27 items found

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  • JournalArticle

    Toward Accountable Discrimination Aware Data Mining

    "Big Data" and data-mined inferences are affecting more and more of our lives, and concerns about their possible discriminatory effects are growing. Methods for...
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  • JournalArticle

    Solving the Black Box Problem. A Normative Framework for Explainable Artifici...

    Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how they work. Explainable Artificial...
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  • JournalArticle

    The fundamental rights challenges of algorithms

    Algorithms form an increasingly important part of our daily lives, even if we are often unaware of it. They are enormously useful in many different ways. They facilitate the...
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  • JournalArticle

    Seeing without knowing. Limitations of transparency and its application to al...

    Models for understanding and holding systems accountable have long rested upon ideals and logics of transparency. Being able to see a system is sometimes equated with being able...
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  • JournalArticle

    Recommender systems and their ethical challenges

    This article presents the first, systematic analysis of the ethical challenges posed by recommender systems through a literature review. The article identifies six areas of...
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  • JournalArticle

    Privacy in the clouds

    Informational self-determination refers to the right or ability of individuals to exercise personal control over the collection, use and disclosure of their personal data by...
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  • ConferencePaper

    Explanation of Deep Models with Limited Interaction for Trade Secret and Priv...

    An ever-increasing number of decisions affecting our lives are made by algorithms. For this reason, algorithmic transparency is becoming a pressing need: automated decisions...
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  • ConferencePaper

    Multi layered Explanations from Algorithmic Impact Assessments in the GDPR

    Impact assessments have received particular attention on both sides of the Atlantic as a tool for implementing algorithmic accountability. The aim of this paper is to address...
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  • JournalArticle

    Explanation in artificial intelligence. Insights from the social sciences

    There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to provide more transparency to their algorithms....
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  • JournalArticle

    Measuring discrimination in algorithmic decision making

    Society is increasingly relying on data-driven predictive models for automated decision making. This is not by design, but due to the nature and noisiness of observational...
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  • JournalArticle

    Machine Learning Explainability Via Microaggregation and Shallow Decision Trees

    Artificial intelligence (AI) is being deployed in missions that are increasingly critical for human life. To build trust in AI and avoid an algorithm-based authoritarian...
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  • BookChapter

    Machine Learning Explainability Through Comprehensible Decision Trees

    The role of decisions made by machine learning algorithms in our lives is ever increasing. In reaction to this phenomenon, the European General Data Protection Regulation...
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  • JournalArticle

    Ethical Value Centric Cybersecurity. A Methodology Based on a Value Graph

    Our society is being shaped in a non-negligible way by the technological advances of recent years, especially in information and communications technologies (ICTs). The...
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  • JournalArticle

    How the machine thinks. Understanding opacity in machine learning algorithms

    This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud...
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  • JournalArticle

    Grounds for Trust. Essential Epistemic Opacity and Computational Reliabilism

    Several philosophical issues in connection with computer simulations rely on the assumption that results of simulations are trustworthy. Examples of these include the debate...
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  • ConferencePaper

    Fairness and Abstraction in Sociotechnical Systems

    A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outcomes such as...
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  • JournalArticle

    Designing for human rights in AI

    In the age of Big Data, companies and governments are increasingly using algorithms to inform hiring decisions, employee management, policing, credit scoring, insurance...
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  • JournalArticle

    Democratizing Algorithmic Fairness

    Machine learning algorithms can now identify patterns and correlations in (big) datasets and predict outcomes based on the identified patterns and correlations. They can then...
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  • JournalArticle

    Big Data Ethics

    The speed of development in Big Data and associated phenomena, such as social media, has surpassed the capacity of the average consumer to understand his or her actions and...
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  • JournalArticle

    Bias in algorithmic filtering and personalization

    Online information intermediaries such as Facebook and Google are slowly replacing traditional media channels thereby partly becoming the gatekeepers of our society. To deal...
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