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
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.
Module 1 - Supervised Machine Learning Algorithms
Topics Covered:
- Linear & Logistic Regression
- Decision Trees & Random Forests
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Naive Bayes
- Model evaluation metrics
- Hyperparameter tuning
- Building end-to-end ML pipelines
Module 2 - Unsupervised Learning & Pattern Recognition
Topics Covered:
- K-Means clustering
- Hierarchical clustering
- Dimensionality reduction (PCA)
- Anomaly detection
- Association rules
- Customer segmentation (use case)
- Evaluating unsupervised models
- Visualization for cluster insights
Module 3 - Deep Learning & Neural Networks (DL Basics)
Topics Covered:
- Understanding neural network architecture
- Forward & backward propagation
- Activation functions
- Loss functions & optimizers
- Creating neural networks with TensorFlow/Keras
- Underfitting vs overfitting
- Model regularization
- DL experimentation workflows
Module 4 - Computer Vision (CV) Fundamentals
Topics Covered:
- Image processing basics
- Convolutional Neural Networks (CNNs)
- Pooling, padding, filters
- Image classification
- Data augmentation
- Transfer learning (VGG, ResNet)
- Building CV projects using Keras
- Model deployment
Module 5 - Natural Language Processing (NLP)
Topics Covered:
- Text preprocessing: tokenization, stemming, lemmatization
- Bag of Words & TF-IDF
- Sentiment analysis
- Intro to word embeddings
- Text classification use cases
- Building NLP models with Scikit-learn
- Deploying NLP models
- Real-world NLP workflows
Module 1 - Product Thinking for AI & ML Solutions
Topics Covered:
- Problem identification using ML
- Understanding business metrics
- Mapping data to product features
- Building user-centric AI solutions
- Figma for AI product UI
- Model lifecycle in products
- Documentation & ML requirements
Module 2 - Startup Innovation Using AI & ML
Topics Covered:
- AI product idea generation
- Competitive analysis
- Building MVPs for ML products
- AI monetization strategies
- Creating pitch decks
- Using AI tools for product research
- Preparing go-to-market (GTM) plans
Module 3 - Professional Development for ML Careers
Topics Covered:
- Resume building for AI/ML profiles
- LinkedIn optimization
- Writing ML case studies
- GitHub portfolio for ML projects
- Communication skills for data storytelling
- Interview preparation (ML + coding)
Mini Projects
- Sales prediction using Regression
- Iris dataset classifier
- Customer segmentation using K-means
- Sentiment analysis on social media data
Concepts Covered:
- EDA
- ML basics
- Clustering
- Classification
Intermediate Projects
- Image classification with CNN
- Spam detection (NLP)
- Credit card fraud detection
- Housing price prediction
Concepts Covered:
- Deep learning
- NLP
- Evaluation metrics
- Optimization
Capstone Projects
- End-to-End ML Pipeline & Deployment
- AI Customer Insights Platform
- NLP-powered Recommendation System
- Deep Learning Visual Recognition System
Concepts Covered:
- Architecture design
- Deployment
- Experimentation
- Documentation
Tools & Softwares
Program Outcomes
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