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Responsible AI: Bias, Fairness, and Explainability in ML

Build trustworthy AI systems with a focus on ethical design, algorithmic fairness, and model interpretability techniques.
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Course Duration: 10 Hours
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Responsible AI: Bias, Fairness, and Explainability in ML is a comprehensive and practice-driven course designed for AI/ML professionals, data scientists, policy advocates, and ethical technologists who seek to build accountable and transparent machine learning systems. In today’s AI-driven world, creating systems that are fair, explainable, and free from harmful biases is not just an ethical imperative but a professional responsibility.
 
This course starts by introducing foundational concepts behind Responsible AI (RAI), diving into the origins and impacts of bias in AI systems. You will explore various forms of bias—data bias, societal bias, algorithmic bias—and learn how to mitigate them during data preparation, model training, and deployment phases. You’ll develop a deep understanding of fairness metrics like equal opportunity, demographic parity, and disparate impact.
 
As the course progresses, you’ll tackle explainability—a cornerstone of responsible AI adoption. You will learn to interpret black-box models using cutting-edge tools such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and counterfactual explanations. Through hands-on projects, you will assess models for bias and fairness, make them interpretable, and build trust through transparency.
 
What is Responsible AI: Bias, Fairness, and Explainability in ML?
 
Responsible AI is a discipline within machine learning that ensures algorithms are developed and deployed in ways that are ethical, unbiased, fair, transparent, and explainable. It encompasses the principles and tools needed to build AI systems that are socially and legally aligned with diverse user needs. The Bias, Fairness, and Explainability components of RAI focus on identifying potential discrimination in algorithms, ensuring equitable outcomes for all user groups, and providing interpretable results to decision-makers and end users.
 
How to Use This Course
 
To make the most of this course:
  • Start with theoretical modules on ethics, governance, and AI bias before progressing to technical implementations.
  • Perform hands-on labs that use real-world datasets to measure fairness and apply bias mitigation techniques.
  • Use Jupyter notebooks to experiment with model interpretation libraries such as SHAP and LIME.
  • Reflect on ethical dilemmas through case studies from domains like hiring, lending, facial recognition, and criminal justice.
  • Utilize discussion forums to share insights and learn from others working in AI ethics and accountability.
Whether you're building ML models or auditing them, this course empowers you to become a responsible AI practitioner—balancing innovation with societal good.

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  • The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.

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Course Objectives Back to Top
By the end of this course, you will be able to:
 
  1. Understand core concepts of Responsible AI and its importance in modern ML systems.
  2. Identify different types of bias in datasets and algorithms.
  3. Apply fairness metrics to evaluate machine learning models.
  4. Use bias mitigation techniques such as reweighing, adversarial debiasing, and preprocessing methods.
  5. Explain the predictions of complex ML models using SHAP and LIME.
  6. Analyze fairness trade-offs and interpretability vs. performance trade-offs.
  7. Explore legal and ethical frameworks governing AI usage.
  8. Conduct Responsible AI audits on existing models.
  9. Build models that satisfy multiple fairness constraints.
  10. Integrate explainability tools into production ML pipelines.
Course Syllabus Back to Top
Course Syllabus
 
