AI Transparency
AI Transparency refers to the principle and practice of making artificial intelligence systems understandable, explainable, and accountable to stakeholders, including developers, users, and regulators. It involves providing clear insights into how AI models make decisions, what data they use, and their potential biases or limitations. This concept is crucial for building trust, ensuring ethical deployment, and enabling effective oversight of AI technologies.
Developers should learn and apply AI Transparency when building or deploying AI systems in high-stakes domains like healthcare, finance, or autonomous vehicles, where decisions impact human lives or rights. It helps mitigate risks such as algorithmic bias, enhances regulatory compliance (e.g., with GDPR or AI Act requirements), and improves user trust by allowing stakeholders to understand and challenge AI outcomes. In practice, this involves techniques like model interpretability, documentation of data sources, and clear communication of system capabilities.