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The cost of reading privacy policies
... This textual mode of providing transparency information implies a broad variety of well-known and well-researched shortcomings, ranging from general incomprehensibility for laypersons [2][3][4] to paradigmatic incongruence with modern, highly agile development practices [5,6]. Besides these, however, it also severely hinders any attempt to perform higher-level analyses to be conducted on publicly reported transparency and accountability data, especially across multiple, and possibly interrelated, controllers. ...
... The established practice to align with the transparency principle is to provide a written privacy policy. However, repeated studies have shown that these are frequently not read due to their length [2,7,8], are hard to understand because of legalese [3] and/or technical [9] language, and often do not contain all necessary information [10][11][12]. In addition, they tend to include generalized and vague terms and therefore lack clarity [13]. ...
... For doing so, we publish the collected information in a public open-source repository of TILT documents. 2 The corpus of machine-readable transparency information were extracted from real-world online. They contain information about the collection, processing and use of personal data in accordance with the legal requirements of the GDPR. ...
Transparency and accountability are indispensable principles for modern data protection, from both, legal and technical viewpoints. Regulations such as the GDPR, therefore, require specific transparency information to be provided including, e.g., purpose specifications, storage periods, or legal bases for personal data processing. However, it has repeatedly been shown that all too often, this information is practically hidden in legalese privacy policies, hindering data subjects from exercising their rights. This paper presents a novel approach to enable large-scale transparency information analysis across service providers, leveraging machine-readable formats and graph data science methods. More specifically, we propose a general approach for building a transparency analysis platform (TAP) that is used to identify data transfers empirically, provide evidence-based analyses of sharing clusters of more than 70 real-world data controllers, or even to simulate network dynamics using synthetic transparency information for large-scale data-sharing scenarios. We provide the general approach for advanced transparency information analysis, an open source architecture and implementation in the form of a queryable analysis platform, and versatile analysis examples. These contributions pave the way for more transparent data processing for data subjects, and evidence-based enforcement processes for data protection authorities. Future work can build upon our contributions to gain more insights into so-far hidden data-sharing practices.
... Already in 2008, it was estimated that each US citizen would spend between 181 and 304 hours per year to read (or between 81 and 293 hours per year to skim) privacy notices for each new website they visit (McDonald and Cranor 2008). Some progress has been made to improve privacy notices-for instance, by improving the information offered (eg, Reidenberg et al. 2016, Sánchez et al. 2021 or the user interfaces for communicating the information (eg, Karegar et al. 2020, Schaub et al. 2017. ...
... Establishing coverage is a necessary but not sufficient metarequirement for establishing TIPP. As demonstrated by the practice of posting privacy notices (McDonald andCranor 2008, Sunyaev et al. 2015), presenting consumers with a lot of information leads to information overload and prevents consumers from retrieving information of interest to them (McDonald and Cranor 2008, Milne and Culnan 2004, Sheng and Simpson 2014. Consequently, establishing TIPP also requires adaptivity. ...
... Establishing coverage is a necessary but not sufficient metarequirement for establishing TIPP. As demonstrated by the practice of posting privacy notices (McDonald andCranor 2008, Sunyaev et al. 2015), presenting consumers with a lot of information leads to information overload and prevents consumers from retrieving information of interest to them (McDonald and Cranor 2008, Milne and Culnan 2004, Sheng and Simpson 2014. Consequently, establishing TIPP also requires adaptivity. ...
The rising diffusion of information systems (IS) throughout society poses an increasingly serious threat to privacy as a social value. One approach to alleviating this threat is to establish transparency of information privacy practices (TIPP) so that consumers can better understand how their information is processed. However, the design of transparency artifacts (eg, privacy notices) has clearly not followed this approach, given the ever-increasing volume of information processing. Hence, consumers face a situation where they cannot see the 'forest for the trees' when aiming to ascertain whether information processing meets their privacy expectations. A key problem is that overly comprehensive information presentation results in information overload and is thus counterproductive for establishing TIPP. We depart from the extant design logic of transparency artifacts and develop a theoretical foundation (TIPP theory) for transparency artifact designs useful for establishing TIPP from the perspective of privacy as a social value. We present TIPP theory in two parts to capture the sociotechnical interplay. The first part translates abstract knowledge on the IS artifact and privacy into a description of social subsystems of transparency artifacts, and the second part conveys prescriptive design knowledge in form of a corresponding IS design theory. TIPP theory establishes a bridge from the complexity of the privacy concept to a metadesign for transparency artifacts that is useful to establish TIPP in any IS. In essence, transparency artifacts must accomplish more than offering comprehensive information; they must also be adaptive to the current information needs of consumers.
... Other scholars study privacy policies to examine their usability and readability. Privacy policies are notoriously lengthy and difficult to read [16,27], and some researchers have sought to quantify policies' vagueness [23,25] and legibility, such as by studying policy length [3], estimating required reading comprehension [54], and measuring whether policy users can actually demonstrate comprehension [36]. Further work has examined the interplay between privacy policies and legislation, including whether selected services comply with relevant legislation such as the European Union's General Data Protection Regulation (GDPR) [29,35,48], and what impact particular legislation has had on privacy policies [15,52]. ...
... We can similarly infer some of the challenges researchers likely face when studying privacy policies. For example, privacy policies are notoriously vague and difficult to interpret by users [16,27]; there has been work showing that even experts might disagree on the interpretation of privacy policies [36]. The abundance of papers seeking to automate or expedite the analysis of privacy policies (e.g., see [4,18,51]) implies that privacy policy analysis is tedious and time-consuming. ...
