Meta AI ethics encompasses bias, transparency, and responsible AI development, addressing whether systems perform unfairly across groups, languages, regions, or contexts, whether users can tell when interacting with AI, and whether safety, privacy, accountability, and human review are built into the AI lifecycle. Meta's work on Meta AI and Llama 3 provides a case study in balancing open models, which invite outside testing but also enable misuse, with user-facing assistants that may hallucinate, repeat social bias, or generate synthetic media. Meta's April 2024 post on its responsible approach states the company evaluates risk across model development and deployment, not only at launch. Users are told they are interacting with AI technology and receive information about possible inaccuracies. Meta released safety tools around Llama 3, including Llama Guard 2 for input and output safety classification, CyberSecEval 2 for cybersecurity risk evaluation, and Code Shield for safer code generation workflows. AI bias typically arises from training data, annotation choices, product design, and evaluation sets carrying uneven assumptions, with common causes including historical data bias reflecting past discrimination and representation gaps in languages, dialects, cultural references, or geographies.