Univerzitní knihovna OU - Úplné zobrazení záznamu

Intro -- Table Of Contents -- Profiling The European Citizen: Why Today’S Democracy Needs To Look Harder At The Negative Potential Of New Technology Than At Its Positive Potential -- Introitus: What Descartes Did Not Get -- Part I. Theories Of Normativity Between Law And Machine Learning -- From Agency-Enhancement Intentions To Profile-Based Optimisation Tools: What Is Lost In Translation -- Mathematical Values And The Epistemology Of Data Practices -- Stirring The Pots: Protective Optimization Technologies -- On The Possibility Of Normative Contestation Of Automated Data-Driven Decisions -- Part II. Transparency Theory For Data-Driven Decision Making -- How Is ’Transparency’ Understood By Legal Scholars And The Machine Learning Community? -- Why Data Protection And Transparency Are Not Enough When Facing Social Problems Of Machine Learning In A Big Data Context -- Transparency Is The Perfect Cover-Up (If The Sun Does Not Shine) -- Transparency As Translation In Data Protection -- Part III. Presumption Of Innocence In Data-Driven Government -- The Presumption Of Innocence’s Janus Head In Data-Driven Government -- Predictive Policing. In Defence Of ’True Positives’ -- The Geometric Rationality Of Innocence In Algorithmic Decisions -- On The Presumption Of Innocence In Data-Driven Government. Are We Asking The Right Question? -- Part IV. Legal And Political Theory In Data-Driven Environments -- A Legal Response To Data-Driven Mergers -- Ethics As An Escape From Regulation. From "Ethics-Washing" To Ethics-Shopping? -- Citizens In Data Land -- Part V. Saving Machine Learning From P-Hacking -- From Inter-Subjectivity To Multi-Subjectivity: Knowledge Claims And The Digital Condition -- Preregistration Of Machine Learning Research Design. Against P-Hacking -- Induction Is Not Robust To Search -- Part VI. The Legal And Ml Status Of Micro-Targeting.

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