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Alba Soriano Arnanz
Data protection for the prevention of algorithmic discrimination
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Summary
Acknowledgements
Preface
Introduction
Chapter I An introduction to algorithmic decision-making
1. Big data
1.1. The three Vs in big data
1.2. The need to process (raw) big data
1.3. The fourth V: value
2. Data processing tools and technologies 2.1. Machine learning and data mining
2.2. Supervised and unsupervised learning
2.3. Algorithms and models
3. The application of automated systems
3.1. The use of algorithms by the private sector
3.1.1. Scoring individuals
3.1.1.1. The banking sector and the expansion of credit scores
3.1.1.2. Healthcare
3.1.1.3. Human resources
3.1.2. Consumer profiling and advertising
3.2. The use of algorithms by the public sector
3.2.1. The use of algorithms in public service management and provision
3.2.1.1. The use of algorithms in public aid and welfare programmes
3.2.1.2. Automation of public services, aid and welfare programmes and the perpetuation of inequality
3.2.2. The use of algorithms in public administration’s regulatory and coercive activity: law enforcement 3.2.2.1. Police departments and the criminal justice system
3.2.2.2. Other algorithmic applications in the exercise of administrative regulatory and coercive powers
4. Types of algorithmic decision-making
4.1. Automatic and autonomous systems
4.2. Automated and semi-automated systems
4.3. Profiling and automated decision-making: descriptive, predictive, classification and recommendation purposes
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