issues. This article offers a novel and interdisciplinary perspective on nudging which delves into the legal, ethical, and trust implications of collecting and processing large amounts of personal and impersonal data to influence citizens’ behavior in smart cities….(More)”.
Paper by Rashida Richardson, Jason Schultz, and Kate Crawford: “Law enforcement agencies are increasingly using algorithmic predictive policing systems to forecast criminal activity and allocate police resources. Yet in numerous jurisdictions, these systems are built on data produced within the context of flawed, racially fraught and sometimes unlawful practices (‘dirty policing’). This can include systemic data manipulation, falsifying police reports, unlawful use of force, planted evidence, and unconstitutional searches. These policing practices shape the environment and the methodology by which data is created, which leads to inaccuracies, skews, and forms of systemic bias embedded in the data (‘dirty data’). Predictive policing systems informed by such data cannot escape the legacy of unlawful or biased policing practices that they are built on. Nor do claims by predictive policing vendors that these systems provide greater objectivity, transparency, or accountability hold up. While some systems offer the ability to see the algorithms used and even occasionally access to the data itself, there is no evidence to suggest that vendors independently or adequately assess the impact that unlawful and bias policing practices have on their systems, or otherwise assess how broader societal biases may affect their systems.
In our research, we examine the implications of using dirty data with predictive policing, and look at jurisdictions that (1) have utilized predictive policing systems and (2) have done so while under government commission investigations or federal court monitored settlements, consent decrees, or memoranda of agreement stemming from corrupt, racially biased, or otherwise illegal policing practices. In particular, we examine the link between unlawful and biased police practices and the data used to train or implement these systems across thirteen case studies. We highlight three of these: (1) Chicago, an example of where dirty data was ingested directly into the city’s predictive system; (2) New Orleans, an example where the extensive evidence of dirty policing practices suggests an extremely high risk that dirty data was or will be used in any predictive policing application, and (3) Maricopa County where despite extensive evidence of dirty policing practices, lack of transparency and public accountability surrounding predictive policing inhibits the public from assessing the risks of dirty data within such systems. The implications of these findings have widespread ramifications for predictive policing writ large. Deploying predictive policing systems in jurisdictions with extensive histories of unlawful police practices presents elevated risks that dirty data will lead to flawed, biased, and unlawful predictions which in turn risk perpetuating additional harm via feedback loops throughout the criminal justice system. Thus, for any jurisdiction where police have been found to engage in such practices, the use of predictive policing in any context must be treated with skepticism and mechanisms for the public to examine and reject such systems are imperative….(More)”.
Paper by Monika Zalnieriute: “As the global policy-making capacity and influence of non-state actors in the digital age is rapidly increasing, the protection of fundamental human rights by private actors becomes one of the most pressing issues in Global Governance. This article combines business and human rights and digital constitutionalism discourses and uses the changing institutional context of Internet Governance and Internet Corporation for Assigned Names and Numbers (‘ICANN’) as an example to argue that economic incentives act against the voluntary protection of human rights by informal actors and regulatory structures in the digital era. It further contends that the global policy-making role and increasing regulatory power of informal actors such as ICANN necessitates reframing of their legal duties by subjecting them to directly binding human rights obligations in international law.
The article argues that such reframing is particularly important in the information age for three reasons. Firstly, it is needed to rectify an imbalance between hard legal commercial obligations and human rights soft law. This imbalance is well reflected in ICANNs policies. Secondly, binding obligations would ensure that individuals whose human rights have been affected can access an effective remedy. This is not envisaged under the new ICANN Bylaw on human rights precisely because of the fuzziness around the nature of ICANN’s obligations to respect internationally recognized human rights in its policies. Finally, the article suggests that because private actors such as ICANN are themselves engaging in the balancing exercise around such rights, an explicit recognition of their human rights obligations is crucial for the future development of access to justice in the digital age….(More)”.
Paper by Daniel Berliner, Alex Ingrams and Suzanne J. Piotrowski: “July 4, 2016 marked the fiftieth anniversary of the 1966 Freedom of Information Act of the United States. Freedom of Information (FOI) has become a vital element of the American political process, become recognized as a core value of democracy, and helped to inspire similar laws and movements around the world. FOI has always faced myriad challenges, including resistance, evasion, and poor implementation and enforcement. Yet the last decade has brought a change of a very different form to the evolution of FOI policy—the emergence of another approach to transparency that is in some ways similar to FOI, and in other ways distinct: open government. The open government agenda, driven by technological developments and motivated by a broader conception of transparency, today rivals, or by some measures, even eclipses FOI in terms of political attention and momentum. What have been the consequences of these trends? How does the advent of new technologies and new agendas shape the transparency landscape?
The political and policy contexts for FOI have fundamentally shifted due to the rise of the open government reform agenda. FOI was at one point the primary tool used to promote governance transparency. FOI is now just one good governance tool in an increasingly crowded field of transparency policy areas. Focus is increasingly shifting toward technology-enabled open data reforms. While many open government reformers see these as positive developments, many traditional FOI proponents have raised concerns. With a few notable exceptions, the academic literature has been silent on this issue. We offer a systematic framework for understanding the potential consequences—both positive and negative—of the open government agenda for FOI policy and implementation….(More)”.
Paper by Andreas Rasche, Mette Morsing and Erik Wetter in Business and Society: “This article examines the legitimacy attached to different types of multi-stakeholder data partnerships occurring in the context of sustainable development. We develop a framework to assess the democratic legitimacy of two types of data partnerships: open data partnerships (where data and insights are mainly freely available) and closed data partnerships (where data and insights are mainly shared within a network of organizations). Our framework specifies criteria for assessing the legitimacy of relevant partnerships with regard to their input legitimacy as well as their output legitimacy. We demonstrate which particular characteristics of open and closed partnerships can be expected to influence an analysis of their input and output legitimacy….(More)”.