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  • ConferencePaper

    Explaining Image Classifiers Generating Exemplars and Counter-Exemplars from ...

    We present an approach to explain the decisions of black-box image classifiers through synthetic exemplar and counter-exemplar learnt in the latent feature space. Our...
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  • ConferencePaper

    Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exem...

    We present xspells, a model-agnostic local approach for explaining the decisions of a black box model for sentiment classification of short texts. The explanations provided...
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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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  • 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

    Explaining Any Time Series Classifier

    We present a method to explain the decisions of black box models for time series classification. The explanation consists of factual and counterfactual shapelet-based rules...
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  • ConferencePaper

    Interpretable Next Basket Prediction Boosted with Representative Recipes

    Food is an essential element of our lives, cultures, and a crucial part of human experience. The study of food purchases can drive the design of practical services such as...
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  • ConferencePaper

    Heterogeneous Document Embeddings for Cross-Lingual Text Classification

    Funnelling (Fun) is a method for cross-lingual text classification (CLC) based on a two-tier ensemble for heterogeneous transfer learning. In Fun, 1st-tier classifiers, each...
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  • ConferencePaper

    A comparative study of fairness enhancing interventions in machine learning

    Computers are increasingly used to make decisions that have significant impact on people's lives. Often, these predictions can affect different population subgroups...
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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

    Interpretable socioeconomic status inference from aerial imagery through urba...

    Urbanization is a great challenge for modern societies, promising better access to economic opportunities, but widening socioeconomic inequalities. Accurately tracking this...
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  • ConferencePaper

    Algorithmic decision making and the cost of fairness

    Algorithms are now regularly used to decide whether defendants awaiting trial are too dangerous to be released back into the community. In some cases, black defendants are...
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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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  • JournalArticle

    Accountability for the Use of Algorithms in a Big Data Environment

    Accountability is the ability to provide good reasons in order to explain and to justify actions, decisions, and policies for a (hypothetical) forum of persons or...
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  • JournalArticle

    Algorithmic Accountability and Public Reason

    The ever-increasing application of algorithms to decision-making in a range of social contexts has prompted demands for algorithmic accountability. Accountable decision-makers...
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  • TrainingMaterial

    Introduction to Data science for Social Scientists

    This course, initially designed for social scientists, covers several topics of data science. Python programming Data Cleaning and Transformation Classification Clustering...
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  • TrainingMaterial

    Can Big Data Bridge Gaps in Migration Statistics

    Traditional statistical data on international migration suffers from the problems (gaps) of inconsistency in definitions, differences in geographical coverages, absence of...
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  • ConferencePaper

    A qualitative exploration of perceptions of algorithmic fairness

    Algorithmic systems increasingly shape information people are exposed to as well as influence decisions about employment, finances, and other opportunities. In some cases,...
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  • JournalArticle

    AI and Big Data A blueprint for a human rights and social and ethical impact ...

    Building on studies of the collective dimension of data protection, this article sets out to embed this new perspective in an assessment model centred on human rights (Human...
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  • JournalArticle

    European Data Governance Act

    The proposal for a Regulation of the European Parliament and of the Council on data governance is the first of a set of measures announced in the 2020 European strategy for...
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  • JournalArticle

    On Artificial Intelligence - A European approach to excellence and trust

    Artificial Intelligence is developing fast. It will change our lives by improving healthcare (e.g. making diagnosis more precise, enabling better prevention of diseases),...
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