Senseful Consense: Towards Simplified Cookie Banners using Plain Language
- URL: http://arxiv.org/abs/2609.10271v1
- Date: Wed, 09 Sep 2026 14:54:50 GMT
- Title: Senseful Consense: Towards Simplified Cookie Banners using Plain Language
- Abstract summary: This study investigates the impact of applying plain language to cookie banners within the IAB Transparency & Consent Framework (TCF)<n>By substituting complex legal terms with simplified descriptions, we successfully demonstrated that the comprehension barrier can be lowered from a college-graduate level to a 7th-grade level.<n>We conclude that while plain language is a vital tool for digital accessibility, it must be paired with standardised implementation guidelines to ensure that cookie banners are both readable and informative.
- Score: 42.97962051360944
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: While the GDPR and ePrivacy Directive mandate that consent information must be clear and accessible, most modern cookie banners remain obscured by technical jargon, vague phrasing, and frequent content overload or underload. This feasibility study investigates the impact of applying plain language (Einfache Sprache) to cookie banners within the IAB Transparency & Consent Framework (TCF). In our study, we analysed cookie banner texts from 200 websites, using AI-based mapping to categorise extracted content into standardised processing purposes. By substituting complex legal terms with simplified descriptions, we successfully demonstrated that the comprehension barrier can be lowered from a college-graduate level to a 7th-grade level. However, the effectiveness of plain language is inherently constrained by the informativeness of the original content; it cannot compensate for banners that omit legally required details. We conclude that while plain language is a vital tool for digital accessibility, it must be paired with standardised implementation guidelines to ensure that cookie banners are both readable and informative.
Related papers
- Gloss-Free Representation Learning for Cross-Dataset Sign Spotting [0.8590590965724384]
We study whether weak transcript-based supervision can pretrain a reusable sign encoder in this setting.<n>We pretrain on TSL-News, a new Turkish broadcast corpus, using pseudo-gloss labels derived from transcripts.<n>We evaluate the learned representations via cross-dataset sign spotting on a new TSL Spotting Benchmark.
arXiv Detail & Related papers (2026-08-11T18:31:57Z) - Eta-WavLM: Efficient Speaker Identity Removal in Self-Supervised Speech Representations Using a Simple Linear Equation [1.3874486202578669]
Self-supervised learning (SSL) has reduced the reliance on expensive labeling in speech technologies by learning meaningful representations from unannotated data.<n>We propose a novel disentanglement method that linearly decomposes SSL representations into speaker-specific and speaker-independent components.
arXiv Detail & Related papers (2025-05-25T19:05:26Z) - Lost in Translation, Found in Context: Sign Language Translation with Contextual Cues [56.038123093599815]
Our objective is to translate continuous sign language into spoken language text.<n>We incorporate additional contextual cues together with the signing video.<n>We show that our contextual approach significantly enhances the quality of the translations.
arXiv Detail & Related papers (2025-01-16T18:59:03Z) - LLM-Assisted Content Conditional Debiasing for Fair Text Embedding [37.92120550031469]
This paper proposes a novel method for learning fair text embeddings.
We define a novel content-conditional equal distance (CCED) fairness for text embeddings.
We also introduce a content-conditional debiasing (CCD) loss to ensure that embeddings of texts with different sensitive attributes but identical content maintain the same distance from the embedding of their corresponding neutral text.
arXiv Detail & Related papers (2024-02-22T01:20:51Z) - Natural Language Decompositions of Implicit Content Enable Better Text Representations [52.992875653864076]
We introduce a method for the analysis of text that takes implicitly communicated content explicitly into account.<n>We use a large language model to produce sets of propositions that are inferentially related to the text that has been observed.<n>Our results suggest that modeling the meanings behind observed language, rather than the literal text alone, is a valuable direction for NLP.
arXiv Detail & Related papers (2023-05-23T23:45:20Z) - Gloss-Free End-to-End Sign Language Translation [59.28829048788345]
We design the Gloss-Free End-to-end sign language translation framework (GloFE)
Our method improves the performance of SLT in the gloss-free setting by exploiting the shared underlying semantics of signs and the corresponding spoken translation.
We obtained state-of-the-art results on large-scale datasets, including OpenASL and How2Sign.
arXiv Detail & Related papers (2023-05-22T09:57:43Z) - It's not Greek to mBERT: Inducing Word-Level Translations from
Multilingual BERT [54.84185432755821]
multilingual BERT (mBERT) learns rich cross-lingual representations, that allow for transfer across languages.
We study the word-level translation information embedded in mBERT and present two simple methods that expose remarkable translation capabilities with no fine-tuning.
arXiv Detail & Related papers (2020-10-16T09:49:32Z) - Improving Disentangled Text Representation Learning with
Information-Theoretic Guidance [99.68851329919858]
discrete nature of natural language makes disentangling of textual representations more challenging.
Inspired by information theory, we propose a novel method that effectively manifests disentangled representations of text.
Experiments on both conditional text generation and text-style transfer demonstrate the high quality of our disentangled representation.
arXiv Detail & Related papers (2020-06-01T03:36:01Z) - Learning Interpretable and Discrete Representations with Adversarial
Training for Unsupervised Text Classification [87.28408260725138]
TIGAN learns to encode texts into two disentangled representations, including a discrete code and a continuous noise.
The extracted topical words for representing latent topics show that TIGAN learns coherent and highly interpretable topics.
arXiv Detail & Related papers (2020-04-28T02:53:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.