Lebenswelt im Wohnkontext

Hildebrandt, Johanna

doi:10.1007/978-3-531-94258-2_9

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…46 Mireille Hildebrandt has shown how wider theoretical assumptions are necessarily embedded, even within unsupervised learning algorithms, in the constructions of a model of reality by translating external reality into data sets, and 'theory' is also implicated in reviewing the validity of found correlations. 47 Furthermore, data simply never speaks for itself: 'Making sense of data is always frameddata are examined through a particular lens that influences how they are interpreted.' 48 Still, whilst exaggerated, the core assertion about the new approach to discoveries stands, even if it over-promises results outside retail and marketing.…”

Section: Knowledge and Truth Production Throughmentioning

confidence: 99%

The Pixelated Person: Humanity in the Grip of Algorithmic Personalisation

Kohl¹

2021

Data-Driven Personalisation in Markets, Politics and Law

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By far the most fascinating and profitable subject of predictive algorithms is the human actor. The capacity to predict human preferences, responses and behaviours offers endless possibilities for science, commerce, politics and regulation, and promises convenience and efficiency that further private and public interests in equal measure. There is nothing inherently new about the attempt to predict buying choices, political leanings and likely votes of individuals and groups, the probable effectiveness of medical treatments, likely defaults on loans, the chance of fraudulent insurance claims or of reoffending. Yet, the capacity to 'know' the individual and the group, and to predict their constitution and behaviour has witnessed a sudden upturn of unprecedented scale. The rise of network society and smart technology is generating endless trails of personal data, finely pixelated digital footprints, that are aggregated into big data setsthat involve large collections (volume) of real-time (velocity), diverse and relational personal data (variety) 1about virtually all aspects of human life from shopping, food and entertainment preferences, friendship networks, romantic attachments, social activities, health concerns, physical movements, driving behaviour or sporting activities, to biometric data, such as voice, face, gait or keystroke, or physiological data on heart rate, blood pressure or sleeping patterns. These data sets, when mined by algorithms, can reveal significant patterns and correlations and, ultimately, produce knowledge about the group (e.g. behavioural trends, economic activity, delinquency, spread of disease, political trends, etc. 2 ) and about the individual (e.g. educational level, social status, political leaning, sexual orientation, emotional states and psychological vulnerabilities as well as predilections for activities and movements). This knowledge then lies at the disposal of the private sector and government to be used for a wide range of purposes, implemented through 'personalised' services, treatments and regulationsome beneficial, some harmful, but mostly a mixture of both.

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Section: Knowledge and Truth Production Throughmentioning

confidence: 99%

The Pixelated Person: Humanity in the Grip of Algorithmic Personalisation

Kohl¹

2021

Data-Driven Personalisation in Markets, Politics and Law

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“…These systems will soon be able to detect different types of legal argumentation in legal text, while charting the legal domain to which it applies, the kind of case it concerns (asking for an injunction or compensation, charging a defendant), and the legal effect that is at stake. 80 At some point this will lead to an era where 'if datadriven agency defines a situation as real, it will be real in its consequences' 81 and to the extent that such determinations are not contestable by those subjected to their implications, we will have to reinvent such contestation to uphold the Rule of Law. Though it makes sense to help computers to figure out our concept of a capital city by teaching them to 'take the vector for Paris and subtract the vector for France and add Italy, [such that] you get Rome', this is not what we mean by a thought.…”

Section: Law Informed By Data-driven Agentsmentioning

confidence: 99%

Law as Information in the Era of Data‐Driven Agency

Hildebrandt

2016

Modern Law Review

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This contribution introduces the mathematical theory of information that 'informs' computer systems, the internet and all that has been built upon it. The aim of the author is to invite lawyers to reconsider the grammar and alphabet of modern positive law and of the Rule of Law, in the face of the alternative grammar and alphabet of a data-driven society. Instead of either embracing or rejecting the technological transitions that reconfigure the operations of the law, this article argues that lawyers should collaborate with the computer scientists that engineer and design the affordances of our new onlife world. This is crucial if we want to sustain democratic participation in law-making, contestability of legal effect and transparency of how citizens may be manipulated by the invisible computational backbone of our rapidly and radically changing world.

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Lebenswelt im Wohnkontext

Cited by 2 publications

References 3 publications

The Pixelated Person: Humanity in the Grip of Algorithmic Personalisation

The Pixelated Person: Humanity in the Grip of Algorithmic Personalisation

Law as Information in the Era of Data‐Driven Agency

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