An Interpretive Framework for Narrower Immunity Under Section 230 of the Communications Decency Act

• URL: http://arxiv.org/abs/2306.04461v1
• Date: Mon, 5 Jun 2023 14:10:07 GMT
• Title: An Interpretive Framework for Narrower Immunity Under Section 230 of the Communications Decency Act
• Authors: Gregory M. Dickinson
• Abstract summary: Almost all courts to interpret Section 230 of the Communications Decency Act have construed its ambiguously worded immunity provision broadly. This analysis examines the text and history of Section 230 in light of two strains of pre-Internet vicarious liability defamation doctrine. It concludes that the immunity provision of Section 230, though broad, was not intended to abrogate entirely traditional common law notions of vicarious liability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Almost all courts to interpret Section 230 of the Communications Decency Act have construed its ambiguously worded immunity provision broadly, shielding Internet intermediaries from tort liability so long as they are not the literal authors of offensive content. Although this broad interpretation effects the basic goals of the statute, it ignores several serious textual difficulties and mistakenly extends protection too far by immunizing even direct participants in tortuous conduct. This analysis, which examines the text and history of Section 230 in light of two strains of pre-Internet vicarious liability defamation doctrine, concludes that the immunity provision of Section 230, though broad, was not intended to abrogate entirely traditional common law notions of vicarious liability. Some bases of vicarious liability remain, and their continuing validity both explains the textual puzzles courts have faced in applying Section 230 and undergirds the push by a small minority of courts to narrow the section's immunity provision.

Related papers

• Ambiguity is the last thing you need [0.0]
  Clear legal language forms the backbone of a contract for numerous reasons. Disputes often arise between contract parties where ambiguous language has been used. Unambiguous language can also be important where there is an imbalance of bargaining strength between the parties.
  arXiv  Detail & Related papers  (2024-10-26T16:35:45Z)
• Hybrid Deep Learning for Legal Text Analysis: Predicting Punishment Durations in Indonesian Court Rulings [0.0]
  This study develops a deep learning-based predictive system for court sentence lengths. Our model, combining CNN and BiLSTM with attention mechanism, achieved an R-squared score of 0.5893.
  arXiv  Detail & Related papers  (2024-10-26T07:07:48Z)
• DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment [55.91429725404988]
  We introduce DELTA, a discriminative model designed for legal case retrieval. We leverage shallow decoders to create information bottlenecks, aiming to enhance the representation ability. Our approach can outperform existing state-of-the-art methods in legal case retrieval.
  arXiv  Detail & Related papers  (2024-03-27T10:40:14Z)
• Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning [49.23103067844278]
  We propose the task of multi-defendant LJP, which aims to automatically predict the judgment results for each defendant of multi-defendant cases. Two challenges arise with the task of multi-defendant LJP: (1) indistinguishable judgment results among various defendants; and (2) the lack of a real-world dataset for training and evaluation.
  arXiv  Detail & Related papers  (2023-12-10T04:46:30Z)
• Towards Grammatical Tagging for the Legal Language of Cybersecurity [0.0]
  Legal language can be understood as the language typically used by those engaged in the legal profession. Recent legislation on cybersecurity obviously uses legal language in writing. This paper faces the challenge of the essential interpretation of the legal language of cybersecurity.
  arXiv  Detail & Related papers  (2023-06-29T15:39:20Z)
• Rebooting Internet Immunity [0.0]
  Article proposes refining online immunity by limiting it to claims that threaten to impose a content-moderation burden on internet defendants. Internet also operates as a platform for the delivery of real-world goods and services. This approach empowers courts to identify culpable actors in the virtual world and treat like conduct alike wherever it occurs.
  arXiv  Detail & Related papers  (2023-06-05T13:47:47Z)
• Toward Textual Internet Immunity [0.0]
  Under Section 230 of the Communications Decency Act of 1996, online entities are immune from lawsuits related to content authored by third parties. This Essay discusses how courts' zealous enforcement of the early internet's free-information ethos gave birth to an expansive immunity doctrine. It explores what a narrower, text-focused doctrine might mean for the tech industry.
  arXiv  Detail & Related papers  (2023-06-05T13:47:30Z)
• SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval [75.05173891207214]
  Legal case retrieval plays a core role in the intelligent legal system. Most existing language models have difficulty understanding the long-distance dependencies between different structures. We propose a new Structure-Aware pre-traIned language model for LEgal case Retrieval.
  arXiv  Detail & Related papers  (2023-04-22T10:47:01Z)
• Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction [46.71918729837462]
  Given the fact description text of a legal case, legal judgment prediction aims to predict the case's charge, law article and penalty term. Previous studies fail to distinguish different classification errors with a standard cross-entropy classification loss. We propose a moco-based supervised contrastive learning to learn distinguishable representations. We further enhance the representation of the fact description with extracted crime amounts which are encoded by a pre-trained numeracy model.
  arXiv  Detail & Related papers  (2022-11-15T15:53:56Z)
• Improving Self-Supervised Speech Representations by Disentangling Speakers [56.486084431528695]
  Self-supervised learning in speech involves training a speech representation network on a large-scale unannotated speech corpus. Disentangling speakers is very challenging, because removing the speaker information could easily result in a loss of content as well. We propose a new SSL method that can achieve speaker disentanglement without severe loss of content.
  arXiv  Detail & Related papers  (2022-04-20T04:56:14Z)
• Proper Network Interpretability Helps Adversarial Robustness in Classification [91.39031895064223]
  We show that with a proper measurement of interpretation, it is difficult to prevent prediction-evasion adversarial attacks from causing interpretation discrepancy. We develop an interpretability-aware defensive scheme built only on promoting robust interpretation. We show that our defense achieves both robust classification and robust interpretation, outperforming state-of-the-art adversarial training methods against attacks of large perturbation.
  arXiv  Detail & Related papers  (2020-06-26T01:31:31Z)

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.