The article highlights ten critical ethical risks of AI in testing, including algorithmic bias and fairness, the "black box" issues with AI decision-making, privacy and data security vulnerabilities, accountability and liability diffusion, job displacement and workforce disruption, over-reliance on automation, ethical oversight in AI-driven defect resolution, AI performance drift, intellectual property infringement, and environmental impact and sustainability. To address these risks, it is recommended to implement bias audits, explainable AI tools, anonymization of test data, accountability mechanisms, upskilling existing testers, balanced approaches combining AI automation with manual testing, human-in-the-loop review mechanisms, continuous monitoring systems, IP audit policies, and choosing cloud providers with renewable energy commitments. The article emphasizes the importance of transparency, accountability, and cross-functional teams in implementing effective AI testing solutions.