The rapid expansion of artificial intelligence depends on an often-invisible workforce of data annotators, content moderators, and data labellers primarily based in the Global South, who work under harsh and murky conditions set by the platforms. One most essential aspects of this work, but often not mentioned, is the systematic violation of data privacy of those workers. They are often requested to submit personal data such as images, voice recordings, and biometrics to train the AI models. Such breaches are against existing data protection laws. Article 5(1)(b) of the General Data Protection Regulation (GDPR) mandates purpose limitation, while Section 25 of Kenya's Data Protection Act upholds principles of lawfulness, fairness, and transparency in data processing. Even then, AI companies and outsourcing firms are in constant disregard of these laws and use loopholes in the various country laws to coerce workers into collecting their data under coercive employment terms, with little to no oversight.

Therefore, this research seeks to answer two questions: (1) What are the legal and ethical implications of requiring platform workers to submit personal data (e.g., biometrics) for AI training, and how do these practices intersect with existing labour and data protection laws? (2) How are these data workers organizing to demand stronger privacy protections and ethical AI labour conditions? Using a mixed-methods approach like qualitative interviews with affected workers, legal analysis of data protection regimes, and case studies of worker-led movements, the study seeks to show how algorithmic management enforces labour exploitation and data privacy infringements while leaving workers without control over their personal information.

Grassroots and transnational advocacy continue to work against such organizing challenges. The unions and worker-led campaigns demand fair wages and transparency in algorithmic management and the right to data protection, consent, and control over their personal information. Thus, this paper argues that a dual approach, strengthening data protection laws to protect workers' privacy and supporting collective organizing efforts to resist exploitative AI labour practices, must address AI labour precarity. The research will form a more extensive discourse on countering the powers of platforms and promoting the ethical governance of AI by linking data protection/privacy with labour rights.