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  1. Transparency in Algorithmic and Human Decision-Making: Is There a Double Standard?John Zerilli, Alistair Knott, James Maclaurin & Colin Gavaghan - 2019 - Philosophy and Technology 32 (4):661-683.
    We are sceptical of concerns over the opacity of algorithmic decision tools. While transparency and explainability are certainly important desiderata in algorithmic governance, we worry that automated decision-making is being held to an unrealistically high standard, possibly owing to an unrealistically high estimate of the degree of transparency attainable from human decision-makers. In this paper, we review evidence demonstrating that much human decision-making is fraught with transparency problems, show in what respects AI fares little worse or better and argue that (...)
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  • The Digital Phenotype: A Philosophical and Ethical Exploration.Michele Loi - 2019 - Philosophy and Technology 32 (1):155-171.
    The concept of the digital phenotype has been used to refer to digital data prognostic or diagnostic of disease conditions. Medical conditions may be inferred from the time pattern in an insomniac’s tweets, the Facebook posts of a depressed individual, or the web searches of a hypochondriac. This paper conceptualizes digital data as an extended phenotype of humans, that is as digital information produced by humans and affecting human behavior and culture. It argues that there are ethical obligations to persons (...)
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  • Axiological Futurism: The Systematic Study of the Future of Values.John Danaher - forthcoming - Futures.
    Human values seem to vary across time and space. What implications does this have for the future of human value? Will our human and (perhaps) post-human offspring have very different values from our own? Can we study the future of human values in an insightful and systematic way? This article makes three contributions to the debate about the future of human values. First, it argues that the systematic study of future values is both necessary in and of itself and an (...)
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  • Social Impacts of Algorithmic Decision-Making: A Research Agenda for the Social Sciences.Frauke Kreuter, Christoph Kern, Ruben L. Bach & Frederic Gerdon - 2022 - Big Data and Society 9 (1).
    Academic and public debates are increasingly concerned with the question whether and how algorithmic decision-making may reinforce social inequality. Most previous research on this topic originates from computer science. The social sciences, however, have huge potentials to contribute to research on social consequences of ADM. Based on a process model of ADM systems, we demonstrate how social sciences may advance the literature on the impacts of ADM on social inequality by uncovering and mitigating biases in training data, by understanding data (...)
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  • Co-Designing Algorithms for Governance: Ensuring Responsible and Accountable Algorithmic Management of Refugee Camp Supplies.Mark van Embden Andres, S. Ilker Birbil, Paul Koot & Rianne Dekker - 2022 - Big Data and Society 9 (1).
    There is increasing criticism on the use of big data and algorithms in public governance. Studies revealed that algorithms may reinforce existing biases and defy scrutiny by public officials using them and citizens subject to algorithmic decisions and services. In response, scholars have called for more algorithmic transparency and regulation. These are useful, but ex post solutions in which the development of algorithms remains a rather autonomous process. This paper argues that co-design of algorithms with relevant stakeholders from government and (...)
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  • Governing Algorithmic Decisions: The Role of Decision Importance and Governance on Perceived Legitimacy of Algorithmic Decisions.Kirsten Martin & Ari Waldman - 2022 - Big Data and Society 9 (1).
    The algorithmic accountability literature to date has primarily focused on procedural tools to govern automated decision-making systems. That prescriptive literature elides a fundamentally empirical question: whether and under what circumstances, if any, is the use of algorithmic systems to make public policy decisions perceived as legitimate? The present study begins to answer this question. Using factorial vignette survey methodology, we explore the relative importance of the type of decision, the procedural governance, the input data used, and outcome errors on perceptions (...)
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  • Promises and Pitfalls of Algorithm Use by State Authorities.Maryam Amir Haeri, Kathrin Hartmann, Jürgen Sirsch, Georg Wenzelburger & Katharina A. Zweig - 2022 - Philosophy and Technology 35 (2):1-31.
