Deepfake-induced harm and AI accountability: a layered civil-liability framework for generative models, platforms, and digital identity

Deepfake and other synthetic-media harms create a civil-liability problem that ordinary tort doctrine does not easily resolve: harmful content may be generated, amplified, monetised, and redistributed through a chain of actors in which no single participant controls the whole causal process. The objective of this article is to develop a layered civil-liability framework for that problem. It examines the Saudi and Jordanian civil-liability regimes as the principal doctrinal focus, while using selected EU, US, UAE, and Chinese materials as comparative reference points. The article remains private-law centred. It asks how civil-liability doctrine can respond when reputational, identity-based, corporate, and non-material harm is produced through the combined conduct of generative-model developers, prompting users, online platforms, and secondary distributors. The analysis identifies three pressure points: the natural-person wording of Article 138(2) of the Saudi Civil Transactions Law, which narrows moral-harm protection in relation to corporate and institutional injury; the persistence of a single-wrongdoer model in Saudi and Jordanian doctrine, despite the layered actor structure of deepfake production; and the evidential asymmetry created by opaque algorithmic causation. The article's main contribution is a calibrated model of layered civil liability: clearer protection for juridical-person reputation, a custody-based rule for algorithmic systems, cautious burden shifting where relevant information is controlled by platforms or developers, stronger private-law links to data-protection regimes, and targeted transparency duties for synthetic-media systems. The contribution is not to replace civil law with AI regulation, but to show how both fields must work together if deepfake-induced harm is to be remedied in a legally coherent way.