Breaking Down Walls of Text: How Can NLP Benefit Consumer Privacy? (2021)

Abstract: Privacy plays a crucial role in preserving democratic ideals and personal autonomy. The dominant legal approach to privacy in many jurisdictions is the “Notice and Choice” paradigm, where privacy policies are the primary instrument used to convey information to users. However, privacy policies are long and complex documents that are difficult for users to read and comprehend. We discuss how language technologies can play an important role in addressing this information gap, reporting on initial progress towards helping three specific categories of stakeholders take advantage of digital privacy policies: consumers, enterprises, and regulators. Our goal is to provide a roadmap for the development and use of language technologies to empower users to reclaim control over their privacy, limit privacy harms, and rally research efforts from the community towards addressing an issue with large social impact. We highlight many remaining opportunities to develop language technologies that are more precise or nuanced in the way in which they use the text of privacy policies.

## introduction privacy is a fundamental right central to a democratic society, in which individuals can operate as autonomous beings free from undue interference from other individuals or entities @xcite . however, certain functions of privacy, such as the power to grant or deny access to one's personal information, are eroded by modern commercial and business practices that involve vast collection, linking, sharing, and processing of digital personal information through an opaque network, often without data subjects' knowledge or consent. in many jurisdictions, online privacy is largely governed by "notice and choice" @xcite . under this framework, data-collecting and data-processing entities publish privacy policies that disclose their data practices. theoretically, users are free to make choices about which services and products they use based on the disclosures made in these policies. thus, the legitimacy of this framework hinges on users reading a large number of privacy policies to understand what data can be collected and how that data can be processed before making informed privacy decisions. in practice, people seldom read privacy policies, as this would require prohibitive amounts of their time @xcite @xcite @xcite . thus, an opportunity exists for language technologies to bridge this gap by processing privacy policies to meet the needs of internet and mobile users. nlp has made inroads in digesting large amounts of text in domains such as scientific publications and news @xcite @xcite @xcite , with several practical tools based on these technologies helping users every day @xcite @xcite . these domains have also received considerable research attention: several benchmark datasets and technologies are based in texts from these domains @xcite @xcite . we highlight that the privacy domain can also benefit from increased research attention from the community. moreover, technologies developed in the privacy domain have potential for significant and large-scale positive social impact-the affected population includes virtually every internet or mobile user @xcite . automated processing of privacy policies opens the door to a number of scenarios where language technologies can be developed to support users in the context of different tasks. this includes saving data subjects the trouble of having to read the entire text of policies when they are typically only concerned about one or a small number of issues (e.g., determining whether they can opt out of some practices or whether some of their data might be shared with third parties). it includes helping companies ensure that they are compliant and that their privacy policies are consistent with what their code actually does. it also includes supporting regulators, as they face the daunting task of enforcing compliance across an ever-growing collection of software products and processes, including sophisticated data collection and use practices. in this work, we conduct an extensive survey of initial progress in applying nlp to address limitations of the notice and choice model. we expect our work to serve as a useful starting point for practitioners to familiarize themselves with technological progress in this domain, by providing both an introduction to the basic privacy concerns and frameworks surrounding privacy policies, as well as an account of applications for which language technologies have been developed. finally, we highlight many remaining opportunities for nlp technologies to extract more precise, more nuanced, and ultimately more useful information from privacy policy textdescribing key challenges in this area and laying out a vision for the future. ## privacy as a social good in 1890, warren and brandeis defined the right to privacy as "the right to be let alone"(warren and brandeis, 1890). more recently, westin defined the right as "the claim of individuals, groups, or institutions to determine for themselves when, how, and to what extent information about them is communicated to others" @xcite . a primary aspiration of privacy is to allow for the separation of individual and society as a means of fostering personal autonomy. to that end, privacy "protects the situated practices of boundary management through which the capacity for self-determination develops," and further "shelters dynamic, emergent subjectivity from the efforts of commercial and government actors to render individuals and communities fixed, transparent, and predictable" @xcite . privacy, therefore, is "foundational to the practice of informed and reflective citizenship," and serves as "an indispensable structural feature of liberal democratic political systems" @xcite . when privacy is threatened, we risk losing the chance for critical self-reflection of political pro-cesses and social norms. indeed, privacy undergirds the concepts of human dignity and other key values, such as the freedoms of association and speech. for these reasons and others, privacy is regarded as a fundamental human right @xcite . in the digital age, privacy is threatened by aggressive, rapid, and largely automated collection, linking, sharing, and processing of digital personal information. digital privacy is intrinsically linked to the fundamental ethical principles of transparency, fairness and agency. the dominant paradigm to address these principles in the united states and most legal jurisdictions around the world, is the 'notice and choice' regulatory framework @xcite . 'notice and choice' regimes are based on the presupposition that consumers will adequately manage their privacy, if provided sufficient information about how their data will be collected, used and managed, as well as offered meaningful choices. today, 'notice' is often practically realized through publishing privacy policies, which are long and verbose documents that users are expected to read and understand. 