Presumed Innocent En Ligne _hot_ Review

This paper investigates the following question: To what extent does the principle of presumed innocent apply in online environments, and what normative framework should govern its application? The analysis proceeds in three parts. First, a conceptual overview of the presumption in traditional jurisprudence. Second, a diagnosis of three zones of inversion: platform moderation, digital evidence, and networked vigilantism. Third, a proposal for procedural reforms grounded in "digital due process."

A coherent response requires three levels of intervention. presumed innocent en ligne

In a physical courtroom, the presumption of innocence operates as a procedural shield: the state bears the burden of proof, and doubt benefits the accused. In online spaces (en ligne), this shield is frequently absent, perforated, or reversed. When a social media algorithm suspends an account for "potential hate speech," when law enforcement accesses a encrypted chat log before trial, or when a viral tweet labels an individual a "scammer" based on unverified screenshots—each event enacts a digital verdict without a digital trial. This paper investigates the following question: To what

Outside formal legal systems, online communities conduct their own rapid adjudications. A single accusatory post—screenshots of a text exchange, a video clip—can trigger a "digital pile-on." Within hours, the accused is named, shamed, and subjected to reputational and economic sanctions (job loss, doxing, harassment). Second, a diagnosis of three zones of inversion:

This is a shift from adjudication to pre-crime analytics . As Crawford and Schultz (2019) argue, algorithmic systems "produce suspicion rather than respond to it." The user has no right to confront the algorithm, no discovery of the training data, and often no meaningful appeal. In Jasper v. Meta (N.D. Cal. 2024), the court held that Section 230 shielded Meta from liability, but noted that "the plaintiff was effectively tried and convicted by a statistical model."

Private online platforms (X, Meta, TikTok) moderate billions of content items daily. Their terms of service often include clauses allowing suspension or removal "at our sole discretion." In practice, automated systems flag content based on statistical risk scores. A user is not presumed innocent; rather, a post is presumed violative if it matches a pattern (e.g., certain keywords, account age, report frequency).