810 research outputs found

    Society-in-the-Loop: Programming the Algorithmic Social Contract

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    Recent rapid advances in Artificial Intelligence (AI) and Machine Learning have raised many questions about the regulatory and governance mechanisms for autonomous machines. Many commentators, scholars, and policy-makers now call for ensuring that algorithms governing our lives are transparent, fair, and accountable. Here, I propose a conceptual framework for the regulation of AI and algorithmic systems. I argue that we need tools to program, debug and maintain an algorithmic social contract, a pact between various human stakeholders, mediated by machines. To achieve this, we can adapt the concept of human-in-the-loop (HITL) from the fields of modeling and simulation, and interactive machine learning. In particular, I propose an agenda I call society-in-the-loop (SITL), which combines the HITL control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems, and monitoring compliance with the agreement. In short, `SITL = HITL + Social Contract.'Comment: (in press), Ethics of Information Technology, 201

    Sustainability Standards and Stakeholder Engagement: Lessons From Carbon Markets

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    Stakeholders play an increasingly active role in private governance, including development of standards for measuring sustainability. Building on prior studies focused on standards and stakeholder engagement, we use an innovation management theoretical lens to compare stakeholder engagement and standards developed in two carbon markets: the Climate Action Reserve and the U.N.’s Clean Development Mechanism. We develop and test hypotheses regarding how different processes of stakeholder engagement in standard development affect the number, identity, and age of stakeholders involved, as well as the variation and quality of the resulting standards. In doing so, we contribute to the growing literature on stakeholder engagement in developing sustainability standards

    Moral Machine or Tyranny of the Majority?

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    With Artificial Intelligence systems increasingly applied in consequential domains, researchers have begun to ask how these systems ought to act in ethically charged situations where even humans lack consensus. In the Moral Machine project, researchers crowdsourced answers to "Trolley Problems" concerning autonomous vehicles. Subsequently, Noothigattu et al. (2018) proposed inferring linear functions that approximate each individual's preferences and aggregating these linear models by averaging parameters across the population. In this paper, we examine this averaging mechanism, focusing on fairness concerns in the presence of strategic effects. We investigate a simple setting where the population consists of two groups, with the minority constituting an {\alpha} < 0.5 share of the population. To simplify the analysis, we consider the extreme case in which within-group preferences are homogeneous. Focusing on the fraction of contested cases where the minority group prevails, we make the following observations: (a) even when all parties report their preferences truthfully, the fraction of disputes where the minority prevails is less than proportionate in {\alpha}; (b) the degree of sub-proportionality grows more severe as the level of disagreement between the groups increases; (c) when parties report preferences strategically, pure strategy equilibria do not always exist; and (d) whenever a pure strategy equilibrium exists, the majority group prevails 100% of the time. These findings raise concerns about stability and fairness of preference vector averaging as a mechanism for aggregating diverging voices. Finally, we discuss alternatives, including randomized dictatorship and median-based mechanisms.Comment: To appear in the proceedings of AAAI 202

    CHARACTERIZING ENABLING INNOVATIONS AND ENABLING THINKING

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    The pursuit of innovation is engrained throughout society whether in business via the introduction of offerings, non-profits in their mission-driven initiatives, universities and agencies in their drive for discoveries and inventions, or governments in their desire to improve the quality of life of their citizens. Yet, despite these pursuits, innovations with long-lasting, significant impact represent an infrequent outcome in most domains. The seemingly random nature of these results stems, in part, from the definitions of innovation and the models based on such definitions. Although there is debate on this topic, a comprehensive and pragmatic perspective developed in this work defines innovation as the introduction of a novel or different idea into practice that has a positive impact on society. To date, models of innovation have focused on, for example, new technological advances, new approaches to connectivity in systems, new conceptual frameworks, or even new dimensions of performance - all effectively building on the first half of the definition of innovation and encouraging its pursuit based on the novelty of ideas. However, as explored herein, achieving profound results by innovating on demand might require a perspective that focuses on the impact of an innovation. In this view, innovation does not only entail doing new things, but consciously driving them towards achieving impact through proactive design behaviors. Explicit consideration of the impact dimension in innovation models has been missing, even though it may arguably be the most important since it represents the outcome of innovation

    Citizens AND HYdrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges

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    Widely available digital technologies are empowering citizens who are increasingly well informed and involved in numerous water, climate, and environmental challenges. Citizen science can serve many different purposes, from the "pleasure of doing science" to complementing observations, increasing scientific literacy, and supporting collaborative behaviour to solve specific water management problems. Still, procedures on how to incorporate citizens' knowledge effectively to inform policy and decision-making are lagging behind. Moreover, general conceptual frameworks are unavailable, preventing the widespread uptake of citizen science approaches for more participatory cross-sectorial water governance. In this work, we identify the shared constituents, interfaces, and interlinkages between hydrological sciences and other academic and non-academic disciplines in addressing water issues. Our goal is to conceptualize a transdisciplinary framework for valuing citizen science and advancing the hydrological sciences. Joint efforts between hydrological, computer, and social sciences are envisaged for integrating human sensing and behavioural mechanisms into the framework. Expanding opportunities of online communities complement the fundamental value of on-site surveying and indigenous knowledge. This work is promoted by the Citizens AND HYdrology (CANDHY) Working Group established by the International Association of Hydrological Sciences (IAHS)

    Model(ing) Privacy: Empirical Approaches to Privacy Law and Governance

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    Model(ing) Privacy: Empirical Approaches to Privacy Law and Governanc

    How open is innovation? A retrospective and ideas forward

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    This paper sheds fresh light on our 2010 paper How Open Is Innovation by taking into consideration notable developments in innovation over the last decade. The original paper developed four types of openness: sourcing, acquiring, selling, and revealing. Reflecting on important technological, organizational, and societal changes in the past decade, we highlight how these changes prompt novel questions for open innovation. While the core features of the original framework still stands, there are many new questions that have emerged in recent years. We end by charting a path for future research that emphasizes opportunities, costs and tradeoffs between different modes of open innovation, the need to better understand the nature of data, new organizational designs and legal instruments, and multilevel aspects and relationships that affect the extent and nature of openness
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