Responsible Tech and AI Ethics
Course provided by Model Institute of Engineering & Technology
5 modules
Explore the fundamentals of Frontier Technologies
4 Level NCrF
National Credit Framework
25 Hours
Flexible Learning
Beginner Level
No prior experience required
Nano Credit Course
01 Credit
Course Overview
This course equips learners with a comprehensive understanding of responsible AI, emphasizing ethical, transparent, and accountable practices in AI development and deployment. Students will explore sources of bias in AI systems, learn strategies to mitigate them, and examine privacy, explainability, and inclusivity concerns. Through real-world case studies, learners will analyze ethical dilemmas and governance challenges in AI across industries. By the end of the course, participants will be able to design and implement AI frameworks that uphold ethical standards and foster trust in AI technologies.
Key Learning Highlights
Understanding core principles of responsible AI, including fairness, transparency, and accountability.
Identifying and mitigating bias in AI systems and ensuring inclusive AI practices.
Evaluating ethical concerns such as privacy, explainability, and societal impact.
Analyzing real-world case studies of AI ethics, dilemmas, and governance.
Developing frameworks and strategies for deploying AI responsibly across industries.
Tools & Platform Used
Learning Outcome
After completing this course, learners will be able to:
Demonstrate a clear understanding of responsible AI principles, including fairness, transparency, and accountability.
Identify potential sources of bias in AI systems and implement strategies to mitigate them.
Evaluate ethical issues related to privacy, explainability, and inclusivity in AI applications.
Analyze real-world AI case studies to understand ethical dilemmas and governance challenges.
Develop practical frameworks for designing, deploying, and managing AI solutions responsibly across industries.
Master the course with just 5 Modules
This course provides a comprehensive understanding of responsible AI, covering foundational ethical principles, bias detection and mitigation, transparency, explainability, and accountability in AI systems. Learners explore real-world governance frameworks and policies while gaining practical experience designing AI solutions that are fair, transparent, and ethically sound through a capstone mini project.
Foundations of Responsible AI
Understand the definition and importance of responsible AI in modern technology.
Learn key ethical principles: fairness, transparency, privacy, and accountability.
Explore real-world examples highlighting the need for ethical AI practices.
Bias and Fairness in AI
Identify types of algorithmic bias including data, systemic, and labeling biases.
Learn strategies to detect, evaluate, and mitigate bias in AI systems.
Design inclusive AI systems that promote fairness across diverse user groups.
Transparency and Explainability
Understand Explainable AI (XAI) and its significance in high-stakes decisions.
Learn methods to balance model performance with interpretability.
Explore tools and techniques for visually explaining AI decision-making processes.
Accountability and Ethical Governance
- Study ownership, liability, and privacy considerations in AI systems.
- Understand regulatory frameworks and ethical guidelines (e.g., EU AI Act, NITI Aayog).
- Learn governance models to ensure responsible AI deployment within organizations
Capstone Mini Project – Responsible AI Blueprint
Design a responsible AI framework for a real-world or hypothetical application.
Define ethical principles, risk mitigation strategies, and stakeholder considerations.
Apply course concepts to develop practical, accountable, and transparent AI solutions.
Roles
- AI Ethics Officer
- Responsible AI Consultant
- AI Governance Specialist
- Data Ethics Analyst
- AI Policy Advisor