Companies' privacy policies and their contents are being analyzed for many reasons, including to assess the readability, usability, and utility of privacy policies; to extract and analyze data practices of apps and websites; to assess compliance of companies with relevant laws and their own privacy policies, and to develop tools and machine learning models to summarize and read policies. Despite the importance and interest in studying privacy policies from researchers, regulators, and privacy activists, few best practices or approaches have emerged and infrastructure and tool support is scarce or scattered. In order to provide insight into how researchers study privacy policies and the challenges they face when doing so, we conducted 26 interviews with researchers from various disciplines who have conducted research on privacy policies. We provide insights on a range of challenges around policy selection, policy retrieval, and policy content analysis, as well as multiple overarching challenges researchers experienced across the research process. Based on our findings, we discuss opportunities to better facilitate privacy policy research, including research directions for methodologically advancing privacy policy analysis, potential structural changes around privacy policies, and avenues for fostering an interdisciplinary research community and maturing the field.
... In this area of tension, the HCI sub-community called "Usable Privacy" is researching, among other things, the optimization of the user-friendliness of privacy management in terms of both awareness [9,11,32,45,74,82] and control [3,14,15,23,31,34,58]. Often times, such research quite naturally intersects with data protection law: A classic example of this is the extensive study of privacy notices, which, in essence, illustrates a problem also present for other applications of law: the content is written by lawyers for lawyers and is hardly understandable for laypersons [64,65]. Privacy communication tends to be too long, legalistic, and as such not graspable for consumers. ...
... While the work on Usable Privacy policies in HCI has found that privacy policies lack readability and understandability, it does not seek to reformulate purposes to make them more meaningful, but rather to take the wording used as given (e.g.: [64,65,76,84]). The very fact that purpose specifications form a design space resource where data subjects are likely to have justified interests must spark HCI interest. ...
Usable Privacy often works at the intersection of regulation to thrive for more usable solutions to normative provisions. The regulatory provisions themselves, or the legal standards of their implementation, however, typically remain unquestioned in the design process. This way, HCI falls short of its potential to inform regulation with insights on human expectations, attitudes, and behavior in the real world, to make law more effective. In this chapter, we present the extensive impulses that are also coming from legal sciences themselves motivating a more substantial collaboration of HCI and legal sciences. We turn to the example of data protection legislation and discuss the legislative intentions surrounding the landmark case of the European General Data Protection Regulation (GDPR). We show how GDPRs’ requirement of “effectiveness” of technical and organizational protection measures opens the door for more in-depth collaboration with HCI and provide examples of high potential for such joint research.
... How people interact with existing and fictional ToS and associated policies and how to improve the results are two lines of work on ToS mainly rooted in human-computer interaction (HCI). The length and complexity and, consequently, the cost of time to read ToS and associated policies are mentioned as the main problems, causing people to either partly or entirely bypass them (Bakos et al., 2014;Luger et al., 2013;Maronick, 2014;McDonald & Cranor, 2008;Obar & Oeldorf-Hirsch, 2018). ...
Contemporary connected things entail ongoing relations between producers , end users, and other actors characterized by ongoing updates and production of data about and through use. These relations are currently governed by Terms of Service (ToS) and related policy documents, which are known to be mostly ignored beyond the required interaction of ticking a box to indicate consent. This seems to be a symptom of failure to design for effectively mediating ongoing relations among multiple stakeholders involving multiple forms of value generation. In this paper, we use ToS as an entrance point to explore design practices for democratic data govern-ance. Drawing on posthuman perspectives, we make three posthuman design moves exploring entanglements, decentering, and co-performance in relation to Terms of Service. Through these explorations we begin to sketch a space for design to engage with democratic data governance through a practice of what we call revealing design that is aimed at meaningfully making visible these complex networked relations in actionable ways. This approach is meant to open alternative possible trajectories that could be explored for design to enable genuine democratic data governance.
... Privacy policies play a crucial role in protecting customer privacy, but comprehending these documents can be a challenge due to their often lengthy and complex nature [19,20]. Research conducted in the United States revealed that citizens would need to spend an average of 40 min per day just to read all the privacy policies they encounter [21]. Moreover, privacy policies may be influenced by other privacy regulations from various regions worldwide. ...
This study examines the privacy protection challenges in data sharing between organisations and third-party entities, focusing on changing collaborations in the digital age. Utilising a mixed-method approach, we categorise data-sharing practices into three business models, each with unique privacy concerns. The research reviews legal regulations like the General Data Protection Regulation (GDPR), highlighting their emphasis on user privacy protection but noting a lack of specific technical guidance. In contrast, industrial privacy frameworks such as NIST and Five Safes are explored for their comprehensive procedural and technical guidance, bridging the gap between legal mandates and practical applications. A key component of this study is the analysis of the Facebook–Cambridge Analytica data breach, which illustrates the significant privacy violations and their wider implications. This case study demonstrates how the principles of the NIST and Five Safes frameworks can effectively mitigate privacy risks, enhancing transparency and accountability in data sharing. Our findings highlight the dynamic nature of data sharing and the vital role of both privacy regulations and industry-specific frameworks in protecting individual privacy rights. This study contributes insights into the development of robust privacy strategies, highlighting the necessity of integrating comprehensive privacy frameworks into organisational practices for improved decision making, operational efficiency, and privacy protection in collaborative data environments.
... Jarovski distinguishes between problems related to cognition (complexity, manipulation and behavioral biases), and information overload (length, uniquity, insufficient information, lack of intervenability, and lack of free choice) [48]. Presenting individuals with the result of the matching and asking for their consent each time a new request for access to personal data reaches the Pod might not scale well [50]. ...
Personal Information Management Systems (PIMS) are acquiring a prominent role in the data economy by promoting services that help individuals to have more control over the processing of their personal data, in line with the European data protection laws. One of the highlighted solutions in this area is Solid, a new protocol that is decentralizing the storage of data, through the usage of interoperable web standards and semantic vocabularies, to empower its users to have more control over the processing of data by agents and applications. However, to fulfill this vision and gather widespread adoption, Solid needs to be aligned with the law governing the processing of personal data in Europe, the main piece of legislation being the General Data Protection Regulation (GDPR). To assist with this process, we analyze the current efforts to introduce a policy layer in the Solid ecosystem, in particular, related to the challenge of obtaining consent for processing personal data, focusing on the GDPR. Furthermore, we investigate if, in the context of using personal data for biomedical research, consent can be expressed in advance, and discuss the conditions for valid consent and how it can be obtained in this decentralized setting, namely through the matching of privacy preferences, set by the user, with requests for data and whether this can signify informed consent. Finally, we discuss the technical challenges of an implementation that caters to the previously identified legal requirements.
