Responsible & Ethical AI
To integrate ethical frameworks, governance principles, and responsible AI practices, enabling learners to design, develop, and deploy AI systems that are fair, transparent, accountable, and compliant with global standards.
- Academic partner with UGC, AICTE, and NCVET alignment
- 200+ programs across domains
- 1,000+ industry partners and global collaborations
Program Objective
To integrate ethical frameworks, governance principles, and responsible AI practices, enabling learners to design, develop, and deploy AI systems that are fair, transparent, accountable, and compliant with global standards.
Key Features
Program Outcomes
Program Outcomes
Module 1 – Foundations of Responsible & Ethical AI
Topics Covered:
Introduction to Artificial Intelligence & Societal Impact
What is Responsible AI? Why It Matters
History of AI Ethics & Major AI Failures (Case Studies)
Core Principles:
Fairness
Transparency
Accountability
Privacy
Safety
Human-Centered AI Design
Ethical Theories in Technology (Utilitarianism, Deontology, etc.)
AI Risk Landscape & Emerging Concerns
Module 2 – Bias, Fairness & Inclusive AI Systems
Topics Covered:
Types of Bias in AI:
Data Bias
Algorithmic Bias
Selection Bias
Historical Bias
Fairness Metrics & Evaluation Techniques
Bias Detection Tools & Techniques
Bias Mitigation Strategies:
Pre-processing methods
In-processing techniques
Post-processing adjustments
Inclusive AI Design Practices
Case Studies in Hiring, Lending & Healthcare AI
Module 3 – Transparency, Explainability & Trustworthy AI
Topics Covered:
Importance of Explainable AI (XAI)
Black Box vs White Box Models
Model Interpretability Concepts
Explainability Techniques:
LIME
SHAP
Feature Importance
Documentation & Model Cards
Communicating AI Decisions to Non-Technical Stakeholders
Building Trust in AI Systems
Module 4 – Data Privacy, Security & Regulatory Compliance
Topics Covered:
Data Privacy Principles
Personally Identifiable Information (PII)
Data Minimization & Consent
Privacy-Preserving Techniques:
Anonymization
Differential Privacy
Encryption
Global Regulations Overview:
GDPR
EU AI Act (Overview)
Data Protection Frameworks
AI Security Risks & Adversarial Attacks
Responsible Data Governance
Module 5 – AI Governance, Risk Management & Accountability
Topics Covered:
AI Governance Frameworks
Ethical AI Policies & Guidelines
AI Risk Management Frameworks
Model Risk Management (MRM)
Internal AI Audit Practices
Human-in-the-Loop Systems
Incident Response & Ethical Escalation
Responsible AI Lifecycle Management
Module 6 – Capstone Project: Designing a Responsible AI Framework
Select an AI use case (Healthcare / Finance / HR / Retail etc.)
Conduct ethical risk assessment
Perform bias analysis
Apply explainability techniques
Develop governance & compliance strategy
Submit a Responsible AI Implementation Report
Learning Outcome:
Demonstrate industry-ready expertise in implementing responsible AI systems.
Tools & Softwares










Salary Scale
Career Roles
- Responsible AI Specialist
- AI Ethics Consultant
- AI Governance Analyst
- Data Privacy Analyst
- Model Risk & Compliance Specialist
FAQ's
Yes. You'll receive VTU-compliant certificates and documentation.
No. The program starts from fundamentals and scales gradually.
Prediction models, CNN-based classifiers, NLP applications, and a full ML pipeline.
Yes — resume, LinkedIn, GitHub, mock interviews & job guidance.
Offered in both offline and hybrid formats.
Yes, you will receive a verified completion certificate from Rooman Technologies upon meeting all requirements.
CSE, ISE, AIML, ECE, EEE, Civil, Mechanical and all final-year VTU students.
Python, Scikit-learn, TensorFlow, Pandas, Kaggle workflows, cloud ML basics.
Contact Us
Have questions about our programs or need guidance? Reach out to us and we’ll be happy to help.
Email Us
online@rooman.net
Call Us
080 6945 1000
Send us a Message
Contact Us
Have questions about our programs or need guidance? Reach out to us and we’ll be happy to help.
Email Us
online@rooman.net
Call Us
080 6945 1000