Consent for all

Luger, Ewa; Moran, Stuart; Rodden, Tom

doi:10.1145/2470654.2481371

Cited by 104 publications

(33 citation statements)

References 14 publications

(19 reference statements)

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“…This is a precursor to their ability to consciously exercise agency in situations where personal data being collected and processed. Already recognised in specific contexts, such as consent and withdrawal (Coles-Kemp and Zabihi, 2010;Luger et al, 2013;Custers et al, 2013), the need for data to be more legible is becoming pervasive as society becomes more data-driven.…”

Section: Legibilitymentioning

confidence: 99%

Human-Data Interaction: The Human Face of the Data-Driven Society

et al. 2014

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The increasing generation and collection of personal data has created a complex ecosystem, often collaborative but sometimes combative, around companies and individuals engaging in the use of these data. We propose that the interactions between these agents warrants a new topic of study: Human-Data Interaction (HDI). In this paper we discuss how HDI sits at the intersection of various disciplines, including computer science, statistics, sociology, psychology and behavioural economics. We expose the challenges that HDI raises, organised into three core themes of legibility, agency and negotiability, and we present the HDI agenda to open up a dialogue amongst interested parties in the personal and big data ecosystems.

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Section: Legibilitymentioning

confidence: 99%

Human-Data Interaction: The Human Face of the Data-Driven Society

et al. 2014

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“…A significant body of research concludes that such approaches to acquiring consent are flawed. Luger et al find that the terms and conditions provided by major energy companies are not sufficiently readable, excluding many from being able to make informed decisions about whether they agree to such terms [29]. Indeed, Obar and Oeldorf-Hirsch find that the vast majority of people do not even read such documents [40], with all participants in a user study accepting terms including handing over their first-born child to use a social network site.…”

Section: Introductionmentioning

confidence: 99%

Beyond the EULA: Improving Consent for Data Mining

Hutton

Henderson

2017

Studies in Big Data

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Companies and academic researchers may collect, process, and distribute large quantities of personal data without the explicit knowledge or consent of the individuals to whom the data pertains. Existing forms of consent often fail to be appropriately readable and ethical oversight of data mining may not be sufficient. This raises the question of whether existing consent instruments are sufficient, logistically feasible, or even necessary, for data mining. In this chapter, we review the data collection and mining landscape, including commercial and academic activities, and the relevant data protection concerns, to determine the types of consent instruments used. Using three case studies, we use the new paradigm of human-data interaction to examine whether these existing approaches are appropriate. We then introduce an approach to consent that has been empirically demonstrated to improve on the state of the art and deliver meaningful consent. Finally, we propose some best practices for data collectors to ensure their data mining activities do not violate the expectations of the people to whom the data relate.

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“…While it is well understood that lengthy terms of service are often not sufficiently readable [44], and that most people do not read them [45], it is of concern that so many applications, including those from QS market leaders, fail to make fundamental information about the way their services function available in situ. With data protection regimes such as the EU's GDPR strengthening the requirements for clear privacy notices at the time of data collection, it is evident that this is being regularly subverted by many QS applications.…”

Section: Discussionmentioning

confidence: 99%

Assessing the Privacy of mHealth Apps for Self-Tracking: Heuristic Evaluation Approach (Preprint)

Hutton¹,

Price²,

Kelly³

et al. 2017

Preprint

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Background: The increasing sophistication of self-tracking technologies allows individuals to generate significant quantities of timely and accurate data about their lifestyle, biology, and environment, which can be used to support health interventions and measure outcomes. However, such user-generated data is often not treated with the sensitivity of other medical data, with data stored and processed by vendors with commercial motivations, such as enabling targeted advertising and making inferences from linked data. As the sensors and applications which enable these technologies continue to become more sophisticated, the privacy implications may become more severe. Methods for systematically identifying and

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Consent for all

Cited by 104 publications

References 14 publications

Human-Data Interaction: The Human Face of the Data-Driven Society

Human-Data Interaction: The Human Face of the Data-Driven Society

Beyond the EULA: Improving Consent for Data Mining

Assessing the Privacy of mHealth Apps for Self-Tracking: Heuristic Evaluation Approach (Preprint)

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