... In 2008, it was estimated the average internet user would need 76 days per year to wade through every internet privacy policy before deliberating over consenting, a figure undoubtedly higher today. 42 Zuboff argues that the very minutiae and scale of the small print is one reason consumers are inclined to put their privacy at risk. 17 Simply put, the standard of competence required to meaningfully consent is often unreasonable. ...
Patient online record access (ORA) is spreading worldwide, and in some countries, including Sweden, and the USA, access is advanced with patients obtaining rapid access to their full records. In the UK context, from 31 October 2023 as part of the new NHS England general practitioner (GP) contract it will be mandatory for GPs to offer ORA to patients aged 16 and older. Patients report many benefits from reading their clinical records including feeling more empowered, better understanding and remembering their treatment plan, and greater awareness about medications including possible adverse effects. However, a variety of indirect evidence suggests these benefits are unlikely to accrue without supplementation from internet-based resources. Using such routes to augment interpretation of the data and notes housed in electronic health records, however, comes with trade-offs in terms of exposing sensitive patient information to internet corporations. Furthermore, increased work burdens on clinicians, including the unique demands of ORA, combined with the easy availability and capability of a new generation of large language model (LLM)-powered chatbots, create a perfect collision course for exposing sensitive patient information to private tech companies. This paper surveys how ORA intersects with internet associated privacy risks and offers a variety of multilevel suggestions for how these risks might be better mitigated.
... For these individuals, the Model Card may be used to understand the AI and use it to "pursue remedies" [9]. Nevertheless, it is not sufficient that information about the model is public, as the example of privacy policies demonstrates [15]. Model Cards in their current version include information that individual non-expert users may find difficult to understand. ...
... Privacy policies are required by law to explain the purpose and scope of the collection of information [72]. Sadly, most privacy policies are lengthy, difficult, and consume much time to read and understand, and are therefore rarely read [18,[74][75][76]. Table 10 shows the classification of computer privacy. ...
The study of security and computer privacy has become a significant focus in security and privacy research. To reflect a website's, service's, or app's privacy policies, they're frequently used as a beginning step for researchers investigating the reliability of stated data regulations, user comprehension of policy, or user control methods. It's challenging to collect information about privacy practices from Internet resources like websites and mobile applications for analysis because of the wide variations in the structure, presentation, and content. Most computer privacy studies attempt to test new methods for detecting, classifying, and analyzing computer privacy content. However, numerous papers have been published to promote research activities, and no trace of any bibliometric analysis work on computer privacy demonstrates research trends. By conducting a thorough analysis of computer privacy studies, it searches the Scopus database, which contains over 2000 papers published between 1976 and 2020. Using the bibliometric analysis technique, this study examines research activity in Europe, South America, and other continents. This work investigated the number of papers published, citations, research area, keywords, institutions, topics, and researchers in detail. An overview of the research efforts is followed by listing the words into a classification of computer privacy analysis tools, emphasizing the significance of a computer privacy research study. According to the investigation findings, there are numerous significant implications of research efforts in Europe compared to other continents. Finally, we summarize the review findings for each part by highlighting potential future research directions.
... Sixthly, rational ignorance is in place when the costs of learning new information are higher than the benefits of using this information in our decision making. In their calculations of the costs of reading privacy policies, Aleecia McDonald and Lorrie Faith Cranor (2008) found that it would take a US citizen 201 hours a year, and the total loss for the US economy would be 781 billion dollar. Seventhly, the status quo bias makes us prefer things to stay the way they are. ...
In the vastly changing world of consumer privacy, laws that protect citizens from the data hunger of companies are of the utmost importance. While the GDPR does protect consumer privacy in a certain way, it is based on a very limited conception of privacy. This paper examines the dominant paradigm in privacy law and shows that there are other ways to conceive of privacy. This will be done by looking at three components: (1) what is privacy, (2) what is privacy behaviour, and (3) why is privacy important. I labelled the current paradigm as the liberal conception of privacy. It contends that privacy is having control over information, that privacy behaviour is determined by rational choice and that privacy is important because it is a prerequisite for autonomy. This paper shows that the meaning of privacy could also be the right to be let alone, or more broad conceptions of control over information. Furthermore, privacy behaviour is not as straightforward as the privacy calculus model makes it seem, behavioural economics and social theory provide us with different understandings of privacy behaviour. Finally, when it comes to the value of privacy, republicanism showed the importance for democracy, relationship theory indicated its role in the development of love, friendship and trust, and critical theory explained the power of surveillance and how losing privacy is losing our humanity. This study concludes that the liberal paradigm provides a very limited way of looking at privacy and consequently, current law does not accurately protect consumer privacy.
... To see this, it is useful to keep in mind, as critics of online consent like to point out (Solove 2013; Hull 2015; Zuboff 2019), that we would need to devote an enormous amount of time to read in their entirety the privacy policies and other terms and conditions we have agreed to. The cost of doing that was estimated (already over a decade ago) to come to more than $780 billion in potentially lost productive capacity (McDonald and Cranor 2008). In other words, to read all the information about how our data are used, we would collectively be giving up hundreds of billions of dollars. ...
This paper argues, against the prevailing view, that consent to privacy policies that regular internet users usually give is largely unproblematic from the moral point of view. To substantiate this claim, we rely on the idea of the right not to know (RNTK), as developed by bioethicists. Defenders of the RNTK in bioethical literature on informed consent claim that patients generally have the right to refuse medically relevant information. In this article we extend the application of the RNTK to online privacy. We then argue that if internet users can be thought of as exercising their RNTK before consenting to privacy policies, their consent ought to be considered free of the standard charges leveled against it by critics.
