Understanding AI

All the analytics integrations you will ever need.

3 min

AI Literacy : Policy and Practice

This lesson consolidates and summarizes the core technical, conceptual, and ethical understanding of AI necessary for anyone involved in its governance. It clarifies that AI literacy extends beyond basic definitions, encompassing an appreciation for AI's diverse capabilities, inherent limitations, underlying mechanisms (e.g., data's role, model lifecycle), and the broader ethical implications, all crucial for informed decision-making in governance roles.

EU AI Act, OECD AI Principles, NIST AI Ris

10 min

Governing AI: From Frameworks to Function

Outlines the phases of AI governance from design to post-deployment monitoring. Offers examples of effective ongoing governance.

NIST AI Risk Management Framework, ISO/IEC 27001

10 min

Principles for Responsible AI

Delves into core ethical principles guiding responsible AI, such as fairness, accountability, transparency, privacy, safety, and human oversight. It frames societal impact within these principles, discussing how adherence minimizes harm and maximizes benefit to individuals and communities.

OECD AI Principles, UNESCO Recommendation on the Ethics of AI, IEEE 7000 Series, AI Bill of Rights.

7 min

Applied AI: From Lab to Industry

Applies AI concepts to healthcare, finance, and marketing. Discusses risk tradeoffs and governance concerns across sectors.

OECD AI Principles, EU AI Act

7 min

Explainable AI: Unpacking Transparency

Introduces the concept of Explainable AI (XAI), why it's crucial for understanding complex AI models, and various techniques to provide transparent and interpretable insights into their decision-making processes.

EU AI Act (Transparency), NIST AI Risk Management Framework (Explainability), ISO/IEC 23894.

7 min

Hallucination and Bias

Demystifies hallucinations in AI systems and discusses risks like misinformation, bias, and loss of trust.

ISO/IEC 27001, GDPR

5 min

How Models Learn (and Fail)

Explains common issues where models either perform too well on training data but poorly on new data, or are too simplistic to capture underlying patterns, affecting real-world accuracy.

NIST AI Risk Management Framework

5 min

Synthetic Data: Purpose and Applications

Defines synthetic data and how it helps train AI models ethically and efficiently, especially where real data is sensitive.

GDPR, EU AI Act

7 min

AI Bias: Data to Model

Details the various ways bias can enter AI systems through data, from initial collection to its use in training models, and outlines methods for detection and reduction.

OECD AI Principles, ISO 42001

7 min

Types of AI

Covers narrow, general, generative, and superintelligent AI types. Highlights use cases across industry.

OECD AI Principles, NIST AI Risk Management Framework

10 min

LLMs: Limits and Governance

Explains the architecture behind LLMs and their policy implications. Highlights governance risks in content generation and automation.

OECD AI Principles, EU AI Act

7 min

Narrow vs. General AI

Explores the fundamental differences between AI designed for specific tasks and AI aiming for human-like intelligence across domains.

OECD AI Principles, IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems

10 min

AGI: Hype vs. Reality

Explores the elusive nature of Artificial General Intelligence (AGI), critically examining its definitions, the current state of research, and the inherent weaknesses in establishing clear benchmarks for human-level AI. It addresses the gap between public perception and scientific reality.

None (Focus is on conceptual understanding and critique rather than formal frameworks)

7 min

AI Lifecycle: Data to Decisions

Explains data collection, model training, deployment, and monitoring. Ties each stage to compliance and security standards.

NIST AI Risk Management Framework, ISO/IEC 27001 (data security)

7 min

ML and Neural Networks

Covers neural networks and deep learning. Real-world uses in NLP, facial recognition, and image analysis.

NIST AI Risk Management Framework

7 min

What Is an AI System?

Breaks down AI into models, algorithms, and data. Examples from finance and healthcare show AI systems in action.

EU AI Act (system classification)

7 min

Types of Machine Learning

Clarifies how AI systems learn and operate through distinct methods: learning from labeled data, finding patterns in unlabeled data, or trial-and-error with feedback.

None (These are fundamental ML paradigms, not specific frameworks)

5 min

What Is Machine Learning?

Clarifies how machine learning differs from traditional programming. Includes use cases like fraud detection and content recommendations.

NIST AI Risk Management Framework

5 min

What Is AI?

Defines AI in relation to machine learning and deep learning. Explains key distinctions using real-world tools like smart assistants.

OECD AI Principles

Understanding AI

Concepts, Capabilities, and Core Components

5 min

What Is AI?

A short introducion to the core ideas behind artificial intelligence.

Referenced Frameworks

OECD AI Principles

Learning Time

5 min

What Is Machine Learning?

Understand how machines learn patterns instead of following rules.