Module 1: Introduction to Responsible AI
  • What is Responsible AI?
  • Ethics in Machine Learning
  • Overview of Bias, Fairness, and Explainability
Module 2: Understanding Bias in ML
  • Types of Bias: Historical, Representation, Measurement
  • Case Studies: COMPAS, Amazon Hiring Model
  • Consequences of Biased AI
Module 3: Fairness in Machine Learning
  • Fairness Definitions (Demographic Parity, Equal Opportunity, etc.)
  • Trade-offs Between Fairness and Accuracy
  • Group vs. Individual Fairness
Module 4: Fairness Metrics & Evaluation
  • Statistical Fairness Metrics
  • Visualizing and Auditing Model Fairness
  • Practical Fairness Tools (AI Fairness 360, Fairlearn)
Module 5: Bias Mitigation Techniques
  • Preprocessing Techniques: Sampling, Reweighing
  • In-processing: Adversarial Debiasing
  • Post-processing: Equalized Odds, Reject Option Classification
Module 6: Model Explainability: Theory and Tools
  • Why Explainability Matters
  • Interpretable vs. Black-box Models
  • Overview of LIME, SHAP, and Counterfactuals
Module 7: SHAP and LIME in Practice
  • SHAP Values for Tree and Deep Models
  • Local and Global Interpretability
  • Explaining Individual Predictions
Module 8: Counterfactual and Causal Explanations
  • Introduction to Counterfactual Reasoning
  • Causal Inference and Causal ML
  • Application in Recourse Analysis
Module 9: Responsible AI Case Studies
  • AI in Healthcare, Finance, HR, and Law Enforcement
  • Fairness Failures and Regulatory Impacts
  • Designing Auditable AI Systems
Module 10: Legal, Social, and Regulatory Perspectives
  • GDPR, EEOC, and AI Regulations
  • AI Governance and Risk Management
  • AI Ethics Boards and Policies
Module 11: Building a Responsible ML Pipeline
  • Data Collection and Curation
  • Training with Fairness Constraints
  • Monitoring in Production
Module 12: Capstone Project: Responsible AI in Action
 
  • Select Domain (e.g., Loan Approval)
  • Audit Model Bias and Fairness
  • Explain Model Decisions Using SHAP/LIME
Certification Back to Top
Upon completing this course, learners will earn a Certificate of Completion in Responsible AI: Bias, Fairness, and Explainability in ML from Uplatz. This industry-aligned certificate affirms your ability to analyze, build, and audit machine learning models with fairness and interpretability at the forefront. It demonstrates to employers and stakeholders that you understand key ethical principles in AI, can evaluate model outputs for discriminatory patterns, and have hands-on skills in applying responsible AI toolkits like SHAP, LIME, AI Fairness 360, and Fairlearn.
 
This certification will enhance your professional credibility and support your transition into roles such as AI Auditor, Responsible AI Engineer, Machine Learning Ethicist, and Fairness Consultant. Whether you're entering AI from a data science, legal, or policy background, this credential confirms your ability to contribute to ethical AI deployment and compliance with global standards.
Career & Jobs Back to Top
As AI systems become more pervasive, there's a rapidly growing demand for professionals skilled in building transparent, unbiased, and accountable models. Completion of this course opens up multiple career pathways in the AI ethics and governance domain.
 
Graduates can pursue roles such as:
  • Responsible AI Engineer
  • Machine Learning Ethicist
  • AI Governance and Policy Specialist
  • Fairness and Explainability Consultant
  • AI Auditor or Compliance Analyst
  • AI Researcher (Ethics and Bias Focus)
Organizations across industries—including finance, healthcare, education, government, and HR tech—are hiring experts who can evaluate model outputs for fairness, conduct algorithm audits, and ensure regulatory compliance. Tech companies and startups are forming dedicated Responsible AI teams, while consulting firms are offering AI audit services to clients.
 
Moreover, regulatory bodies and research institutions are also employing specialists to contribute to ethical AI design, risk frameworks, and explainability standards. Whether you work in technical development or regulatory oversight, this course equips you with tools to bridge both domains.
 
Professionals who understand how to balance innovation with accountability will play a pivotal role in shaping the future of trustworthy AI. If you are passionate about AI and societal impact, this is a high-growth, high-impact field.
Interview Questions Back to Top
1. What is Responsible AI and why is it important?
Responsible AI ensures that AI systems are fair, ethical, transparent, and accountable. It minimizes harm and builds trust with stakeholders.
 
2. What are the major sources of bias in machine learning?
Bias can stem from training data (historical bias), model assumptions (algorithmic bias), or operational context (measurement bias).
 
3. Define fairness in AI.
Fairness refers to the equitable treatment of all individuals or groups by an AI system. This can be measured through statistical or individual fairness metrics.
 
