Data Integrity: Strategies For Lawful Data, Ethical AI

About Course

Course Overview:

This course addresses the necessity of ethical and lawful AI, regulatory requirements, and the importance of understanding lawful data use in AI systems. Students will learn measures to improve AI and personal data practices, balanced AI risk mitigation strategies, and steps for the appropriate and lawful use of AI. The course also covers key components of Data Protection Impact Assessments (DPIAs), the significance of meaningful explanations and explainability in AI decisions, and approaches to reconcile data minimization with AI development.

Additionally, the course explores methods to address bias and discrimination in AI design and development, ensuring data quality, and assessing consequences for AI systems. Students will gain insights into investing time and resources in data preparation, labeling special category data, securing AI systems, and enhancing AI systems security. The role of human involvement in AI decisions, the importance of adequate training for reviewers, and collaboration with AI vendors are also key components of this curriculum.

Course Highlights:

The necessity of ethical and lawful AI
Regulatory requirements and the importance of lawful data use in AI systems
Measures to improve AI and personal data practices within organizations
Balanced AI risk mitigation strategies
Steps for the appropriate and lawful use of AI
Key features of Data Protection Impact Assessments (DPIAs)
Significance of meaningful explanations and explainability in AI decisions
Approaches to reconciling data minimization with AI development
Methods to address bias and discrimination in AI design and development
Ensuring data quality and assessing consequences for AI systems
Investing time and resources in data preparation
Labeling special category data and ensuring appropriate labeling
Securing AI systems and enhancing AI systems security
Human involvement in AI decisions and reviewer training
Collaboration with AI vendors and sourcing lawful AI

Detailed exploration of ethical and lawful AI requirements
Practical measures for improving organizational data practices
Balanced strategies for AI risk mitigation
Comprehensive understanding of DPIA processes and their key aspects
Techniques for ensuring AI transparency and explainability
Addressing early-stage bias and discrimination in AI development
Importance of data quality and preparation for robust AI systems
Security approaches to safeguard AI technologies
Human involvement in AI decision-making and adequate training for reviewers
Collaboration and lawful sourcing of AI systems

Course Content

Section 1: Foundations of Ethical and Lawful AI

Outlining the Need for Ethical and Lawful AI

06:21
Why Regulators should have an Understanding of the Lawful Use of Data and AI Systems

04:10
Two Important Steps for the Appropriate and Lawful Use of AI

03:12
Meaningful Human Involvement – An Introduction

04:06
Two Key Features of Human Reviewers of AI Decisions

03:20
Significance of Adequate Training and Authority in Human Reviewers of AI Systems

04:24
LAWGAME_Mastering Data Integrity

Section 2: Data Protection and Risk Management

Section 3: Improving AI and Personal Data Practices

Section 4: Enhancing Data Quality and Minimization

Section 5: Addressing Bias and Ensuring Fairness

An Introduction to Bias

04:14
Seven Measures to Avoid Bias and Discrimination in AI

03:43
How to Address Imbalanced Training Data in AI Systems

02:43
Three Approaches to Addressing Bias and Discrimination in Training Data

02:53
What are Variables in a Dataset?

03:44
Bias in Data Labeling and Variable Aggregation

02:21
Ensuring Fairness and Accuracy in the Labelling Process

02:43
Two Steps to Address Bias and Discrimination in the Early Stages of AI Design and Development

03:21
LAWGAME_Mastering Data Integrity

Section 6: Statistical Accuracy and Explainability

Accuracy and Probabilistic Calculations

03:17
What are Probabilistic Calculations in relation to AI systems?

03:26
Accuracy and Statistical Modeling

02:57
Should an AI system be a Hundred Percent Accurate?

03:14
Three Measures Demonstrating Commitment to Accuracy – Practical Example

03:13
Are AI Predictions Absolute Truths?

02:29
How Statistically Accurate Should AI Systems Be?

