45 Fall 2026 disinformation and bias. AI is used in many sectors, and fragmented regulation across existing bodies can lead to inconsistencies, allowing harm to persist in the gaps. A central authority would streamline accountability, enforce reporting on high-risk models, and could coordinate responses to emerging threats without creating blanket bans. This would enhance its effectiveness in working against AI-generated antisemitism whilst supporting agile innovation through expert-led evaluations. Traceability mechanisms, through which AI outputs are linked back to their creating service via embedded identifiers, represent another vital change. This is required to hold developers accountable for illegal content, such as incitement to hatred, without relying solely on end-user platforms. By enabling swift identification and removal, traceability would combat AI-generated antisemitism by disrupting the anonymity that allows bad actors to exploit tools, thereby reducing real-world harms like incident surges tied to online disinformation. This measure supports free speech by targeting only unlawful uses, not legitimate expression, and fosters innovation through clear guidelines that encourage ethical design without broad prohibitions. To improve content moderation, regulation should require hybrid systems that integrate AI with human oversight, particularly for nuanced harms. This is necessary as standalone AI classifiers often fail to detect implicit patterns, allowing adaptive content to evade filters. Mandating specialized training for moderation tools would make a difference by increasing accuracy in identifying and removing AI-generated hate, thus curbing its spread and protecting vulnerable communities. Regulations should also enforce public education programs on media literacy, including how to recognize AIgenerated content, understanding how and where AI tools generate content, the information AI tools rely upon, and developing skills to evaluate the reliability and validity of AI-generated content. These types of programs build resilience against disinformation, as informed users are less likely to propagate harmful material if they understand the technicality of its flaws. By promoting critical assessment without mandating content removal, this approach combats antisemitism through empowerment rather than restriction, upholding free speech whilst indirectly reducing online amplification. Any regulatory framework should ideally be applied in concert with international partners that include governments, technology companies, civil society organizations, and academia. Establishing national or regional ethical standards for the use of AI and its development can support regulation and aid in preventing the misuse of AI technologies. Given the rapidly evolving nature of the subject, some aspects of this analysis and the proposed approach may become dated over time, a limitation inherent to research in this area. Integration into Existing or New Legislation In the UK, these regulatory changes could be incorporated into the OSA, which already imposes duties on platforms to mitigate illegal and harmful content, including hate speech. Amending the OSA would streamline enforcement by extending its risk assessment requirements to AI developers interacting with regulated services, ensuring cohesive oversight under Ofcom. However, this might limit the scope to online harm and potentially overlook AI’s broader applications in fields like healthcare or finance where biases could indirectly exacerbate societal divisions. It would also lead to a fragmented regulation of AI, as discussed earlier. A separate AI Bill offers a more comprehensive solution. Modeled on the government’s 2025 AI blueprint with its emphasis on agile sandboxes and safeguards, such legislation could regulate AI across sectors. This would address generative risks holistically whilst incorporating the proposed measures for combatting antisemitism within a hate speech and disinformation framework. This would make a greater difference by enabling cross-domain standards, such as universal bias audits, preventing fragmented regulations that allow different types of harm to migrate. Critically, a standalone Bill can balance innovation through voluntary testing environments and tiered obligations, avoiding overreach that stifles free speech by focusing on high-risk uses. This approach would position the UK as a leader in ethical AI, adapting to technological evolution without compromising economic growth. In summary, these targeted changes: transparency, risk assessments, traceability, hybrid moderation, and education, are indispensable for combating AI-generated antisemitism. They address root causes at creation and dissemination stages, reducing harm through proactive prevention rather than reactive measures. By being principles-based and threshold-driven, they safeguard innovation and free speech, ensuring the UK harnesses AI’s benefits whilst mitigating its dangers.
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