Responsible AI: transparency, fairness and accountability in automated decisions

1Introduction

AI now shapes lending, hiring and pricing; errors or bias can harm groups at scale.

2Discussion

Principles: fairness, transparency and interpretability, privacy, security and accountability.

Practice: bias audits of training data, per-group testing, model documentation and human review for high-stakes decisions.

3Conclusion

Frameworks such as the EU AI Act impose strict duties on high-risk systems like credit scoring; early adopters win trust.