Deepfakes Pose Ethical and Legal Challenges, Harvard Chan School Reports
A deepfake is defined as realistically generated or altered media, including audio, images, and video, that can make an individual appear to say or do things they did not. While synthetic media can serve beneficial purposes such as accessibility, translation, education, or simulation when its origin and purpose are transparent and permissions are secured, its non-consensual use, deception, and impersonation create significant ethical and legal problems. According to Trishan Panch of the Harvard Chan School, detecting deepfakes is challenging, as visual glitches or unusual audio may sometimes reveal fabrications, but their absence does not guarantee authenticity. Automated detectors can also be unreliable, making errors or performing poorly with unfamiliar generation methods. The European Union's AI Act includes specific transparency rules for synthetic media and deepfakes, though these are not universally applicable as a single labeling requirement for all AI-edited files. Other laws may address issues lik...