A comprehensive keyword analysis of online privacy policies

Kaur, Jasmin; Dara, Rozita; Obimbo, Charlie; Song, Fei; Menard, Karen

doi:10.1080/19393555.2019.1606368

Cited by 16 publications

(25 citation statements)

References 18 publications

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“…Noteworthy works include identification of policy segments commenting on specific data practices (Wilson et al, 2016b), extraction of opt-out choices, and their provisions in policy text (Sathyendra et al, 2016;Mysore Sathyendra et al, 2017), and others (Bhatia and Breaux, 2015;. Kaur et al (2018) used a keyword-based technique to compare online privacy policies. Natural language processing (NLP) techniques such as text alignment ), text classification (Harkous et al, 2018;Zimmeck et al, 2019;Wilson et al, 2016a) and question answering (Shvartzshanider et al, 2018;Harkous et al, 2018; has been studied in prior works to facilitate privacy policy analysis.…”

Section: Related Workmentioning

confidence: 99%

“…Related WorkThe Usable Privacy Project(Sadeh et al, 2013) has made several attempts to automate the analysis of privacy policies(Wilson et al, 2016a;Zimmeck et al, 2019). Noteworthy works include identification of policy segments commenting on specific data practices(Wilson et al, 2016b), extraction of opt-out choices, and their provisions in policy text(Sathyendra et al, 2016;Mysore Sathyendra et al, 2017), and others(Bhatia and Breaux, 2015; Kaur et al (2018). used a keyword-based technique to compare online privacy policies.…”

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confidence: 99%

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PolicyQA: A Reading Comprehension Dataset for Privacy Policies

Ahmad

Chi

Tian

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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Privacy policy documents are long and verbose. A question answering (QA) system can assist users in finding the information that is relevant and important to them. Prior studies in this domain frame the QA task as retrieving the most relevant text segment or a list of sentences from the policy document given a question. On the contrary, we argue that providing users with a short text span from policy documents reduces the burden of searching the target information from a lengthy text segment. In this paper, we present PolicyQA, a dataset that contains 25,017 reading comprehension style examples curated from an existing corpus of 115 website privacy policies. PolicyQA provides 714 human-annotated questions written for a wide range of privacy practices. We evaluate two existing neural QA models and perform rigorous analysis to reveal the advantages and challenges offered by PolicyQA.

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

PolicyQA: A Reading Comprehension Dataset for Privacy Policies

Ahmad

Chi

Tian

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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“…Transparency can be ensured through data license agreements. Since most privacy policies and terms of use agreements use legal terminologies, it can be difficult for farmers to fully understand the content of these documents (Kaur et al, 2018). The ambiguity of agreements and legal frameworks around data collection, processing, and sharing is another issue that may result in an invasion of farmers' privacy and can lead to misuse of data.…”

Section: Recommendations For Agricultural Technology Providersmentioning

confidence: 99%

“…In the agreements, ATPs should be clear and transparent about what data are collected from farmers and their farms, for what purpose data are used, and possibly even what data are used in building the AI models. Moreover, collected data should not be shared with third parties without farmers' consent (Kaur et al, 2018). Farmers should be given the choice to give or withdraw consent for the use of their data.…”

Section: Recommendations For Agricultural Technology Providersmentioning

confidence: 99%

Recommendations for ethical and responsible use of artificial intelligence in digital agriculture

Dara

Fard

Kaur

2022

Front. Artif. Intell.

Self Cite

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Artificial intelligence (AI) applications are an integral and emerging component of digital agriculture. AI can help ensure sustainable production in agriculture by enhancing agricultural operations and decision-making. Recommendations about soil condition and pesticides or automatic devices for milking and apple picking are examples of AI applications in digital agriculture. Although AI offers many benefits in farming, AI systems may raise ethical issues and risks that should be assessed and proactively managed. Poor design and configuration of intelligent systems may impose harm and unintended consequences on digital agriculture. Invasion of farmers' privacy, damaging animal welfare due to robotic technologies, and lack of accountability for issues resulting from the use of AI tools are only some examples of ethical challenges in digital agriculture. This paper examines the ethical challenges of the use of AI in agriculture in six categories including fairness, transparency, accountability, sustainability, privacy, and robustness. This study further provides recommendations for agriculture technology providers (ATPs) and policymakers on how to proactively mitigate ethical issues that may arise from the use of AI in farming. These recommendations cover a wide range of ethical considerations, such as addressing farmers' privacy concerns, ensuring reliable AI performance, enhancing sustainability in AI systems, and reducing AI bias.

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“…Standard Contractual Clauses to protect your personal data...In certain situations, the courts, law enforcement agencies, regulatory agencies or security authorities in those countries might be entitled to access your personal information." Moreover, regulations have huge impacts on privacy policies (Kaur et al, 2018), especially for the enacting of General Data Protection Regulation (GDPR) 2 . For instance, The New York Times privacy policy was updated on May 24th, 2018 (one day before GDPR was formally put into action) and a term specifying International Data Transfer, which is explicitly required in GDPR, was added.…”

Section: 7cmmentioning

confidence: 99%

APPCorp: A Corpus for Android Privacy Policy Document Structure Analysis

Liu¹,

Guo²,

Zhao³

et al. 2020

Preprint

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With the increasing popularity of mobile devices and the wide adoption of mobile Apps, an increasing concern of privacy issues is raised. Privacy policy is identified as a proper medium to indicate the legal terms, such as GDPR, and to bind legal agreement between service providers and users. However, privacy policies are usually long and vague for end users to read and understand. It is thus important to be able to automatically analyze the document structures of privacy policies to assist user understanding. In this work we create a manually labelled corpus containing 167 privacy policies (of more than 447K words and 5, 276 annotated paragraphs). We report the annotation process and details of the annotated corpus. We also benchmark our data corpus with 4 document classification models, thoroughly analyze the results and discuss challenges and opportunities for the research committee to use the corpus. We release our labelled corpus as well as the classification models for public access.

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A comprehensive keyword analysis of online privacy policies

Cited by 16 publications

References 18 publications

PolicyQA: A Reading Comprehension Dataset for Privacy Policies

PolicyQA: A Reading Comprehension Dataset for Privacy Policies

Recommendations for ethical and responsible use of artificial intelligence in digital agriculture

APPCorp: A Corpus for Android Privacy Policy Document Structure Analysis

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