Acknowledgements	5
	Contents	6
	Authors	8
	Part I: Opportunities of Data Mining and Profiling	18
	Data Dilemmas in the Information Society:	Data Dilemmas in the Information Society:
	19	19
	The Information Society	19
	What This Book Is About	20
	Responsible Innovation	21
	Data Mining and Profiling	23
	Data Mining: A Step in the KDD-Process	23
	From Data to Knowledge	26
	Profiles of Individuals and of Groups	28
	Why We Need These Tools	29
	Discrimination, Privacy and Other Issues	31
	Any News?	31
	Problems and Solutions	33
	Structure of This Book	34
	Part I: Opportunities of Data Mining and Profiling	34
	Part II: Possible Discrimination and Privacy Issues	35
	Part III: Practical Applications	36
	Part IV: Solutions in Code	37
	Part V: Solutions in Law, Norms and the Market	38
	Part VI: Concise Conclusions	39
	References	40
	What Is Data Mining and How Does It Work?	43
	Introduction	43
	Data Mining and Related Research Areas	44
	Database Terminology	45
	Basic Techniques	47
	Classification	48
	Clustering	50
	Pattern Mining	52
	Supporting Techniques	54
	Pre-processing Techniques	54
	Database Coupling	55
	Conclusion	57
	References	58
	Why Unbiased Computational Processes Can	Why Unbiased Computational Processes Can
	59	59
	Introduction	59
	Characterization of the Computational Modeling Process	61
	Modeling Assumptions	62
	Origins of Training Data	63
	Types of Problems	64
	Accuracy and Discrimination	65
	Scenario 1: Incorrect Labels	66
	Scenario 2: Sampling Bias	67
	Scenario 3: Incomplete Data	68
	Potential Solutions for Discrimination Free Computation	69
	Basic Techniques That Do Not Solve the Problem	69
	Computational Modeling for Discrimination Free Decision Making	71
	Conclusion and Open Problems	71
	References	72
	Part II: Possible Discrimination and	Part II: Possible Discrimination and
	74	74
	A Comparative Analysis of Anti-Discrimination	A Comparative Analysis of Anti-Discrimination
	75	75
	The $Huber$	The $Huber$
	75	75
	Place of the Two Rights in the EU Legal Order	77
	Discrimination, a Concept in Search of Unity	78
	Differences in the Scope of EU Data Protection and Anti-Discrimination Legislation	81
	A Legal Regime Comprising Both an Administrative Structure and a Bundle of Subjective Rights	82
	Data Protection and Anti-Discrimination: Two Regulatory Human Rights	87
	Overlaps: At the Crossroad between Data Protection and Anti-Discrimination	90
	Conclusions: Articulating the Two Rights	95
	References	97
	The Discovery of Discrimination	104
	Introduction	104
	Classification Rules for Discrimination Discovery	108
	Classification Rules	108
	Measures of Discrimination	109
	Direct Discrimination Discovery	113
	Indirect Discrimination Discovery	114
	Argumentation	116
	Affirmative Actions	117
	The DCUBE Tool	118
	Conclusions	119
	References	119
	Discrimination Data Analysis:	Discrimination Data Analysis:
	122	122
	Introduction	122
	Sociological and Legal Perspectives	123
	Labour Economic Perspective	125
	(Quasi-)Experimental Perspective	128
	Auditing	128
	Controlled Experiments	129
	Profiling Perspective	130
	Racial Profiling	131
	Credit Markets	132
	Knowledge Discovery Perspective	133
	Conclusions	135
	References	135
	Risks of Profiling and the Limits of Data	Risks of Profiling and the Limits of Data
	149	149
	Introduction	149
	Risks Associated with Profiling	150
	Discrimination	150
	De-individualisation	150
	Stereotyping	151
	Information Asymmetries	151
	Inaccuracy	152
	Abuse	152
	Privacy and Data Protection in Light of Profiling	152
	Data Protection Law	154
	Drawbacks to the Current Approach to Data Protection in the Context of Profiling	156
	The ‘Binary’ Nature of Data Protection Law	157
	The Procedural Nature of Data Protection Law	158
	Inflation of the Personal Sphere	158
	Data Minimisation	159
	Is Data Protection Law an Adequate Solution?	159
	Shifting the Focus in Data Protection Law	160
	Differentiation in Data Protection: Data Centric Approach	160
	Focus on the ‘Why’ Instead of the ‘What’: Goal Oriented Approach	161
	Revisiting the Moral Reasons for Data Protection	161
	From ‘Privacy by Design’ to ‘Ethics by Design’	162
	Conclusion	162
	References	163
	Part III: Practical Applications	165
	Explainable and Non-explainable	Explainable and Non-explainable
	166	166
	Introduction	166
	Explainable and Non-explainable Discrimination	168
	Discussion of the Legal Aspects	168
	Motivation for the Explainable Discrimination	168
	Discrimination in Decision Making	169
	Conditional Non-discrimination in Decision Making	170
	An Example on University Admission	170
	Measuring Discrimination	171
	Illustration of the Redlining Effect	173
	Illustration of the Reverse Discrimination	174
	Removing the Illegal Discrimination When Training a Classifier	175
	Techniques	175
	Computational Experiments	177
	Conclusion	179
	References	180
	Knowledge-Based Policing:	Knowledge-Based Policing:
	182	182
	Introduction	182
	Intelligence-Led Policing	184
	Origin and Epistemological Basis	184
	Reorienting Data, Information, Knowledge, and Intelligence	185
	Knowledge-Based Policing	187
	The Need for a New Foundation	187