... Empirical studies demonstrate the challenges in doing so in a meaningful way. One barrier is the decision volume resulting from users browser multiple websites with diverse and complex privacy policies [MC08,BP10,So12]. Even when the user has made a decision, dialogs can be designed to make opting-out difficult [MBS20,No20]. ...
Many privacy and data protection laws, such as Article 8 GDPR and the CCPA, establish different requirements when establishing a legal basis for collecting personal data about children. Our study asks whether and how children's websites collect consent. We conduct an automated analysis of 2, 066 educational and gaming websites, and a manual analysis of 13 large sites. We measure the prevalence of deceptive patterns identified in prior work, plus a new design consideration, whether the dialog is addressed to the child user's parent or guardian. A small minority of websites address dialogs for children, which suggest the majority of children's websites in our sample may not comply with Article 8 GDPR.
... Like other previous studies [37], participants shared their concerns about the uninspiring length of privacy terms and conditions although they are meant to spell out the salient points regarding smartphone functionality and app implications. Participants who were aware of MPDFs, privacy controls, and the impact of app permissions, were not so interested in smartphone policy guides but rather on the need for greater privacy controls allocated to users to safeguard their data from manufacturers and third-party apps. ...
Smartphone manufacturer provided default features (e.g., default location services, iCloud, Google Assistant, ad tracking) enhance the usability and extend the functionality of these devices. Prior studies have highlighted smartphone vulnerabilities and how users' data can be harvested without their knowledge. However, little is known about manufacturer provided default features in this regard -- their usability concerning configuring them during usage, and how users perceive them with regards to privacy. To bridge this gap, we conducted a task-based study with 27 Android and iOS smartphone users in order to learn about their perceptions, concerns and practices, and to understand the usability of these features with regards to privacy. We explored the following: users' awareness of these features, why and when do they change the settings of these features, the challenges they face while configuring these features, and finally the mitigation strategies they adopt. Our findings reveal that users of both platforms have limited awareness of these features and their privacy implications. Awareness of these features does not imply that a user can easily locate and adjust them when needed. Furthermore, users attribute their failure to configure default features to hidden controls and insufficient knowledge on how to configure them. To cope with difficulties of finding controls, users employ various coping strategies, some of which are platform specific but most often applicable to both platforms. However, some of these coping strategies leave users vulnerable.
... Reading Time. Privacy policies are hard to read and therefore do not help customers make informed decisions due to the fact that they are very lengthy and time-consuming [34]. Reading time is calculated using an individual's typical reading pace (roughly 238 WPM) [35]. ...
As the adoption of smart devices continues to permeate all aspects of our lives, concerns surrounding user privacy have become more pertinent than ever before. While privacy policies define the data management practices of their manufacturers, previous work has shown that they are rarely read and understood by users. Hence, automatic analysis of privacy policies has been shown to help provide users with appropriate insights. Previous research has extensively analyzed privacy policies of websites, e-commerce, and mobile applications, but privacy policies of smart devices, present some differences and specific challenges such as the difficulty to find and collect them. We present PrivacyLens, a novel framework for discovering and collecting past, present, and future smart device privacy policies and harnessing NLP and ML algorithms to analyze them. PrivacyLens is currently deployed, collecting, analyzing, and publishing insights about privacy policies to assist different stakeholders of smart devices, such as users, policy authors, and regulators. We show several examples of analytical tasks enabled by PrivacyLens, including comparisons of devices per type and manufacturing country, categorization of privacy policies, and impact of data regulations on data practices. At the time of submitting this paper, PrivacyLens had collected and analyzed more than 1,200 privacy policies for 7,300 smart device
... Users of a commercial digital service sign a contract with the provider that determines the terms of their interaction, including the use of their data. It is well known that most users will not read the contracts that they digitally sign [87,98]. In some cases, the system's design can be such that the offered consent can be considered either uninformed (c.f. ...
A fiduciary is a trusted agent that has the legal duty to act with loyalty and care towards a principal that employs them. When fiduciary organizations interact with users through a digital interface, or otherwise automate their operations with artificial intelligence, they will need to design these AI systems to be compliant with their duties. This article synthesizes recent work in computer science and law to develop a procedure for designing and auditing Fiduciary AI. The designer of a Fiduciary AI should understand the context of the system, identify its principals, and assess the best interests of those principals. Then the designer must be loyal with respect to those interests, and careful in an contextually appropriate way. We connect the steps in this procedure to dimensions of Trustworthy AI, such as privacy and alignment. Fiduciary AI is a promising means to address the incompleteness of data subject's consent when interacting with complex technical systems.
... OPP-115 (Wilson et al., 2016) collects 115 English websites' privacy policies and makes annotations at the sentence level. OPP-115 designs labels based on previous works (McDonald and Cranor, 2008;Staff, 2011). APP-350 (Zimmeck et al., 2019) gathers Android apps' privacy policies written in English. ...
... Notice is a cornerstone of privacy: entities collecting information are expected to disclose the types of data collected, and how it is used. Privacy policies serve as the primary mechanism for notice, yet research has shown that privacy policies are long, complex documents that users seldom read [20] [24] [23]. ...
... The shortest Chinese privacy statement had only 393 words and the longest 22,371 words. Assuming a reading speed of 250 words per minute (McDonald & Cranor, 2009), it took almost half an hour to read an average privacy statement from China. The average privacy statement from Germany had 4284 words (standard deviation 2836 words). ...
This chapter investigates how crowdsourcing platforms handle matters of data protection and analyzes information from 416 privacy statements. We find that German platforms mostly base their data processing solely on the GDPR, while U.S. platforms refer to numerous international, European, and state-level legal sources on data protection. The Chinese crowdsourcing platforms are usually not open to foreigners and do not refer to the GDPR. The privacy statements provide evidence that some U.S. platforms are specific in the sense that they explicitly state which data are not processed. When we compare the privacy practices of crowdsourcing platforms with the German fintech sector, it is noticeable that pseudonymization and anonymization are, at least in Germany, used much more frequently on crowdsourcing platforms. Most privacy statements did not exhaustively clarify what personal data are shared, even though they mentioned the sharing of data with third parties.
