Artificial intelligence accountability by dr.pavan duggal

AI Accountability: Explainability, Risk Management, Liability & Ethical AI Governance

What you’ll learn
learners will be able to define key terms such as “AI accountability,” “transparency,” “responsibility,” and “governance,” and apply apply these concepts
Learners will be able to map key stakeholders (developers, data teams, product managers, regulators, and end‑users) and explain who is accountable at each stage
Learners will be able to identify typical risks (e.g., bias, discrimination, lack of explainability and safety failures
Learners will be able to outline simple accountability frameworks such as audit trails, model documentation and oversight procedures

Requirements
No programming experience required. You can learn everything that you need to learn about AI Accountability

Description
As Artificial Intelligence systems increasingly influence decisions across governance, business, and society, the need for accountability has never been more critical. This course Artificial Intelligence Accountability by Dr. Pavan Duggal provides a structured understanding of how responsibility, transparency, and oversight can be established in AI-driven environments.

The course explores the key dimensions of AI accountability, including liability, algorithmic transparency, explainability, auditability, and traceability. Learners will gain insight into how accountability is embedded across the entire AI lifecycle from design and development through to deployment and real-world application. The course further examines the legal and regulatory landscape surrounding responsible AI, covering risk assessment, compliance obligations, and governance frameworks that are becoming central to AI regulation worldwide.

Special focus is given to practical challenges: identifying liability when AI systems cause harm, ensuring fairness and non-discrimination, and building effective mechanisms for redress and oversight. Emerging global approaches to AI accountability and the need for robust institutional frameworks are also examined in depth.

Designed for legal professionals, policymakers, regulators, technology stakeholders, and general users alike, this course bridges the gap between theory and practice. By the end, learners will be equipped to critically assess AI systems, implement meaningful accountability measures, and contribute to building trustworthy, responsible AI ecosystems.

Who this course is for
This course is for anyone who wants to understand how to make artificial intelligence systems transparent, responsible, and answerable when things go wrong. Who this course is for: AI practitioners and developers who build or deploy machine‑learning models and want to integrate accountability into their design, testing, and deployment workflows. Product managers, data scientists, and tech leads who need to manage AI projects with ethical and governance considerations, including risk assessment and documentation. Policy makers, compliance officers, and regulators interested in how to oversee AI systems, set guardrails, and ensure organizations fulfill their accountability obligations. Business leaders and executives who use AI tools in operations, marketing, HR, or customer service and need to understand their responsibilities and potential liabilities. Students and professionals in law, ethics, public policy, or social sciences who want to grasp the practical side of AI accountability and how it connects to real‑world governance and regulation. In short, this course is ideal for learners who care about “who is responsible when AI causes harm” and want concrete principles and tools to foster accountable AI in their work