51 items found

Groups: Ethics and Legality

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  • Dataset

    Papers on Gender Bias in Academic Promotions

    This dataset contains the result of a systematic mapping study conducted to analyse how the issue of gender bias in academic promotions has been addressed by the literature....
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  • Application

    MANILA

    MANILA is a low-code web application to support the specification and execution of machine learning fairness evaluations. In particular, through MANILA it is possible to...
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  • ConferencePaper

    Democratizing Quality-Based Machine Learning Development through Extended Fea...

    ML systems have become an essential tool for experts of many domains, data scientists and researchers, allowing them to find answers to many complex business questions...
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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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  • 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

    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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  • 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

    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

    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

    Fair Prediction with Disparate Impact A Study of Bias in Recidivism Predictio...

    Recidivism prediction instruments (RPIs) provide decision-makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time....
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  • JournalArticle

    Algorithmic Decision Making Based on ML from Big Data. Can Transparency Resto...

    Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would...
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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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  • 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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  • Method

    Python library for direct and indirect discrimination prevention in data mining

    This python library implements the discrimination discovery and prevention method proposed in the paper: “A methodology for direct and indirect discrimination prevention in...
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  • Experiment

    Label flipping attacks in Federated Learning

    The following experiments showcase Federated Learning using Scikit-learn.
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  • Method

    privlib

    A python library for assessing and mitigating privacy and discrimination risk in tabular and sequential data
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    Deliverable

    Private Deliverable D2.3 Report on WP2 activities

  • JournalArticle

    An ethico-legal framework for social data science

    This paper presents a framework for research infrastructures enabling ethically sensitive and legally compliant data science in Europe. Our goal is to describe how to design...
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