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Master's Thesis from the year 2020 in the subject Organisation and administration - Public administration, grade: 1,1, Free University of Berlin (Otto-Suhr-Institut), language: English, abstract: The paper is divided in two main parts. The first part introduces the agency theory and its application to two relevant aspects: the agency theory in the public sector and the agency theory involving artificial agents. The second part aims at providing answers to the research questions, by discussing the changes in the agency of the public administrations, as well as the changes in the control methods used to monitor these administrations. Finally, the conclusion summarizes the answer to the research questions, exposes the implications and limits of this paper and offers leads for possible future research on this topic. Automated decision-making (ADM), a type of algorithm which supports decision-making and combines advanced analytics and data minig to make predictions, has been developed in various public sector fields, from predictive policing to healthcare, and is increasingly helping public agents by delivering analysis that they can leverage to make their decisions. This technique involves three main stakeholders: the programmer of the algorithmic system; the user, who is the public agent operating the ADM system; and the individuals affected by the decisions made using ADM. This paper focuses on the consequences on the governance and responsibility of administrations increasingly relying on algorithms to make their decisions. Does the introduction of ADM in public administrations transform their agency? If so, why does this change occur and how does it impact the control methods required to supervise the actions of administrations? The chosen approach is the agency theory, which is suited to deal with delegation, specifically between actors from different contextual backgrounds. France has been chosen as the case-study for this topic, as it has put in place relevant laws and public institutions in order to deal with public ADM. The method chosen to investigate this issue is based on a literature review, as it is appropriate to approach a case-study. This includes scientific papers for the technical aspects, from computer sciences to social and political sciences, as well as reports from governments, international institutions and private companies. More general literature, such as articles and blog posts are used for information on the use of ADM in France and the public debate surrounding it. Finally, the methodology also includes semi-structured interviews led with experts working on the topic of ADM in the public sector.
The world is changing at a fast pace, so is the Government and Governance style. Humans are bound to go for Algorithmic strategies rather than manual or electronic ones in different domains. This book introduces the Algorithmic Government or Government by Algorithm, which refers to authorizing machines in the Public Sector for automated decision-making based on Artificial Intelligence, Data Science, and other technologies. It is an emerging concept introduced globally and will be considered revolutionary in the future. The book covers concepts, applications, progress status, and potential use-cases of Algorithmic Government. This book serves as introductory material for the readers from technology, public policy, administration, and management fields.
This edited volume highlights the latest advances in and findings from research on service automation in public sector organizations. The contributing authors use a mix of social and technological approaches to increase readers’ understanding of public service automation. The respective chapters discuss the automation of services in public organizations from a conceptual standpoint, present empirical examples of automation applications in public organizations, and consider the implementation-related challenges that can arise. The book’s overall goal is to aid and inspire researchers and practitioners to expand their knowledge of service automation in public organizations, while also providing a foundation for policy development and future research. Following a brief introductory chapter, the book addresses major gaps in our current understanding of service automation in public organizations, and provides suggestions for future research. Moreover, it argues that there is a continued need to observe and learn from empirical examples, and a need for more critical studies on the social and societal consequences of increased service automation in public organizations.
This book argues that ethical evaluation of AI should be an integral part of public service ethics and that an effective normative framework is needed to provide ethical principles and evaluation for decision-making in the public sphere, at both local and international levels. It introduces how the tenets of prudential rationality ethics, through critical engagement with intersectionality, can contribute to a more successful negotiation of the challenges created by technological innovations in AI and afford a relational, interactive, flexible and fluid framework that meets the features of AI research projects, so that core public and individual values are still honoured in the face of technological development. This book will be of key interest to scholars, students, and professionals engaged in public management and ethics management, AI ethics, public organizations, public service leadership and more broadly to public administration and policy, as well as applied ethics and philosophy.
This book gives a comprehensive overview of the state of Artificial Intelligence (AI), especially machine learning (ML) applications in public service delivery in Estonia, discussing the manifold ethical and legal issues that arise under both European and Estonian law. Final conclusions and recommendations set out and analyze various policy options for the public sector, taking into account recent developments at the European level – such as the AIA proposal – as well as the experience of countries that have issued principles and guidelines or even laws for the use of ML in the public sector. “For two reasons, this study is relevant not only for an audience which is interested in Estonian administrative law. First, the authors base their legal analysis primarily on EU law and provide a state of the art-analysis of the relevant secondary legislation. This makes the book a reference text for the European debate on public sector AI governance. Second, this study is part of a larger research project in which four specific use cases of public sector AI have been developed and tested. The practical insights gained in these projects have provided the authors with an excellent understanding of the opportunities and risks of the technology, which distinguishes this legal analysis from similar enterprises.” Excerpt from the foreword by Professor Thomas Wischmeyer (University of Bielefeld)
Artificial intelligence is increasingly being used to make decisions about human welfare. Automated decision systems (ADS) administer U.S. social benefits programs--such as unemployment and disability benefits--across local, state, and Federal governments. While ADS have the potential to enable large gains in efficiency, they also run a high risk of reinforcing the class- and race-based inequities of the status quo. Additionally, the use of these systems is not transparent, often leaving individuals with no meaningful recourse after a decision has been made. Individuals may not even know that ADS played a role in the decision-making process.The Federal Government should take immediate action to promote the transparency and accountability of automated decision systems. Agencies must build internal technical capacity as well as data cultures centered around transparency, accountability, and fairness. The White House should require that agencies using ADS undertake a notice-and-comment process to disclose information about these systems to the public. Finally, in the long-term, Congress must pass comprehensive legislation to implement a single, national standard regulating the use of ADS across sectors and use cases.
This pioneering Research Handbook on Public Management and Artificial Intelligence provides a comprehensive overview of the potentials, challenges, and governance principles of AI in a public management context. Multidisciplinary in approach, it draws on a variety of jurisdictional perspectives and expertly analyses key topics relating to this socio-technical phenomenon.
In today’s global culture where the internet has established itself as a main tool of communication, the global system of economy and regulations, as well as data and decisions based on data analysis, have become essential for public actors and institutions. Governments need to be updated and use the latest technologies to understand what society’s demands are, and user behavioral data, which can be pulled by intelligent applications, can offer tremendous insights into this. The Handbook of Research on Artificial Intelligence in Government Practices and Processes identifies definitional perspectives of behavioral data science and what its use by governments means for automation, predictability, and risks to privacy and free decision making in society. Many governments can train their algorithms to work with machine learning, leading to the capacity to interfere in the behavior of society and potentially achieve a change in societal behavior without society itself even being aware of it. As such, the use of artificial intelligence by governments has raised concerns about privacy and personal security issues. Covering topics such as digital democracy, data extraction techniques, and political communications, this book is an essential resource for data analysts, politicians, journalists, public figures, executives, researchers, data specialists, communication specialists, digital marketers, and academicians.