    Algorithmic systems are increasingly used by state agencies to inform decisions about humans. They produce scores on risks of recidivism in criminal justice, indicate the probability for a job seeker to find a job in the labor market, or calculate whether an applicant should get access to a certain university program. In this contribution, we take an interdisciplinary perspective, provide a bird’s eye view of the different key decisions that are to be taken when state actors decide to use an (...)
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  • Are Algorithmic Decisions Legitimate? The Effect of Process and Outcomes on Perceptions of Legitimacy of AI Decisions.Kirsten Martin & Ari Waldman - forthcoming - Journal of Business Ethics:1-18.
    Firms use algorithms to make important business decisions. To date, the algorithmic accountability literature has elided a fundamentally empirical question important to business ethics and management: Under what circumstances, if any, are algorithmic decision-making systems considered legitimate? The present study begins to answer this question. Using factorial vignette survey methodology, we explore the impact of decision importance, governance, outcomes, and data inputs on perceptions of the legitimacy of algorithmic decisions made by firms. We find that many of the procedural governance (...)
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  • On the Ethics of Biodiversity Models, Forecasts and Scenarios.Pierre Mazzega - 2018 - Asian Bioethics Review 10 (4):295-312.
    The development of numerical models to produce realistic prospective scenarios for the evolution of biological diversity is essential. Only integrative impact assessment models are able to take into account the diverse and complex interactions embedded in social-ecological systems. The knowledge used is objective, the procedure of their integration is rigorous and the data massive. Nevertheless, the technical choices made at each stage of the development of models and scenarios are mostly circumstantial, depending on both the skills of modellers on a (...)
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  • Beyond Mystery: Putting Algorithmic Accountability in Context.Andrea Ballestero, Baki Cakici & Elizabeth Reddy - 2019 - Big Data and Society 6 (1).
    Critical algorithm scholarship has demonstrated the difficulties of attributing accountability for the actions and effects of algorithmic systems. In this commentary, we argue that we cannot stop at denouncing the lack of accountability for algorithms and their effects but must engage the broader systems and distributed agencies that algorithmic systems exist within; including standards, regulations, technologies, and social relations. To this end, we explore accountability in “the Generated Detective,” an algorithmically generated comic. Taking up the mantle of detectives ourselves, we (...)
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  • Big data, algorithms and politics: the social sciences in the era of social media.Felipe González - 2019 - Cinta de Moebio 65:267-280.
    Resumen: El presente artículo ofrece un estado del arte de cómo se ha venido a estudiar empíricamente la relación entre política y redes sociales en la última década, desde el punto de vista de la naturaleza del objeto de estudio, las nuevas técnicas de análisis y métodos sobre las que se han apoyado las ciencias sociales, las agendas de investigación a que ha dado lugar y algunos de los dilemas éticos que suscita. El artículo consta de tres partes. Primero, desarrollamos (...)
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  • New Pythias of public administration: ambiguity and choice in AI systems as challenges for governance.Fernando Filgueiras - forthcoming - AI and Society:1-14.
    As public administrations adopt artificial intelligence, we see this transition has the potential to transform public service and public policies, by offering a rapid turnaround on decision making and service delivery. However, a recent series of criticisms have pointed to problematic aspects of mainstreaming AI systems in public administration, noting troubled outcomes in terms of justice and values. The argument supplied here is that any public administration adopting AI systems must consider and address ambiguities and uncertainties surrounding two key dimensions: (...)
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  • A Taxonomy of Human–Machine Collaboration: Capturing Automation and Technical Autonomy.Monika Simmler & Ruth Frischknecht - 2021 - AI and Society 36 (1):239-250.
    Due to the ongoing advancements in technology, socio-technical collaboration has become increasingly prevalent. This poses challenges in terms of governance and accountability, as well as issues in various other fields. Therefore, it is crucial to familiarize decision-makers and researchers with the core of human–machine collaboration. This study introduces a taxonomy that enables identification of the very nature of human–machine interaction. A literature review has revealed that automation and technical autonomy are main parameters for describing and understanding such interaction. Both aspects (...)
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