Navigating AI Accountability & Algorithmic Bias

• Understand the foundations of AI accountability and why it matters in real-world AI deployments.
  
• Learn how algorithmic bias enters systems through data, labels, sampling, and design decisions.
  
• Gain practical skills to evaluate fairness using bias and performance metrics across groups.
  
• Master bias mitigation approaches including pre-processing, in-processing, and post-processing methods.
  
• Explore explainability methods to improve model transparency and stakeholder trust.

What Will You Learn (Modules)

Module 1:Foundations of AI Accountability

• Learn what AI accountability means in practice—responsibility, traceability, oversight, and measurable controls.

• Understand where accountability fails and how to build it into the AI lifecycle.

Module 2:Where Algorithmic Bias Comes From

• Explore the root causes of algorithmic bias including historical bias, sampling errors, proxy variables, and label noise.
• Identify bias risks across datasets, features, and model objectives.

Module 3: Fairness Metrics & Bias Measurement

• Learn how to quantify unfairness using key fairness in machine learning metrics and group-based evaluation.
• Practice comparing model outcomes across populations and interpreting results responsibly.

Module 4: Bias Mitigation Techniques That Work

• Apply bias mitigation strategies: data balancing, reweighting, fairness constraints, and calibration methods.

Who Should Take This Course?

This course is ideal for:

• AI/ML Professionals working on model development, deployment, evaluation, or AI product decisions
• Data Scientists & Analysts who want to reduce algorithmic bias and improve model reliability
• Students pursuing AI, data science, computer science, law-tech, or policy-related careers

Job Opportunities

After completing this course, learners can pursue roles such as:

• Responsible AI Specialist
• AI Governance Analyst
• AI Risk & Compliance Associate

Why Learn With Nanoschool?

At NanoSchool, we focus on career-relevant learning that builds real capability—not just theory.

• Training by experts: Training by experts who have experience in applying skills to industry and research problems.
• Practical and hands-on training: Skills development through activities, templates, and task-based learning that can be applied immediately.
• Industry-aligned curriculum: Curriculum aligned with industry tools, workflows, and expectations from employers.
• Portfolio-ready outputs: Outputs that can be used in interviews, academic profiles, proposals, or actual work.
• Learner support: Learners are provided with structured support, learning paths, and assistance to remain consistent and finish strong.

Key outcomes of the course

Upon completion, learners will be able to:

• Develop skills to detect and mitigate bias in algorithms through systematic assessment and mitigation processes
• Implement AI accountability techniques that facilitate auditing, interpretability, and responsible deployment of AI
• Develop real-world experience and confidence in fairness evaluation, interpretability, and governance reporting
• Enhance your personal brand for responsible AI careers in industry and academia
• Improve your AI-related decisions, both technically and ethically