... The shortest Chinese privacy statement had only 393 words and the longest 22,371 words. Assuming a reading speed of 250 words per minute (McDonald & Cranor, 2009), it took almost half an hour to read an average privacy statement from China. The average privacy statement from Germany had 4284 words (standard deviation 2836 words). ...
... The reasons for such inconsistency may be complex. A prudent concern for privacy may be a longer-term commitment that conflicts with the immediate gratification and convenience of Internet use as privacy policies are long and complicated [6]. Also, many individuals may simply be misinformed and believe that their privacy is much better protected than it actually is [7]. ...
As technology advances daily, so are the challenges in preserving one's privacy. Being the generation that has been born in such a highly technological environment, members of Generation Z, born between mid-to-late 1990s and the early-to-mid 2000s, have been engaging in privacy related transactions more than any generation before. The issue of privacy is becoming more pronounced, along the possibilities of individuals controlling their data, that can lead to the discrepancy between attitudes about privacy preservation and actual behavior, that has become known as the "privacy paradox". By looking at this paradox through generational attitudes toward privacy, organizational practices and related legal frameworks, as well as the contemporary context of the sharing economy, the research aims to give insight whether members of the affected generation can be classified as fundamentalists, pragmatists, or unconcerned about privacy as a classification used by Alan Westin.
... Along these lines, the readability and clarity of privacy policies are significant concerns and are often introduced by how policies are written and structured. The average length of privacy policies is over 2500 words, and they are typically difficult to read and comprehend [43]. This makes users less likely to try to read or understand what is written in the policies. ...
Privacy policies outline data collection and sharing practices followed by an organization, together with choice and control measures available to users to manage the process. However, users have often needed help reading and understanding such documents, regardless of their being written in a natural language. The fundamental problems with privacy policies persist despite advancements in privacy design, frameworks, and regulations. To identify the causes of privacy policies being persistently challenging to comprehend, it is vital to investigate historical policy patterns and understand the evolution of privacy policies concerning information packaging and presentation. To this aid, we create a sentence-level classifier to conduct a large-scale longitudinal analysis on different privacy policies from 130,604 organizations, totaling approximately one million policies from 1997 to 2019. We annotate 10,717 sentences from 115 policies in the OPP-115 corpus to implement the classifier and then use those annotations to train the XLNet and BERT classifiers. Results from our analysis reveal that specific data practice categories experience more frequent policy changes than others, making it challenging to track relevant information over time. In addition, we discover that every category has distinct composition, readability, and structural issues, which exacerbate when categories frequently co-occur in a document. Based on our observations, we provide recommendations for policy articulation and revision to make privacy policy documents conform to better coherence and structure.
... Privacy policies are ubiquitous and required in many settings [46,48,47,71], and for better or worse, are an important tool for communicating about the behavior of systems. Natural language policies have many shortcomings and are full of technical details and jargon that significant impact their usability [65,42] as a tool to inform users clearly about the behaviors and data management practices. Privacy nutrition labels, or privacy labels, (similar * Primary contacts: Mir Masood Ali -mali92@uic.edu ...
Apple introduced \textit{privacy labels} in Dec. 2020 as a way for developers to report the privacy behaviors of their apps. While Apple does not validate labels, they do also require developers to provide a privacy policy, which offers an important comparison point. In this paper, we applied the NLP framework of Polisis to extract features of the privacy policy for 515,920 apps on the iOS App Store comparing the output to the privacy labels. We identify discrepancies between the policies and the labels, particularly as it relates to data collected that is linked to users. We find that 287$\pm196$K apps' privacy policies may indicate data collection that is linked to users than what is reported in the privacy labels. More alarming, a large number of (97$\pm30$\%) of the apps that have {\em Data Not Collected} privacy label have a privacy policy that indicates otherwise. We provide insights into potential sources for discrepancies, including the use of templates and confusion around Apple's definitions and requirements. These results suggest that there is still significant work to be done to help developers more accurately labeling their apps. Incorporating a Polisis-like system as a first-order check can help improve the current state and better inform developers when there are possible misapplication of privacy labels.
... Notice is a cornerstone of privacy: entities collecting information are expected to disclose the types of data collected, and how it is used. Privacy policies serve as the primary mechanism for notice, yet research has shown that privacy policies are long, complex documents that users seldom read [20] [24] [23]. ...
Privacy policies are long, complex documents that end-users seldom read. Privacy labels aim to ameliorate these issues by providing succinct summaries of salient data practices. In December 2020, Apple began requiring that app developers submit privacy labels describing their apps' data practices. Yet, research suggests that app developers often struggle to do so. In this paper, we automatically identify possible discrepancies between mobile app privacy policies and their privacy labels. Such discrepancies could be indicators of potential privacy compliance issues. We introduce the Automated Privacy Label Analysis System (ATLAS). ATLAS includes three components: a pipeline to systematically retrieve iOS App Store listings and privacy policies; an ensemble-based classifier capable of predicting privacy labels from the text of privacy policies with 91.3% accuracy using state-of-the-art NLP techniques; and a discrepancy analysis mechanism that enables a large-scale privacy analysis of the iOS App Store. Our system has enabled us to analyze 354,725 iOS apps. We find several interesting trends. For example, only 40.3% of apps in the App Store provide easily accessible privacy policies, and only 29.6% of apps provide both accessible privacy policies and privacy labels. Among apps that provide both, 88.0% have at least one possible discrepancy between the text of their privacy policy and their privacy label, which could be indicative of a potential compliance issue. We find that, on average, apps have 5.32 such potential compliance issues. We hope that ATLAS will help app developers, researchers, regulators, and mobile app stores alike. For example, app developers could use our classifier to check for discrepancies between their privacy policies and privacy labels, and regulators could use our system to help review apps at scale for potential compliance issues.
