In my opinion, explainability and transparency are the most critical features of responsible AI tooling because organizations need to understand how and why AI systems make decisions, especially in sensitive areas such as healthcare, finance, hiring, and cybersecurity. When AI models operate like “black boxes,” it becomes difficult to identify bias, errors, or unfair outcomes, which can reduce user trust and create legal or ethical risks. Explainable AI helps developers, businesses, and users gain confidence in automated decisions by providing clear insights into the reasoning behind predictions and recommendations. I also believe responsible AI practices should become mandatory for all AI-based applications because AI systems increasingly influence real-world decisions that affect people’s lives, privacy, and opportunities. Mandatory governance, fairness testing, monitoring, and compliance standards would encourage organizations to build safer and more ethical AI solutions while reducing discrimination, misinformation, and misuse of technology across industries.