Information Privacy Law eJournal最新文献_第10页

What Happens When an Acquaintance Buys Your Data?: A New Privacy Harm in the Age of Data Brokers 当一个熟人购买你的数据会发生什么?:数据经纪人时代的新隐私危害

Information Privacy Law eJournal Pub Date : 1900-01-01 DOI: 10.2139/SSRN.2870044

Theodore Rostow

引用次数: 11

Relational Contracts of Adhesion 关系粘合合同

Information Privacy Law eJournal Pub Date : 1900-01-01 DOI: 10.2139/SSRN.3008687

David Hoffman

{"title":"Relational Contracts of Adhesion","authors":"David Hoffman","doi":"10.2139/SSRN.3008687","DOIUrl":"https://doi.org/10.2139/SSRN.3008687","url":null,"abstract":"Not all digital fine print exculpates liability: some exhorts users to perform before the consumer relationship has soured. We promise to choose strong passwords (and hold them private); to behave civilly on social networks; to refrain from streaming shows and sports; and to avoid reverse-engineering code (or, worse, deploying deadly bots). In short: consumers are apparently regulated by digital fine print, though it’s universally assumed we don’t read it, and even if we did, we’ll never be sued for failing to perform. \u0000On reflection, this ordinary phenomenon is perplexing. Why would firms persist in deploying uncommunicative behavioral spurs? The conventional answer is that fine print acts as an option, drafted by dull, monopolist, lawyers. Through investigation of several sharing economy firms, and discussions with a variety of lawyers in this space, I show that this account is incomplete. Indeed, I identify and explore examples of innovative fine print that appears to really communicate with and manage users. \u0000These firms have cajoled using contracts by trading on their brands and identities, and by giving up on certain exculpatory defenses common to digital agreements. I argue that the result is a new form of relational contracting, taking on attributes of both mass market adhesion contracts and more long-term deals.","PeriodicalId":179517,"journal":{"name":"Information Privacy Law eJournal","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124575975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Privacy, Confidentiality and Property 隐私、保密和财产

Information Privacy Law eJournal Pub Date : 1900-01-01 DOI: 10.2139/ssrn.3801736

Peter Jaffey

引用次数: 0

Case Digest: Pineda v. Williams-Sonoma Stores, Inc. 案例摘要:Pineda诉Williams-Sonoma Stores, Inc。

Information Privacy Law eJournal Pub Date : 1900-01-01 DOI: 10.2139/SSRN.2027703

Matthew Adam Susson

引用次数: 1

Privacy, Employment, and Dignity 隐私、就业和尊严

Information Privacy Law eJournal Pub Date : 1900-01-01 DOI: 10.1007/978-3-319-74639-5_14

J. Francis, L. Francis

引用次数: 0

Administration by Algorithm? Public Management Meets Public Sector Machine Learning 算法管理?公共管理与公共部门机器学习

Information Privacy Law eJournal Pub Date : 1900-01-01 DOI: 10.31235/osf.io/mwhnb

Michael Veale, I. Brass

{"title":"Administration by Algorithm? Public Management Meets Public Sector Machine Learning","authors":"Michael Veale, I. Brass","doi":"10.31235/osf.io/mwhnb","DOIUrl":"https://doi.org/10.31235/osf.io/mwhnb","url":null,"abstract":"Public bodies and agencies increasingly seek to use new forms of data analysis in order to provide 'better public services'. These reforms have consisted of digital service transformations generally aimed at 'improving the experience of the citizen', 'making government more efficient' and 'boosting business and the wider economy'. More recently however, there has been a push to use administrative data to build algorithmic models, often using machine learning, to help make day-to-day operational decisions in the management and delivery of public services rather than providing general policy evidence. This chapter asks several questions relating to this. What are the drivers of these new approaches? Is public sector machine learning a smooth continuation of e-Government, or does it pose fundamentally different challenge to practices of public administration? And how are public management decisions and practices at different levels enacted when machine learning solutions are implemented in the public sector? Focussing on different levels of government: the macro, the meso, and the 'street-level', we map out and analyse the current efforts to frame and standardise machine learning in the public sector, noting that they raise several concerns around the skills, capacities, processes and practices governments currently employ. The forms of these are likely to have value-laden, political consequences worthy of significant scholarly attention.","PeriodicalId":179517,"journal":{"name":"Information Privacy Law eJournal","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122206759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 65

Privacy Impact Assessments: The UK Experience 隐私影响评估:英国经验

Information Privacy Law eJournal Pub Date : 1900-01-01 DOI: 10.2139/SSRN.1606762

A. Warren, R. Bayley, Colin J. Bennett, A. Charlesworth, R. Clarke, C. Oppenheim

引用次数: 7