Responsible AI refers to the broad set of practices, principles, and governance frameworks aimed at ensuring AI systems are developed and deployed in an ethical, transparent, and accountable manner​.It encompasses making sure that AI technologies are fair (avoiding unjust bias or discrimination against any group), inclusive (considering diverse users and impacts), and do not cause harm deliberately or inadvertently​.Key pillars of Responsible AI include: Accountability – organizations and developers should be accountable for the outcomes of their AI systems, implementing oversight and audit mechanisms; Transparency – providing explainability or at least insight into how AI decisions are made, and being open about when AI is in use; Fairness – taking steps to detect and mitigate bias in data or algorithms so that decisions do not systematically disadvantage certain groups; Privacy – ensuring AI systems comply with data privacy laws and respect individuals’ data rights (minimizing personal data usage, using techniques like anonymization or federated learning); and Safety/Security – designing AI to be robust against adversarial attacks and to fail safely. For example, a Responsible AI approach in a lending model would involve checking that the model’s approvals do not discriminate by race or gender (fairness), explaining to rejected applicants the main factors for denial (transparency), and having human review for edge cases (accountability). The term also extends to societal and legal dimensions, encouraging engagement with stakeholders and alignment with human values. Many companies and institutions have Responsible AI guidelines to guide their AI development. In essence, Responsible AI is about trustworthy AI: making sure AI systems are worthy of trust and benefit society, while minimizing risks and negative consequences​.