CLAUDETTE: an automated detector of potentially unfair clauses in online terms of service

Lippi, Marco; Pałka, Przemysław; Contissa, Giuseppe; Lagioia, Francesca; Micklitz, Hans‐W.; Sartor, Giovanni; Torroni, Paolo

doi:10.1007/s10506-019-09243-2

Cited by 106 publications

(110 citation statements)

References 25 publications

(22 reference statements)

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“…This structured querying technique offers an advantage over approaches based on heuristics and keyword analysis (e.g., [3,6]); it allows us to better cover text with varying wordings but similar semantics. Further, this technique avoids the shortcomings of the other approaches that directly use machine learning to quantify the goals (e.g., [5,10,12]); it is more flexible for adapting the goals (i.e., queries) as needed, without having to create new labeling data for each new goal.…”

Section: Methodology Overviewmentioning

confidence: 99%

“…They found a frequent presence of unclear language, problematic processing, and insufficient information. They used Claudette, a recent system designed for the detection of such types of issues in privacy policies [12]. Tesfay et al introduced a tool, inspired by the GDPR to classify privacy policy content into eleven privacy aspects [23].…”

Section: Privacy Policies After the Gdprmentioning

confidence: 99%

See 1 more Smart Citation

The Privacy Policy Landscape After the GDPR

Lindén¹,

Khandelwal²,

Harkous

et al. 2020

Proceedings on Privacy Enhancing Technologies

104

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The EU General Data Protection Regulation (GDPR) is one of the most demanding and comprehensive privacy regulations of all time. A year after it went into effect, we study its impact on the landscape of privacy policies online. We conduct the first longitudinal, in-depth, and at-scale assessment of privacy policies before and after the GDPR. We gauge the complete consumption cycle of these policies, from the first user impressions until the compliance assessment. We create a diverse corpus of two sets of 6,278 unique English-language privacy policies from inside and outside the EU, covering their pre-GDPR and the post-GDPR versions. The results of our tests and analyses suggest that the GDPR has been a catalyst for a major overhaul of the privacy policies inside and outside the EU. This overhaul of the policies, manifesting in extensive textual changes, especially for the EU-based websites, comes at mixed benefits to the users. While the privacy policies have become considerably longer, our user study with 470 participants on Amazon MTurk indicates a significant improvement in the visual representation of privacy policies from the users' perspective for the EU websites. We further develop a new workflow for the automated assessment of requirements in privacy policies. Using this workflow, we show that privacy policies cover more data practices and are more consistent with seven compliance requirements post the GDPR. We also assess how transparent the organizations are with their privacy practices by performing specificity analysis. In this analysis, we find evidence for positive changes triggered by the GDPR, with the specificity level improving on average. Still, we find the landscape of privacy policies to be in a transitional phase; many policies still do not meet several key GDPR requirements or their improved coverage comes with reduced specificity.

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Section: Methodology Overviewmentioning

confidence: 99%

Section: Privacy Policies After the Gdprmentioning

confidence: 99%

The Privacy Policy Landscape After the GDPR

Lindén¹,

Khandelwal²,

Harkous

et al. 2020

Proceedings on Privacy Enhancing Technologies

104

View full text Add to dashboard Cite

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“…Fig 2a sketches the steps of this pipeline. In some consumer-oriented applications [32], such as the detection of unfair contract clauses [33], a first stage could identify sentences expressing potentially unfair clauses through a coarse-grained classifier (see Figure 2b), whereas a second stage could additionally recognize the category of unfairness among a set of pre-defined cases (e.g., arbitration, limitation of liability, etc. ), usually on the basis of the context provided by other relevant sentences within a certain document scope.…”

Section: Prediction As a Servicementioning

confidence: 99%

Parallelizing Machine Learning as a service for the end-user

Loreti¹,

Lippi²,

Torroni³

2020

Future Generation Computer Systems

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As Machine Learning (ML) applications are becoming ever more pervasive, fully-trained systems are made increasingly available to a wide public, allowing end-users to submit queries with their own data, and to efficiently retrieve results. With increasingly sophisticated such services, a new challenge is how to scale up to ever growing user bases. In this paper, we present a distributed architecture that could be exploited to parallelize a typical ML system pipeline. We propose a case study consisting of a text mining service, and discuss how the method can be generalized to many similar applications. We demonstrate the significance of the computational gain boosted by the distributed architecture by way of an extensive experimental evaluation.

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“…One such tool could be the digital control of unfair terms, misleading marketing practices, and data privacy policies. It is for the digital society to develop such tools in co-operation between nongovernmental organizations and front-running academic research (Lipp et al 2019).…”

Section: The Digital World Privacy and Consumer Protection Paradigmsmentioning

confidence: 99%