This paper provides a comprehensive overview of the state-of-the-art in fair-AI methods and resources, as well as the main policies on bias in AI. It also discusses the lessons learned from the NoBIAS project, which aimed to develop interdisciplinary methods for AI-based decision making without bias. The paper highlights the importance of addressing bias in AI, particularly in the context of the European Union, and emphasizes the need for a multidisciplinary approach that integrates legal and technical considerations. It discusses various fairness metrics, methods for detecting and mitigating bias, and the challenges of ensuring fairness in AI systems. The paper also addresses the legal challenges of bias in AI, including the intersection of data protection law and non-discrimination law in the EU. It emphasizes the need for integrated and interdisciplinary approaches to bias management, as well as the importance of transparency and accountability in AI systems. The paper concludes by advocating for a more integrated model that accounts for the deep intertwinement between data protection and non-discrimination legal regimes, and seeks to enhance privacy while engaging in debiasing.This paper provides a comprehensive overview of the state-of-the-art in fair-AI methods and resources, as well as the main policies on bias in AI. It also discusses the lessons learned from the NoBIAS project, which aimed to develop interdisciplinary methods for AI-based decision making without bias. The paper highlights the importance of addressing bias in AI, particularly in the context of the European Union, and emphasizes the need for a multidisciplinary approach that integrates legal and technical considerations. It discusses various fairness metrics, methods for detecting and mitigating bias, and the challenges of ensuring fairness in AI systems. The paper also addresses the legal challenges of bias in AI, including the intersection of data protection law and non-discrimination law in the EU. It emphasizes the need for integrated and interdisciplinary approaches to bias management, as well as the importance of transparency and accountability in AI systems. The paper concludes by advocating for a more integrated model that accounts for the deep intertwinement between data protection and non-discrimination legal regimes, and seeks to enhance privacy while engaging in debiasing.