Beijing Internet Court Rules Platform's Mistaken Flagging of AI-Generated Content as Unlabeled Constitutes Breach of Contract
2 day ago / Read about 0 minute
Author:小编   

The Beijing Internet Court recently disclosed a related lawsuit: User Xiao Tang published text content that he claimed to be original, but the platform determined that it contained unlabeled AI-generated content and handled the matter by hiding the content and imposing a one-day speech ban. After Xiao Tang's appeal was unsuccessful, he filed a lawsuit with the court. The court found that the platform had committed a breach of contract and ordered it to lift the hiding of the content in question and delete the corresponding violation handling records from its backend system. The court noted that, for the creative text at issue, it is difficult to require users to provide materials documenting their creation process, while the algorithm tool's computation process and review results are controlled by the platform. The platform refused to explain its determination mechanism to the user on the grounds that it involved technical secrets and algorithm logic, and failed to reasonably present evidence or provide an explanation regarding the basis for its determination; it should therefore bear liability for breach of contract. This case clarifies the platform's obligation to explain its algorithmic identification of AI-generated content and the requirements for relief in cases of erroneous determination. It points out that a platform cannot refuse—merely on the grounds of involving trade secrets—to explain the algorithm's operating mechanism and erroneous-determination relief measures in a way that users can understand, and suggests that platforms establish a tiered handling mechanism, adhere to the principle that punishment should fit the offense, and improve content review mechanisms. This case provides a judicial reference for balancing algorithmic determination and user rights, and reminds platforms that while fulfilling their responsibility for managing AI content labeling, they must also improve the explanation of the basis for determinations and channels for correcting erroneous determinations.