AI Ethics and Responsibility

About Course

This comprehensive course rigorously explores the ethical dimensions of artificial intelligence, providing students with
advanced frameworks and practical tools to identify, analyze, and proactively address complex ethical challenges
across diverse AI applications. Covering everything from predictive policing to automated hiring and healthcare
diagnostics, participants will learn to design, develop, and deploy AI systems that uphold principles of fairness,
transparency, accountability, and human-centric values, fostering responsible innovation in the AI era

Course Content

Foundations of AI Ethics and Philosophy
Delve into the philosophical underpinnings of ethics in technology, examining historical precedents and modern ethical theories pertinent to AI. This module explores core ethical principles such as beneficence, non- maleficence, autonomy, justice, and explicability as they specifically apply to complex AI systems like autonomous vehicles and large language models.

Algorithmic Bias and Fairness Metrics
Examine the diverse sources of bias within AI systems, spanning from data collection to model deployment. Students will differentiate between statistical fairness metrics (e.g., demographic parity, equalized odds) and qualitative definitions of fairness. The module explores advanced techniques for detecting and mitigating bias in data and models, alongside analyzing real-world case studies of algorithmic bias in criminal justice, hiring, and loan applications.

Transparency, Explainability, and Interpretability (XAI)
Discover leading methodologies for developing interpretable and explainable AI systems. Learn to effectively balance model performance with the critical need for transparency, and master techniques such as LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlanations), and attention mechanisms for post-hoc explanation of complex machine learning models.

Data Privacy, Security, and AI
Address critical privacy challenges associated with large- scale data collection, AI model training, and inferential capabilities. This module investigates privacy-preserving machine learning techniques including differential privacy, federated learning, and homomorphic encryption. It also analyzes key regulatory frameworks such as GDPR, CCPA, HIPAA, and emerging global privacy standards specific to AI.

AI Governance, Auditing, and Policy
Explore best practices for organizational approaches to responsible AI development and deployment. This module covers industry standards like the NIST AI Risk Management Framework, ethical auditing processes for AI systems, and compliance frameworks. Additionally, it analyzes the current and evolving global policy and regulatory landscapes for AI, including international collaborations and national AI strategies.

The Future of Responsible AI and Societal Impact
Consider the long-term ethical implications of advanced AI systems, encompassing the "AI alignment problem" in artificial general intelligence (AGI), the societal impact on employment, and the future of human autonomy. This module facilitates discussions on participatory approaches to AI development, ethical design principles, and strategies for fostering public trust and engagement in AI governance.

Capstone Project
Students will undertake a comprehensive ethical audit and impact assessment of an existing AI system or application, such as an AI-driven lending platform, a facial recognition system, or an automated content moderation tool. This project involves rigorously identifying potential ethical concerns, applying appropriate ethical frameworks, analyzing data for inherent biases, and developing a detailed action plan for addressing these issues through both technical and governance solutions. Findings and recommendations will be presented to a panel of key stakeholders for critical feedback and practical implementation considerations.

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Access a meticulously curated collection of comprehensive ethics frameworks and guidelines from prominent AI organizations (e.g., OECD, IEEE, Partnership on AI),
In-depth case studies illustrating ethical successes and failures
from diverse industries,
Practical open-source tools and libraries for bias detection and mitigation (e.g., AIF360, Fairlearn),
Specialized benchmark datasets for fairness testing, and up-to-date policy and regulatory documents from leading governments and international bodies.

AI Ethics and Responsibility

About Course

What Will You Learn?

Course Content

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AI Ethics and Responsibility

AI Ethics and Responsibility

About Course

What Will You Learn?

Course Content

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