200 research outputs found

    The Perils of Ignoring Data Suitability: The Suitability of Data Used to Train Neural Networks Deserves More Attention

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    The quality and quantity (we call it suitability from now on) of data that are used for a machine learning task are as important as the capability of the machine learning algorithm itself. Yet these two aspects of machine learning are not given equal weight by the data mining, machine learning and neural computing communities. Data suitability is largely ignored compared to the effort expended on learning algorithm development. This position paper argues that some of the new algorithms and many of the tweaks to existing algorithms would be unnecessary if the data going into them were properly pre-processed, and calls for a shift in effort towards data suitability assessment and correction

    Negotiating with social algorithms in the design of service personalization

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    Supported by three standalone yet complimentary essays, this thesis investigates the development of service personalization that has been mediated by technologies characterized as having elements of Artificial Intelligence (AI), including prediction, natural language processing, and machine learning. The aim of this work is to expand our understanding of the role emerging technologies play in affording personalization, and personalization’s relationship with systems increasingly capable of mediating experiences directly with users. Data was collected from participant observation of an AI development company over two and a half years and comprised of a detailed mapping of the technologies as well as development documents, chats, meetings, and interviews with developers and key users. We found that the implementation of deeper forms of personalization over time led to the adoption of emerging technologies like AI. In the context of a government agency, these algorithms changed the way employees are screened and selected. We also found that requests for personalization led to increasingly opaque systems where interpretation about how algorithms work emerges in place of an explanation of how they work. Building upon these findings, a framework was developed to investigate 34 discrete cases of personalization across dimensions of ease of design and ease of understanding. We found that the pursuit of deeper personalization leads to the adoption of tools that make increasingly social decisions. That is, we utilize social technologies despite their complexity because they make faster and deeper decisions about individuals from social data than can be done without them. To accomplish this, various strategies are employed to help increase user tolerance for a lack of understanding of their inner workings and to ensure they operate within bounds acceptable to users and the designers of the systems. As these systems gain increasing autonomy, issues of bias amplification, privacy, and an increasingly inexplainable logic behind decision-making remain persistent and this has implications for theory and practice

    Narrative and Hypertext 2011 Proceedings: a workshop at ACM Hypertext 2011, Eindhoven

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    Analytics and Intuition in the Process of Selecting Talent

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    In management, decisions are expected to be based on rational analytics rather than intuition. But intuition, as a human evolutionary achievement, offers wisdom that, despite all the advances in rational analytics and AI, should be used constructively when recruiting and winning personnel. Integrating these inner experiential competencies with rational-analytical procedures leads to smart recruiting decisions

    Technologies on the stand:Legal and ethical questions in neuroscience and robotics

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    Analytics and Intuition in the Process of Selecting Talent

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    In management, decisions are expected to be based on rational analytics rather than intuition. But intuition, as a human evolutionary achievement, offers wisdom that, despite all the advances in rational analytics and AI, should be used constructively when recruiting and winning personnel. Integrating these inner experiential competencies with rational-analytical procedures leads to smart recruiting decisions

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
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