4. What is demographic parity?
It is a fairness metric ensuring that different groups (e.g., by race or gender) have equal outcomes from a model.
 
5. How does SHAP explain model predictions?
SHAP assigns each feature a contribution value toward the prediction, based on cooperative game theory (Shapley values).
 
6. What is the difference between LIME and SHAP?
LIME approximates the local behavior of a model using interpretable models, while SHAP uses global feature attribution based on solid theoretical foundations.
 
7. What is adversarial debiasing?
It’s a bias mitigation technique where a model is trained to perform well on the main task while reducing its ability to predict sensitive attributes.
 
8. Why is explainability crucial in regulated industries?
Explainability helps organizations meet legal requirements (like GDPR) and allows stakeholders to understand and contest automated decisions.
 
9. How can you audit a model for fairness?
By calculating fairness metrics, visualizing group-wise performance, and testing with bias detection tools like Fairlearn or AI Fairness 360.
 
10. What are counterfactual explanations?
They describe how minimal changes to input features can alter a model's decision, helping users understand how to achieve a desired outcome.
Course Quiz Back to Top
Start Quiz
Q1. What are the payment options?
A1. We have multiple payment options: 1) Book your course on our webiste by clicking on Buy this course button on top right of this course page 2) Pay via Invoice using any credit or debit card 3) Pay to our UK or India bank account 4) If your HR or employer is making the payment, then we can send them an invoice to pay.

Q2. Will I get certificate?
A2. Yes, you will receive course completion certificate from Uplatz confirming that you have completed this course with Uplatz. Once you complete your learning please submit this for to request for your certificate https://training.uplatz.com/certificate-request.php

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A3. All our video courses comes with lifetime access. Once you purchase a video course with Uplatz you have lifetime access to the course i.e. forever. You can access your course any time via our website and/or mobile app and learn at your own convenience.

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A16. Software access can be purchased seperately at an additional cost. The cost varies from course to course but is generally in between GBP 20 to GBP 40 per month.

Q17. Does your course guarantee a job?
A17. Our course is designed to provide you with a solid foundation in the subject and equip you with valuable skills. While the course is a significant step toward your career goals, its important to note that the job market can vary, and some positions might require additional certifications or experience. Remember that the job landscape is constantly evolving. We encourage you to continue learning and stay updated on industry trends even after completing the course. Many successful professionals combine formal education with ongoing self-improvement to excel in their careers. We are here to support you in your journey!

Q18. Do you provide placement services?
A18. While our course is designed to provide you with a comprehensive understanding of the subject, we currently do not offer placement services as part of the course package. Our main focus is on delivering high-quality education and equipping you with essential skills in this field. However, we understand that finding job opportunities is a crucial aspect of your career journey. We recommend exploring various avenues to enhance your job search:
a) Career Counseling: Seek guidance from career counselors who can provide personalized advice and help you tailor your job search strategy.
b) Networking: Attend industry events, workshops, and conferences to build connections with professionals in your field. Networking can often lead to job referrals and valuable insights.
c) Online Professional Network: Leverage platforms like LinkedIn, a reputable online professional network, to explore job opportunities that resonate with your skills and interests.
d) Online Job Platforms: Investigate prominent online job platforms in your region and submit applications for suitable positions considering both your prior experience and the newly acquired knowledge. e.g in UK the major job platforms are Reed, Indeed, CV library, Total Jobs, Linkedin.
While we may not offer placement services, we are here to support you in other ways. If you have any questions about the industry, job search strategies, or interview preparation, please dont hesitate to reach out. Remember that taking an active role in your job search process can lead to valuable experiences and opportunities.

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Q21. Can I get help from a tutor if I have doubts while learning from a video course?
A21. Tutor support is not available for our video course. If you believe you require assistance from a tutor, we recommend considering our live class option. Please contact our team for the most up-to-date availability. The pricing for live classes typically begins at USD 999 and may vary.



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