... Reading the data policy will take a lot of time, and most users will choose to quickly skip to the end and click "Agree". In 2008, a study in the United States showed that if all users in the United States read the online privacy policy word for word, the economic cost in one year would be as high as $781 billion [6]. However, in order to avoid responsibility, data processors are still increasing the length of the policy of formatting data. ...
Constructing data security rules is an important prerequisite for participating in global data governance. Chinas application to join the new digital rules represented by the Digital Economic Partnership Agreement is also one of the ways to try to improve its own data rules, and the protection of personal data rights is the focus of all kinds of data rules. However, informed and consent, public interest authorization and information disclosure, as three ways of data rights of derivative acquisition, also face various transformations in their application. This paper analyzes the current rules dilemma through literature analysis and comparative research trying to find a way out of the current dilemma in data rights of derivative acquisition.
... Although this might be legally adequate, whether this is an ethically valid form of consent has long been debated. 18 Extensive, densely written and sometimes buried policies, which require the client to agree in full or forgo the service, are often poorly read or understood, drawing doubt on whether consent is voluntary and informed. 19 This is even more relevant in the African rural context where users of cell phones may have less information about the technological infrastructure underlying cell phone use and how data are collected. ...
The movements of humans have a significant impact on population health. While studies of such movements are as old as public health itself, the COVID-19 pandemic has raised the profile of mobility research using digital technologies to track transmission routes and calculate the effects of health policies, such as lockdowns. In sub-Saharan Africa, the high prevalence of cell phone and smartphone use is a source of potentially valuable mobility data for public health purposes. Researchers can access call data records, passively collected in real time from millions of clients by cell phone companies, and associate these records with other data sets to generate insights, make predictions or draw possible policy implications. The use of mobility data from this source could have a range of significant benefits for society, from better control of infectious diseases, improved city planning, more efficient transportation systems and the optimisation of health resources. We discuss key ethical issues raised by public health studies using mobility data from cell phones in sub-Saharan Africa and identify six key ethical challenge areas: autonomy, including consent and individual or group privacy; bias and representativeness; community awareness, engagement and trust; function creep and accountability; stakeholder relationships and power dynamics; and the translation of mobility analyses into health policy. We emphasise the ethical importance of narrowing knowledge gaps between researchers, policymakers and the general public. Given that individuals do not really provide valid consent for the research use of phone data tracking their movements, community understanding and input will be crucial to the maintenance of public trust.
... Terms and conditions are generally presented in privacy policies using complicated, formal language that frequently lacks detail. As a result of the policy's complexity, users frequently choose the simpler route of unconditionally agreeing to them [4]. ...
Concerns about online privacy have become more prevalent as a result of the quick spread of internet services. However, customers are frequently presented with lengthy and complicated privacy policy documentation offered by service providers, which prevents them from reading and understanding these policies. In order to overcome this difficulty, this paper suggests a machine learning-based solution that makes it easier to comprehend privacy policies. To develop our method, we sincerely examined the privacy policies of the top 100 Indian-accessible internet services. Whenever a user visits a website, our tool automatically evaluates it and determines if the General Data Protection Regulation (GDPR)'s data protection guidelines are being followed. Users may use these ratings to determine if a certain privacy policy is in compliance with privacy-abiding practices or not. This approach seeks to narrow the knowledge gap between users and privacy policies, promoting informed choice-making and strengthening online privacy protection.
... 4 Data collected and research conducted in online platforms through reliance on the terms and conditions of these platforms also raise a problem about consent. Since it has been shown that individuals cannot practically or reasonably read and understand all of the terms and conditions of all platforms they use, these agreements cannot constitute a proper consent (McDonald and Cranor, 2008). 5 From a regulatory perspective, these agreements are superseded by communication laws of the countries where these platforms reside, which may explicitly permit aggregated and anonymised processing of such data for humanitarian or research purposes. ...
Human migration is an important societal issue with wide-ranging implications, and timely and accurate insights are increasingly needed for understanding the key factors to ensure the well-being of populations. New data sources, such as usage data from mobile phones and applications, remote sensing and satellite images, social media, event and news databases, and financial databases, enable data scientists to collaborate with migration scholars, to equip them with new quantitative tools, to address certain data gaps, and to supply empirical evidence for building and testing theories while complying with ethical requirements. In this book, we provide an overview of the major data sources and link them to migration and mobility in a way accessible to both migration scholars and data scientists, highlighting the relevant issues from multiple aspects, and offering broad social scientific and technical coverage. We describe many case studies about the use of data science in migration and mobility, as well as related areas, such as humanitarian aid. Most importantly, we give a comprehensive treatment of the legal and ethical concerns, discussing surveillance and dataveillance, implications to power structures, and potential misuses of large scale data processing, which need to be addressed to reap the benefits of data science without harming data subjects, or vulnerable groups such as refugees and asylum seekers.
... Finally, in the process of reading, the reader's focus is on what the text is saying; he is not focusing on this thing about whether it is true or false. McDonald (2004) expressed the view that critical reading stems from poststructuralist views of reading. This view presents the idea that the subjectivity of the reader is included whenever he is reading any text. ...
The purpose of this study is to enhance awareness about critical reading strategies among English language learners in public sector schools. The current study focused on high school English language learners and their level of understanding towards critical reading strategies. The nature of the current study is qualitative, in this study teacher researcher had observed that EFL learners, they were unaware about the use of critical reading strategies at secondary school level. This study is purely an action research conducted into three phases by using Stringers (2007) action research cycles. In first phase teacher researcher had analyzed the content of critical reading exercises from textbook published by Sindh textbook board Jamshoro. In the second phase researcher observed the research participants through observation checklist. In the third phase teacher researcher conducted the semi-structured interviews. In last to ensure the validity of data researcher had triangulated the collected data. The data collected in first phase was analyzed through content analysis using Tovani (2000) critical reading model. Moreover the data collected through observation checklist and semi-structured were analyzed through thematic analysis using Braun and Clarke model (2006). The results revealed that critical reading promotes critical reading comprehension, it helps in understanding the meaning behind the text. It is deduced from interviews data that EFL learners' comprehension level was low due to lack of critical reading strategies.
