6,278 research outputs found

    POTs: Protective Optimization Technologies

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    Algorithmic fairness aims to address the economic, moral, social, and political impact that digital systems have on populations through solutions that can be applied by service providers. Fairness frameworks do so, in part, by mapping these problems to a narrow definition and assuming the service providers can be trusted to deploy countermeasures. Not surprisingly, these decisions limit fairness frameworks' ability to capture a variety of harms caused by systems. We characterize fairness limitations using concepts from requirements engineering and from social sciences. We show that the focus on algorithms' inputs and outputs misses harms that arise from systems interacting with the world; that the focus on bias and discrimination omits broader harms on populations and their environments; and that relying on service providers excludes scenarios where they are not cooperative or intentionally adversarial. We propose Protective Optimization Technologies (POTs). POTs provide means for affected parties to address the negative impacts of systems in the environment, expanding avenues for political contestation. POTs intervene from outside the system, do not require service providers to cooperate, and can serve to correct, shift, or expose harms that systems impose on populations and their environments. We illustrate the potential and limitations of POTs in two case studies: countering road congestion caused by traffic-beating applications, and recalibrating credit scoring for loan applicants.Comment: Appears in Conference on Fairness, Accountability, and Transparency (FAT* 2020). Bogdan Kulynych and Rebekah Overdorf contributed equally to this work. Version v1/v2 by Seda G\"urses, Rebekah Overdorf, and Ero Balsa was presented at HotPETS 2018 and at PiMLAI 201

    Planning and Leveraging Event Portfolios: Towards a Holistic Theory

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    This conceptual paper seeks to advance the discourse on the leveraging and legacies of events by examining the planning, management, and leveraging of event portfolios. This examination shifts the common focus from analyzing single events towards multiple events and purposes that can enable cross-leveraging among different events in pursuit of attainment and magnification of specific ends. The following frameworks are proposed: (1) event portfolio planning and leveraging, and (2) analyzing events networks and inter-organizational linkages. These frameworks are intended to provide, at this infancy stage of event portfolios research, a solid ground for building theory on the management of different types and scales of events within the context of a portfolio aimed to obtain, optimize and sustain tourism, as well as broader community benefits

    Predicting electronic structures at any length scale with machine learning

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    The properties of electrons in matter are of fundamental importance. They give rise to virtually all molecular and material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets. Modeling and simulation of such diverse applications rely primarily on density functional theory (DFT), which has become the principal method for predicting the electronic structure of matter. While DFT calculations have proven to be very useful to the point of being recognized with a Nobel prize in 1998, their computational scaling limits them to small systems. We have developed a machine learning framework for predicting the electronic structure on any length scale. It shows up to three orders of magnitude speedup on systems where DFT is tractable and, more importantly, enables predictions on scales where DFT calculations are infeasible. Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances science to frontiers intractable with any current solutions. This unprecedented modeling capability opens up an inexhaustible range of applications in astrophysics, novel materials discovery, and energy solutions for a sustainable future

    Multiscale modeling and deep learning: reverse-mapping of condensed-phase molecular structures

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    The enactment of plural leadership in a health and social care network : the influence of institutional context

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    In this article we employ developments in social network analysis (SNA), specifically the p* model, to examine the enactment of plural leadership within, and across, hierarchical levels and organizational boundaries (Denis et al., 2012). Drawing on an empirical study of an inter-professional, inter-organizational network (number of nodes = 23) that delivers health and social care, we address two research gaps: (i) the effect of power relations, derived from professional hierarchy, upon spread of plural leadership; and (ii) the effect of formal leadership, derived from managerial accountability, in channeling the spread of plural leadership for coherent strategic effect. We show that, in a routine situation, the network is characterized by generalized leadership exchanges. In this situation, professional hierarchy and managerial accountability are not visible, nor is channeling of plural leadership by the formal leader. In a non-routine situation, when a disruptive event occurs, the network is characterized by restricted exchange. In this situation, professional hierarchy and managerial accountability are evident, and a formal leader channels plural leadership
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