The paper provides a comprehensive overview of the state-of-the-art in fair-AI methods and resources, along with key policies on bias in AI. It aims to guide both researchers and practitioners by offering a bird's-eye view of the field. The authors also contribute to policy advice and best practices by leveraging the results from the NoBIAS project, which focuses on legal challenges in the EU context and understanding, mitigating, and accounting for bias from a multidisciplinary perspective. The paper discusses the multifaceted nature of bias, the importance of transparency and accountability in AI systems, and the tensions between data protection and non-discrimination laws. It highlights the need for interdisciplinary research and good practices to address the complex issues of bias and fairness in AI.The paper provides a comprehensive overview of the state-of-the-art in fair-AI methods and resources, along with key policies on bias in AI. It aims to guide both researchers and practitioners by offering a bird's-eye view of the field. The authors also contribute to policy advice and best practices by leveraging the results from the NoBIAS project, which focuses on legal challenges in the EU context and understanding, mitigating, and accounting for bias from a multidisciplinary perspective. The paper discusses the multifaceted nature of bias, the importance of transparency and accountability in AI systems, and the tensions between data protection and non-discrimination laws. It highlights the need for interdisciplinary research and good practices to address the complex issues of bias and fairness in AI.