Master Class - AIGP Exam Preparation

Artificial intelligence enables machines to think, learn, or act like humans without explicit programming, differentiating AI systems from normal systems through learning from data and human defined objectives.

Define machine learning and explain its relationship to artificial intelligence. Explore neural networks, deep neural networks, and deep learning, including how layered structures process data to make predictions.

Differentiate narrow ai from general ai and explain superintelligent ai, with examples like spam filtering and facial recognition, noting that narrow ai is actively used while general ai is theoretical.

Examine key AI risks—ethical bias, privacy, security, misuse, transparency, explainability, dependency, legal issues, job displacement, and brittleness—with practical examples and AP exam relevance.

Discover governance basics—rules, responsibilities, and processes guided by clear policies, roles, oversight, risk management, and audits—and see how AI governance applies these to ensure safety, ethics, fairness, and compliance.

Outline AI governance roles from the board and senior management to the AI governance lead, data scientists, legal, IT security, and internal audit, emphasizing cross-functional alignment and risk management.

Examine centralized, decentralized, and hybrid ai governance models, detailing benefits and challenges, including accountability, faster decision making, consistency, and the need for coordination across large organizations.

Explore what constitutes an AI policy, its governance roles, approval by senior management, and stakeholder consensus to ensure ethical, legal, and responsible AI use.

Identify the problem, determine how ai can help, specify data needs such as delivery times, driver routes, weather, and customer addresses, and address legal requirements, risks, and margins of error.

Explore the principles of responsible AI, including human centric approach, fairness, safety, privacy, transparency, explainability, accountability, and human oversight, as defined by OECD, NIST, and ISO.

Explore how privacy laws govern AI development and maintenance. Apply core principles like consent, purpose limitation, data minimization, storage limitation, integrity and confidentiality, accountability, and privacy by design.

Explore differential privacy and how adding random noise protects personal data while preserving overall trends, enabling organizations to use data without identifying individuals and supporting responsible AI development.

Explore how intellectual property laws shape ai system development, including safeguarding original works and training data, and navigate licenses, fair use, and open source conditions with legal teams.

Explore how nondiscrimination laws including the Civil Rights Act, GDPR, and the Equality Act shape AI development, bias review, fairness testing, and human oversight to prevent discrimination.

Explore how consumer protection laws shape AI system development, ensuring transparent use and no deception. Ensure fair treatment and a right to report issues and seek human assistance.

The EU AI Act establishes the objective of ensuring safe, ethical AI that respects fundamental rights, using a risk-based approach. It classifies AI into prohibited, high-risk, limited-risk, and minimal-risk categories.

Examine prohibited AI systems under the UAE act, including social scoring, behavior manipulation, real-time biometric identification, and emotion recognition; identify high-risk AI like recruitment, credit scoring, and healthcare.

Explore EU AI act high risk requirements, including risk management, data quality, transparency, human oversight, robustness, conformity assessment, and post-market monitoring, plus risk categories and a comparison with NIST.

Explore conformity assessment under the EU AI Act for high risk AI systems, including self-assessment when harmonized standards from ISO, IEC, CE apply, and third-party assessment by a notified body.

Outline the EU AI Act documentation requirements for high-risk AI, including ten-year retention and articles 11 and 17 documents, notified bodies decisions, EU declaration of conformity, and six-month log retention.

Explore EU AI act penalties: severe violations €35 million or 7% turnover, high-risk AI non-compliance €15 million or 3%, and general purpose AI rules start six months after activation.

Explore the OECD principles for trustworthy AI, including inclusive growth, human-centered values, transparency, robustness, and accountability, and learn the OECD policy framework with risk assessment, documentation, oversight, testing, and appeals.

Explore the NIST AI risk management framework, its voluntary and flexible core functions: governance, map, measure, and manage, focusing on risk identification, analysis, and mitigation.

Explore the NIST ARIA program for assessing risk and impact of AI, including safety, fairness, explainability, robustness, and human AI collaboration, and its role in the AI risk management framework.

Explore ISO IEC 42001, the AI management system standard, guiding risk, legal and ethical compliance, leadership, planning, operations, performance evaluation, certification, and continual improvement for responsible AI.

The ISO/IEC 22989 standard provides a common language for AI, defining key terms, concepts, and AI system types to ensure developers, regulators, and users understand each other and avoid miscommunication.

Learn how to perform AI impact assessments, covering fairness, privacy, safety, and social impacts, and apply EU AI act requirements for high-risk models, disclosures, and predeployment documentation.

Distinguish data lineage from data provenance by tracing data movement from upload to processing, and understanding provenance as the data’s origin and authenticity.

Explore AI model functions such as decision tree, recognition, personalization, and multimodal and robotic models. Use real-world examples like loan approvals, face recognition, chatbots, recommendations, warehouse robots, and payroll automation.

Explore supervised, unsupervised, semi-supervised, and reinforcement learning, with examples and contrasts between labeled and unlabeled data, and how a model learns from rewards and penalties.

Explore various machine learning algorithms, from linear and logistic regression to decision trees, random forests, k means clustering, Q-learning, and genetic algorithms, and understand their supervised, unsupervised, and reinforcement uses.

Investigate AI model drift and its impact on accuracy over time, then implement monitoring, automatic alerts, data audits, retraining, and feedback loops to sustain performance and governance.

Understand overfitting and underfitting in AI model training, use diverse, representative training, validation, and testing data, and apply synthetic data to balance datasets for fair, accurate predictions.

Master the objectives of AI model testing, including accuracy, performance, overfitting vs underfitting, fairness, and security, through unit, integration, performance, bias, stress, and security tests.

Discover primary AI model categories: classic AI with decision trees and linear regression, generative AI language models, multimodal AI, and retrieval augmented generation, with examples like ChatGPT and GPT-4.

Examine the differences between proprietary models and open source models, highlighting ownership, access, and transparency of architecture and training details.

Explore open source ai models, freely available yet sometimes restricted for commercial use, with code and data shared to learn, improve, or build upon. Identify licensing, security, and compliance risks.

Organizations own proprietary ai models, built in-house and not publicly shared to gain competitive edge, while facing legal, ethical, and maintenance challenges and accountability for accuracy, fairness, and compliance.

Compare large ai models by size, capability, and use cases like ChatGPT. Large models require powerful hardware and massive datasets; small models run on mobile devices with low latency.

Explore AI deployment options including cloud, on premise, edge, and hybrid models, highlighting scalability, data control, real-time processing, regulatory concerns, and practical use cases.

Understand the AI model card, including its overview, intended use, training data and limitations, performance metrics, fairness and accountability, and maintenance for regulators, developers, and users.