Referenced Frameworks

NIST AI Risk Management Framework

Learning Time

5 min

7 min

Types of Machine Learning

ML Types: Supervised, Unsupervised, Reinforcement

Referenced Frameworks

None (These are fundamental ML paradigms, not specific frameworks)

Learning Time

7 min

ML and Neural Networks

Explore the building blocks of modern AI systems.

Referenced Frameworks

NIST AI Risk Management Framework

Learning Time

7 min

What Is an AI System?

Understand the basic components that make up an AI system.

Referenced Frameworks

EU AI Act (system classification)

Learning Time

7 min

AI Lifecycle: Data to Decisions

Follow the AI lifecycle from raw data to real-world outputs.

Referenced Frameworks

NIST AI Risk Management Framework, ISO/IEC 27001 (data security)

Learning Time

7 min

Types of AI

Learn the spectrum from narrow to superintelligent AI.

Referenced Frameworks

OECD AI Principles, NIST AI Risk Management Framework

Learning Time

7 min

Narrow vs. General AI

The spectrum from focused tasks to human-like intelligence.

Referenced Frameworks

OECD AI Principles, IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems

Learning Time

7 min

10 min

LLMs: Limits and Governance

What large language models can and can’t do — and why it matters.

Referenced Frameworks

OECD AI Principles, EU AI Act

Learning Time

10 min

AGI: Hype vs. Reality

Questioning the existence and definition of Artificial General Intelligence.

Referenced Frameworks

None (Focus is on conceptual understanding and critique rather than formal frameworks)

Learning Time

10 min

7 min

Hallucination and Bias

When AI makes things up — and why it’s a governance issue.

Referenced Frameworks

ISO/IEC 27001, GDPR

Learning Time

7 min

AI Bias: Data to Model

Identifying and mitigating data-driven bias in AI systems.

Referenced Frameworks

OECD AI Principles, ISO 42001

Learning Time

7 min

5 min

Synthetic Data: Purpose and Applications

Description of synthetic data and how it differs from anonymized data.

Referenced Frameworks

GDPR, EU AI Act

Learning Time

5 min

How Models Learn (and Fail)

Optimizing AI models for accuracy and generalization.

Referenced Frameworks

NIST AI Risk Management Framework

Learning Time

5 min

7 min

Explainable AI: Unpacking Transparency

Demystifying AI decisions for trust and accountability.

Referenced Frameworks

EU AI Act (Transparency), NIST AI Risk Management Framework (Explainability), ISO/IEC 23894.

Learning Time

7 min

Applied AI: From Lab to Industry

Applications of AI across healthcare, finance, and marketing.

Referenced Frameworks

OECD AI Principles, EU AI Act

Learning Time

7 min

10 min

Governing AI: From Frameworks to Function

End-to-end oversight for responsible AI.

Referenced Frameworks

NIST AI Risk Management Framework, ISO/IEC 27001

Learning Time

10 min

3 min

AI Literacy : Policy and Practice

Defining the essential AI knowledge required for informed oversight and effective governance.

Referenced Frameworks

EU AI Act, OECD AI Principles, NIST AI Ris

Learning Time

3 min

10 min

Principles for Responsible AI

Guiding AI development for human well-being and responsible innovation.

Referenced Frameworks

OECD AI Principles, UNESCO Recommendation on the Ethics of AI, IEEE 7000 Series, AI Bill of Rights.

Learning Time

10 min

The EU AI Act

The EU's Landmark AI Regulation

Overview of the EU AI Act

Key elements of the EU AI Act.

The AI Act and GDPR: Areas of Overlap

Navigating the intersection between GDPR and the AI Act.

Global Benchmarks: Comparing the EU AI Act to Other Frameworks

Comparing EU AI Act with global standards.

Penalties and Enforcement

Consequences for non-compliance with EU AI regulations.

Timelines and Compliance Planning

Preparing for EU AI Act compliance.

Post-Market Surveillance for High-Risk AI

Ensuring continuous compliance beyond deployment.

Incident Reporting and Monitoring

Ongoing AI system monitoring.

Risk Assessments and CE Marking

Certifying AI system compliance.

Transparency and User Disclosure Rules

AI system disclosure obligations.

Obligations for AI Providers & Deployers

Understanding responsibilities for AI providers, deployers, and other key roles.

Legal Roles and Accountability

AI governance responsibilities.

How AI Systems Are Classified

Risk classification of AI systems.

Prohibited AI: The EU AI Act's Red Lines

Understanding prohibitions on use of AI in the EU.

High-Risk AI: Examples and Edge Cases

Addressing high-risk AI use cases.

AI Governance In Practice

Implementing Principles, Policies, and Oversight

minutes