... In case there is a choice, though, (2) use cases for data are becoming more complex, making it harder for individuals to fully understand the impact of giving access to their data. The lengths of typical privacy policies 1 show the complexity of data processing, meaning a full understanding is questionable [see also 57,84]. This is exacerbated by the fact that (3) software tools are not static products. ...
Employees work in increasingly digital environments that enable advanced analytics. Yet, they lack oversight over the systems that process their data. That means that potential analysis errors or hidden biases are hard to uncover. Recent data protection legislation tries to tackle these issues, but it is inadequate. It does not prevent data misusage while at the same time stifling sensible use cases for data. We think the conflict between data protection and increasingly data-driven systems should be solved differently. When access to an employees' data is given, all usages should be made transparent to them, according to the concept of inverse transparency. This allows individuals to benefit from sensible data usage while addressing the potentially harmful consequences of data misusage. To accomplish this, we propose a new design approach for workforce analytics we refer to as inverse transparency by design. To understand the developer and user perspectives on the proposal, we conduct two exploratory studies with students. First, we let small teams of developers implement analytics tools with inverse transparency by design to uncover how they judge the approach and how it materializes in their developed tools. We find that architectural changes are made without inhibiting core functionality. The developers consider our approach valuable and technically feasible. Second, we conduct a user study over three months to let participants experience the provided inverse transparency and reflect on their experience. The study models a software development workplace where most work processes are already digital. Participants perceive the transparency as beneficial and feel empowered by it. They unanimously agree that it would be an improvement for the workplace. We conclude that inverse transparency by design is a promising approach to realize accepted and responsible people analytics.
... IoT TAPs are an emerging technology that gained traction in academia over 6. See also [40] that estimated that the time required for reading policies in a year would on average exceed 200 hours for a user. 7. Dark patterns are "instances where designers use their knowledge of human behavior (e.g., psychology) and the desires of end users to implement deceptive functionality that is not in the user's best interest" [28]. ...
Users are often overwhelmed by privacy decisions to manage their personal data, which can happen on the web, in mobile, and in IoT environments. These decisions can take various forms -- such as decisions for setting privacy permissions or privacy preferences, decisions responding to consent requests, or to intervene and ``reject'' processing of one's personal data --, and each can have different legal impacts. In all cases and for all types of decisions, scholars and industry have been proposing tools to better automate the process of privacy decisions at different levels, in order to enhance usability. We provide in this paper an overview of the main challenges raised by the automation of privacy decisions, together with a classification scheme of the existing and envisioned work and proposals addressing automation of privacy decisions.
... A group of university professors once calculated that it would take seventy-six working days to read all the privacy documents one agrees to in a single year. 8 Even if our data starts off in a secure environment, there's no guarantee it will stay there. ...
How the global financial services sector has been transformed by artificial intelligence, data science, and blockchain.
Artificial intelligence, big data, blockchain, and other new technologies have upended the global financial services sector, creating opportunities for entrepreneurs and corporate innovators. Venture capitalists have helped to fund this disruption, pouring nearly $500 billion into fintech over the last five years. This book offers global perspectives on technology-fueled transformations in financial services, with contributions from a wide-ranging group of academics, industry professionals, former government officials, and current government advisors. They examine not only the struggles of rich countries to bring the old analog world into the new digital one but also the opportunities for developing countries to “leapfrog” directly into digital.
The book offers accessible explanations of blockchain and distributed ledger technology and explores big data analytics. It considers, among other things, open banking, platform-based strategies for banks, and digital financial services. Case studies imagine possible future fintech-government interaction, emphasizing that legal and regulatory frameworks can help to create trust in financial processes. The contributors offer novel takes and unexpected insights that will be of interest to fintech experts and nonexperts alike.
Contributors
Ajay Bhalla, Michelle Chivunga, John D'Agostino, Mark Flood, Amias Moore Gerety, Oliver R. Goodenough, Thomas Hardjono, Sharmila Kassam, Boris Khentov, Alexander Lipton, Lev Menand, Pinar Ozcan, Alex Pentland, Matthew Reed, David L. Shrier, Markos Zachariadis
The Digital Markets Act (DMA) captures gatekeeper power to address the lack of contestability and unfairness in digital markets. Its provisions imbricate into the regulatory landscape bearing in mind complementarity regarding other acts of Union law which also apply to certain aspects of the digital arena, namely the General Data Protection Regulation (GDPR) or the e-Privacy Directive.
The DMA does not override the provisions of these rules, although the practical implementation of its do’s and don’ts will question the value of non-economic interests which have been at the forefront of EU policy at large in their interaction with digital business models. In the particular case of the intersection between privacy and antitrust, Articles 5(2) and 6(10) of the DMA stand out as the two key areas where the interpretation of the GDPR will play a major role, namely through the force of consent, legal basis, and user choice. Although both provisions impose negative and positive obligations on personal data, their role is tempered when the user is presented with a specific choice and grants consent to the gatekeeper to combine and use personal data.
The paper analyses the potential implications of both provisions in light of the existence of power and information asymmetries between gatekeepers and end users. The paper navigates the cases that have inspired the framework of the DMA in this regard, from an antitrust and data protection perspective. The paper identifies that the interaction between the concept of consent and the massive collection and processing of personal data is designed according to a circular concept. The DMA builds up its provisions on Articles 5 and 6 on the same premise. The paper identifies the circularity which the DMA’s enforcers might incur when enforcing the regulatory instrument.
The present article approaches the paradigm of valuing consent to treat personal data as provided on the General Law of data Protection (GLDP), due to confrontation with its effectiveness, based on informative self-determination. At this point, some insufficiencies of the model to the adequate adjustment in fundamental rights and in alternatives feasible to implement the idea of informative self-determination will be assessed based on the deductive methodology of literature review. It is pointed out that the merely formal consent cannot be enough to free consent protection due to cognitive limitations, asymmetry among powers, need of service usufruct, use of technical terms, time shortage and difficulty to manage future risks. On the other hand, some trends are highlighted to mitigate this insufficiency, be it through information systems such as privacy by design, accountability, offer of paid premium services without counterpart of the indiscriminate assignment of data and other contextual analyses.
