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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... -
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... -
The ambiguity of nestedness under soft and hard constraints
Many real networks feature the property of nestedness, i.e. the neighbours of nodes with a few connections are hierarchically nested within the neighbours of nodes with more... -
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... -
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... -
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... -
Second SoBigData Plus Plus Awareness Panel R. I. Platforms Data Protection an...
Data Protection and Research-
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