'choice' is often limited to the user clicking 'i agree' to the privacy policy, or even interpreting their continued use of the service as some sort of meaningful consent to the terms of the policy. the 'notice and choice' framework is fundamentally broken. in practice, users seldom read privacy policies @xcite @xcite and it is prohibitively expensive for them to even do so. @xcite estimate that if internet users were to actually read the privacy policies of the websites they visited, they would have to spend roughly 250 hours each year just reading tabular understanding reasons we can share your personal information does x share? can you limit this sharing? for our everyday business purposes ... yes no for our everyday marketing purposes ... yes no for joint marketing with other companies no we don't share indeed, 91% of people in the u.s have reported feeling like they have lost control over their information @xcite . moreover, recent privacy laws such as the eu's general data protection regulation (gdpr) (regulation, 2016) still fail to address the critical limitation of notice and choice: the continued reliance on users to read and understand a large number of privacy policies. studies have shown that gdpr requirements have actually resulted in longer privacy policies @xcite , and users still encounter unreadable privacy policies @xcite . the lack of respect for individuals' rights to privacy also has implications for society. with social platforms in particular having access to an unprecedented scale of information about human behaviour, @xcite discuss that users' polarization and confirmation bias can play a role in spreading misinformation on social platforms. @xcite report that particular groups of lessprivileged users on the internet are uniquely vulnerable to various forms of surveillance and privacy harms, which could widen existing economic gaps. @xcite describe privacy as central to human autonomy in social relationships. in this work, we examine the potential of language technologies in enabling people to derive the benefits of their rights to transparency, fairness and agency. ## can nlp help privacy? privacy policies present interesting challenges for nlp practitioners, as they often feature characteristic aspects of language that remain under-examined or difficult to process (table. 1). for example, while many policies discuss similar issues surrounding how user data is collected, managed and stored, policy silence about certain data practices may carry great weight from a legal, policy, and regulatory perspective. 1 in the privacy policy domain, understanding what has not been said in a privacy policy (policy silence) is just as important as understanding what is said @xcite . further, though policies tend to feature literal language (compared to more subjective domains like literature or blog posts), processing them ef-task goal consumer regulator enterprise data practice identification @xcite annotate segments of privacy policies with described data practices. opt-out identification @xcite extract opt-out choices buried in privacy policy text. compliance analysis @xcite analyze mobile app code and privacy policy to identify potential compliance issues. privacy question-answering @xcite allow consumers to selectively query privacy policies for issues that are important to them. policy summarization @xcite construct summaries to aid consumers to quickly digest the content of privacy policies. readability analysis @xcite characterize the ease of understanding or comprehension of privacy policies. we survey current efforts to apply nlp in the privacy domain, discussing both existing task formulations as well as future areas in this domain where language technologies can have impact. 2 2 our survey includes relevant papers from major nlp venues, including acl, emnlp, naacl, eacl, coling, conll, semeval, tacl, and cl. we supplemented these publications with a review of the literature at venues such as soups, pets, www, acm, and ndss. we also included relevant legal venues, such as law reviews and journals. ## progress and challenges although privacy policies have legal effects for most internet users, these types of texts constitute an underserved domain in nlp. nlp has the potential to play a role in easing user burden in understanding salient aspects of privacy policies, help regulators enforce compliance and help developers enhance the quality of privacy policies by reducing the effort required to construct them. yet, the privacy domain presents several challenges that require specialized resources to deal with effectively. we describe some of these distinctive challenges, as well as the capabilities that will need to be developed to process policies satisfactorily. ## ethical considerations while nlp has the potential to benefit consumer privacy, we emphasize there are also ethical considerations to be taken in account. these include: bias of agent providing technology: a factor that must be considered in the practical deployment of nlp systems in this domain is the incentives of the entity creating or providing the technology. for example, the incentives of a company that develops a qa system to answer questions about its own privacy policy may not align with those of a trusted third-party privacy assistant that reviews the privacy policies of many different companies. this information also needs to be communicated in an accurate and unbiased fashion to users. user trust: while nlp systems have the potential to digest policy text and present information to users, nlp systems are seldom completely accurate, and therefore it is important that users be appropriately informed of these limitations. for example, if a qa system communicates a data practice incorrectly in response to a users' question and the user encounters privacy harms contrary to their expectations as a result, they may lose trust in the system. it is important to also identify appropriate disclaimers to accompany nlp systems to manage user expectations. discriminatory outcomes: it is possible that different populations will benefit to different extents from the developed technologies, and we are yet unable to anticipate precisely where the benefits will accrue. for example, users with higher degrees of privacy literacy may be able to take better advantage of a developed qa system. technological solutionism: it is important to consider that while language technologies have the potential to considerably alleviate user burden in reading privacy policies, they are unlikely to completely resolve the issue that users are unable to read and review a multitude of privacy policies everyday. advances toward addressing the limitations of notice and choice will also require progress in regulation and enforcement by regulatory bodies to ensure that enterprises are more accurate in their disclosures and use clearer language, in tandem with creative technological solutions. ## conclusion privacy is about the right of people to control the collection and use of their data. today privacy relies on the 'notice and choice' framework, which assumes that people actually read the text of privacy policies. this is a fantasy as users do not have the time to do so. in this article, we summarize how language technologies can help overcome this challenge and support the development of solutions that assist customers, technology providers and regulators. we reviewed early successes and presented a vision of how nlp could further help in the future. we hope this article will motivate nlp researchers to contribute to this vision and empower people to regain control over their privacy.