	The Role of Boundary Objects in Augmented Reality	188
	Realizing the Augmented Reality Potential	188
	Discussion	191
	Databesity: The Ever Present Hunger for Larger Databases	191
	Augmented Reality: Real-Time Processing of Data-Streams	192
	Developing an Ubiquitous Sensor-Network	193
	Dealing with Privacy Invasion	194
	Dealing with Discrimination	195
	Dealing with Group-Think	195
	Sustaining Trust	196
	Consequences for Legislation	196
	Conclusion	197
	Abbreviations	198
	References	198
	Combining and Analyzing Judicial Databases	201
	Introduction	201
	Databases in the Dutch Criminal Justice System	203
	Collecting and Combining Judicial Data	204
	A Data Warehouse Approach to Combining Judicial Data	205
	A Dataspace Approach to Combining Judicial Data	207
	Challenges in Combining Judicial Data	210
	Protecting Privacy When Combining Judicial Data	212
	Risks of Analyzing Judicial Data	213
	Concluding Remarks	215
	References	215
	Part IV: Solutions in Code	217
	Privacy-Preserving Data Mining Techniques:	Privacy-Preserving Data Mining Techniques:
	218	218
	Introduction	218
	Identity Disclosure	220
	Attribute Disclosure	222
	Privacy of Decentralized Data	224
	New Challenges for Data Privacy	226
	Conclusion	228
	References	228
	Techniques for Discrimination-Free Predictive	Techniques for Discrimination-Free Predictive
	231	231
	Introduction	232
	Problem Statement: Discrimination-Aware Classification	234
	Motivation: Links to Legislation	235
	Measuring Discrimination	236
	Techniques for Discrimination-Free Classification	237
	Pre-processing Techniques	237
	Changing the Learning Algorithms	240
	Post-Processing the Induced Models	242
	Experiments	244
	Discussion and Conclusion	245
	References	246
	Direct and Indirect Discrimination Prevention	Direct and Indirect Discrimination Prevention
	248	248
	Introduction	248
	Preliminaries	251
	Basic Notions	251
	Direct and Indirect Discriminatory Rules	252
	Taxonomy of Discrimination Prevention Methods	253
	Types of Pre-processing Discrimination Prevention Methods	254
	Direct Discrimination Prevention Methods	255
	Indirect Discrimination Prevention Methods	257
	Measuring Discrimination Removal	258
	Measuring Data Quality	258
	Experimental Results	259
	Conclusions and Future Work	260
	References	261
	Introducing Positive Discrimination in	Introducing Positive Discrimination in
	262	262
	Introduction	262
	The Naive Bayes Classifier	265
	The Problem of Discrimination in Data-Mining	266
	Discrimination-Free Naive Bayes Classifiers	270
	Using Different Decision Thresholds	270
	Two Naive Bayes Models	271
	A Latent Variable Model	272
	Comparing the Three Methods	274
	A Note on Positive Discrimination	275
	Concluding Remarks	276
	References	277
	Part V: Solutions in Law,	Part V: Solutions in Law,
	278	278
	From Data Minimization to Data Mini$mum$	From Data Minimization to Data Mini$mum$
	279	279
	Introduction	279
	Data Mining and Profiling Techniques	281
	Data Protection Legislation	281
	Anti-discrimination Legislation	282
	Data Minimization Principles	283
	Loss of Contextuality	285
	Data Minimummization	288
	Conclusion	290
	References	292
	Quality of Information, the Right to Oblivion	Quality of Information, the Right to Oblivion
	294	294
	Quality of Information	294
	The Quality of Information as an Instrument to Guarantee Certain Fundamental Rights	298
	A New Fundamental Right: The Digital Reputation	298
	Quality of Information and Automated Individual Decisions	299
	Quality of Information and Time and the Right to Oblivion	300
	Conclusions	303
	References	304
	Transparency in Data Mining: From Theory	Transparency in Data Mining: From Theory
	305	305
	Introduction: Transparency, Technology and Prediction	306
	Predictions, Data Mining, Personal Information and Information Flows	307
	Example: Data Mining and Security2	307
	Prediction and Data Mining: Technology, Human Discretion and Policy Decisions	308
	The Nature of Transparency in Predictive Modeling: Working through the Information Flow	310
	Why Transparency?	313
	General	313
	Transparency – From Theory to Policy	315
	Bringing It All Together: Towards a Policy Blueprint for Transparency	323
	Coda: The Limits of Transparency	326
	References	327
	Data Mining as Search: Theoretical Insights and	Data Mining as Search: Theoretical Insights and
	329	329
	Introduction: Beyond the Visceral Response to Governmental Data Mining	329
	Governmental Data Mining: Definitions, Participants and Problems	332
	Governmental Data Mining and/as (Illegal) Searches?	333
	Finding a Theory	333
	Data Mining as “Searches”: Introducing Three Perspectives	335
	Conclusion: Novel Practices, Classic Concepts and Policy Proposals	340
	References	342
	Part VI:	Part VI:
	343	343
	The Way Forward	344
	Concise Conclusion: Shifting Paradigms	345
	The Failure of Access Controls	346
	The Failure of Anonymity	348
	The Failure of Purpose Specification	349
	Focus on Transparency and Accountability	351
	Further Research	352
	The Future of Discrimination	354
	The Future of Privacy and Data Protection	357
	References	359
	Author Index	361
	Subject Index	362