Ethical AI refers to the practice of creating and using artificial intelligence (AI) in a way that aligns with moral principles and societal values. It encompasses concepts like fairness, accountability, transparency, explainability, and privacy.

In the context of AI development, ethical considerations might include:

1. Fairness: Ensuring that AI models do not discriminate against certain groups based on factors like race, gender, age, or socioeconomic status.
2. Accountability: Implementing processes to hold developers, users, and organizations accountable for the impacts of AI, including negative outcomes or unintended consequences.
3. Transparency: Making the inner workings of AI systems clear and understandable to users, developers, and regulators, so that people can see how decisions are being made.
4. Explainability: Creating AI systems that can articulate how and why a particular decision was made, which can be crucial for trust and understanding.
5. Privacy: Ensuring that personal and sensitive data are handled securely and that individuals have control over how their data are used.
6. Safety: Developing systems that function reliably and do not pose risks to human health or well-being.
7. Environmental Sustainability: Considering the environmental impact of training and deploying AI models, particularly in terms of energy consumption.

Ethical AI is an evolving field, and organizations around the world are working to establish guidelines, standards, and best practices to ensure that AI technologies are developed and used responsibly. It involves a multi-disciplinary approach that includes not only technologists but also ethicists, legal experts, social scientists, and other stakeholders to create a comprehensive framework for responsible AI development and deployment.