This chapter introduces general concepts of usable privacy and information security, needed as a foundation for the subsequent chapters.
Employees work in increasingly digital environments that enable advanced analytics. Yet, they lack oversight over the systems that process their data. That means that potential analysis errors or hidden biases are hard to uncover. Recent data protection legislation tries to tackle these issues, but it is inadequate. It does not prevent data misusage while at the same time stifling sensible use cases for data.
We think the conflict between data protection and increasingly data-driven systems should be solved differently. When access to an employees' data is given, all usages should be made transparent to them, according to the concept of inverse transparency. This allows individuals to benefit from sensible data usage while addressing the potentially harmful consequences of data misusage. To accomplish this, we propose a new design approach for workforce analytics software we refer to as inverse transparency by design.
To understand the developer and user perspectives on the proposal, we conduct two exploratory studies with students. First, we let small teams of developers implement analytics tools with inverse transparency by design to uncover how they judge the approach and how it materializes in their developed tools. We find that architectural changes are made without inhibiting core functionality. The developers consider our approach valuable and technically feasible. Second, we conduct a user study over three months to let participants experience the provided inverse transparency and reflect on their experience. The study models a software development workplace where most work processes are already digital. Participants perceive the transparency as beneficial and feel empowered by it. They unanimously agree that it would be an improvement for the workplace. We conclude that inverse transparency by design is a promising approach to realize accepted and responsible people analytics.
Police, “engaged in the often competitive enterprise of ferreting out crime” (Johnson v. United States (U.S. 1948)), might be readily expected to turn to each new technology to help them catch and prosecute criminals. And they do. But in the digital age, when technologies explode at a hare’s pace and courts interpreting and Congress legislating police use of those technologies trail far behind at a tortoise’s pace (MacLean, 2014), there is much room, before and after the courts and Congress catch up, for unconstitutional intrusion into privacy rights protected by the Fourth Amendment in the Social Contract. That is awful but lawful.
The actions and inactions of intermediaries have resulted in both private and public harms. Public harms include the illicit influence of voting behavior through manipulation of public opinion, directly undermining democracy. Although the Supreme Court of India recognized such public harms that result from intermediary behavior, it did not go beyond the privacy framework in addressing these harms. Based on an analysis of Indian law, the article proposes a new normative category—constitutional harms—to refocus attention on a special class of public harms, thereby opening up the debate on new remedies to address such harms.
This article aims to identify common practices in Indonesian e-commerce regarding terms of use and privacy policies. Website visit rankings from Alexa and Similarweb were used to identify the 10 most commonly visited e-commerce sites in Indonesia. Then, placement, length, and content structure of the terms of service and privacy policies of these websites were compared. Findings suggest that the information provided by these documents is sufficient and legally compliant, although some of the websites appear to disregard their importance. The actual contents of these documents were not analyzed and are thus open for further study. The information provided in this article may give merchants intending to open e-commerce stores in Indonesia some insight into how the protection of consumers’ personal data leads to better service. This paper also proposes a simple framework for assessing the extent to which an e-commerce website successfully ensures that consumers agree and consent to its terms of use without burdening them with lengthy and obscure legal documents. Keywords: online privacy e-commerce term of use
Economic policy determines the intensity of competition in markets. This gives incumbents the incentive to use their financial resources to influence policymaking in order to restrict competition and maintain or increase economic profits. Public authorities should promote the use of profits rather in welfare-enhancing or neutral ways. Is competition law an adequate tool to promote this goal? This paper aims to ground the discussion on legal administrability considerations. The focus is therefore on whether we can design legal standards and identify evidence that courts can use to assess the tradeoffs between static efficiency, political influence of large corporations, and innovation. This paper argues that if political considerations are to be taken into account in antitrust analysis, these should be made explicit and the evidence at hand in each case should be considered, in order to avoid enforcement guided by assumptions—such as that increases in market concentration always lead to risks in terms of political influence—that can otherwise be revised on a case-by-case basis.
This chapter challenges the current business models of the dominant platforms in the digital economy. In the search for alternatives, and towards the aim of achieving digital sovereignty, it proceeds in four steps: First, it discusses scholarly proposals to constitute a new intellectual property right on data. Second, it examines four models of data governance distilled from the literature that seek to see data administered (1) as a private good regulated by the market, (2) as a public good regulated by the state, (3) as a common good managed by a commons’ community, and (4) as a data trust supervised by means of stewardship by a trustee. Third, the strengths and weaknesses of each of these models, which are ideal types and serve as heuristics, are critically appraised. Fourth, data trusteeship which at present seems to be emerging as a promising implementation model for better data governance, is discussed in more detail, both in an empirical-descriptive way, by referring to initiatives in several countries, and analytically, by highlighting the challenges and pitfalls of data trusteeship.
A privacy notice is a document/notification that is addressed to consumers, describing how their personal information will be handled. While browsing the Internet, installing an app on smartphone, setting up a smart sensor or IoT devices in personal spaces, consumers are often asked to consent to privacy notices. Ideally, the consumer is expected to read and understand the notice and give an informed consent. These notices are often lengthy and complicated, containing legal-technical jargons and ambiguous statements describing commercial use of personal data. Most people reflexively choose “I consent”, unknowingly agreeing to unfair-deceptive practices. Given the ubiquity of IoT and thus ubiquity of (personal) data collection, the reliance on notice and consent is inappropriate. In this article, we present the challenges of the notice and consent paradigm, and explore the idea of privacy-assistive solutions to enhance consumer privacy awareness and control in IoT.KeywordsPrivacyautomated notice processinginformed consentconsumer controlprivacy-assistive technologyprivacy-enhancing technology
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