03:02
Implications of Insufficient Statistical Accuracy on Fairness and Data Minimization

02:54
LAWGAME_Mastering Data Integrity

Section 7: Governance and Security of AI Systems

Section 8: Legal and Regulatory Frameworks

Section 9: Transparency and Accountability

WARGAMES: MASTERING DATA INTEGRITY

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About Course

What Will You Learn?

Course Content

Section 1: Foundations of Ethical and Lawful AI

Outlining the Need for Ethical and Lawful AI

Why Regulators should have an Understanding of the Lawful Use of Data and AI Systems

Two Important Steps for the Appropriate and Lawful Use of AI

Meaningful Human Involvement – An Introduction

Two Key Features of Human Reviewers of AI Decisions

Significance of Adequate Training and Authority in Human Reviewers of AI Systems

LAWGAME_Mastering Data Integrity

Section 2: Data Protection and Risk Management

Three Key Aspects of a Data Protection Impact Assessment (DPIA)

What Lawful Basis is Most Appropriate for AI?

The DPIA Requirement for Processing Sensitive Categories of Data

Four Types of High-Risk Data Processing Unique to AI

DPIA Requirement for Major AI Initiatives

Four Steps to a Balanced AI Risk Mitigation Strategy

Key Data Protection Roles in AI Procurement

LAWGAME_Mastering Data Integrity

Section 3: Improving AI and Personal Data Practices

Eight Measures to Help Improve AI and Personal Data Practices in your Organization

Five Steps to Sourcing Lawful AI

Data Controller’s Accountability Obligation for Third-Party AI

Obligations of the Data Controller of AI Systems

Joint Controllership in AI Procurement

LAWGAME_Mastering Data Integrity

Section 4: Enhancing Data Quality and Minimization

Data Quality and Consequences Assessment for AI Systems

Importance of Time and Resource Investment in Data Preparation

Three Aspects to Labeling Special Category Data

Importance of Appropriately Labeled Data

Applying the Data Minimization Principle to AI

Data Minimization Principle – Six Key Factors

Reconciling Data Minimization and AI Development

LAWGAME_Mastering Data Integrity

Section 5: Addressing Bias and Ensuring Fairness

An Introduction to Bias

Seven Measures to Avoid Bias and Discrimination in AI

How to Address Imbalanced Training Data in AI Systems

Three Approaches to Addressing Bias and Discrimination in Training Data

What are Variables in a Dataset?

Bias in Data Labeling and Variable Aggregation

Ensuring Fairness and Accuracy in the Labelling Process

Two Steps to Address Bias and Discrimination in the Early Stages of AI Design and Development

LAWGAME_Mastering Data Integrity

Section 6: Statistical Accuracy and Explainability

Accuracy and Probabilistic Calculations

What are Probabilistic Calculations in relation to AI systems?

Accuracy and Statistical Modeling

Should an AI system be a Hundred Percent Accurate?

Three Measures Demonstrating Commitment to Accuracy – Practical Example

Are AI Predictions Absolute Truths?

How Statistically Accurate Should AI Systems Be?

Implications of Insufficient Statistical Accuracy on Fairness and Data Minimization

LAWGAME_Mastering Data Integrity

Section 7: Governance and Security of AI Systems

Three Approaches to Securing Your AI Systems

Three Steps to Proactively Enhancing AI Systems Security

Key Considerations for Explainability

The Importance of Meaningful Explanations of AI Decisions

LAWGAME_Mastering Data Integrity

Section 8: Legal and Regulatory Frameworks

Introduction to Solely Automated Decisions under the GDPR

What are legal and similarly significant effects?

Solely Automated Decisions versus Decisions with Human Involvement

Does the GDPR prohibit AI?

Valid Consent under the GDPR and Complex AI Operations

The Right to Withdraw Consent and AI Data Processing

LAWGAME_Mastering Data Integrity

Section 9: Transparency and Accountability

How to Achieve Transparency in AI Data Processing Operations?

Five Key Benefits of an AI Policy

Three Features of AI Vendor Collaboration

Importance of Data Mapping and Scheduled Reviews

LAWGAME_Mastering Data Integrity

WARGAMES: MASTERING DATA INTEGRITY

LAWGAME_Mastering Data Integrity

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