Records Governance for Artificial Intelligence and Machine Learning Systems Course

Course Introduction

Artificial Intelligence (AI) and Machine Learning (ML) systems are transforming how organizations collect, process, analyze, and act on data. However, the effectiveness, fairness, and reliability of these systems depend heavily on the quality, governance, and integrity of the underlying records. This course provides a comprehensive exploration of how records governance frameworks support the development, deployment, and monitoring of AI and ML systems.

In AI-driven environments, data is not just an input but the foundation of all algorithmic decision-making. Poorly governed records can lead to biased models, inaccurate predictions, and unreliable outputs. This course equips participants with the skills to ensure that data used in AI and ML systems is properly structured, validated, and governed throughout its lifecycle to support trustworthy outcomes.

A strong focus is placed on the relationship between records governance and data lifecycle management in machine learning pipelines. Participants will explore how data collection, labeling, training, validation, and deployment processes must be governed to ensure transparency, accountability, and reproducibility in AI systems. Without strong governance, machine learning models risk becoming opaque and unreliable.

The course also examines ethical and regulatory dimensions of AI and ML systems, including fairness, bias mitigation, explainability, and compliance with emerging AI governance frameworks. Participants will learn how records governance plays a critical role in ensuring that AI systems operate within ethical boundaries and regulatory expectations.

Emerging technologies such as generative AI, large language models, autonomous systems, and predictive analytics are reshaping industries globally. This course explores how records governance frameworks must evolve to manage these advanced systems, ensuring that data integrity, model accountability, and auditability are maintained in complex AI environments.

By the end of the course, participants will be able to design and implement robust records governance frameworks that support AI and ML systems. They will be equipped to ensure data quality, reduce algorithmic risk, enhance model transparency, and strengthen trust in AI-driven decision-making systems.

Duration

10 days

Who Should Attend

• AI and machine learning engineers
• Data scientists and data analysts
• Records and information governance professionals
• AI ethics and compliance officers
• ICT and digital transformation leaders
• Big data and analytics professionals
• Risk management and audit professionals
• Software developers working with AI systems
• Policy makers in digital and AI governance
• Research and academic professionals in AI and data science

Course Objectives

• Equip participants with advanced knowledge to design and implement records governance frameworks that support artificial intelligence and machine learning systems while ensuring data integrity, transparency, and accountability across all stages of the data lifecycle.
• Strengthen ability to manage and govern datasets used in AI and ML pipelines, including data collection, labeling, preprocessing, training, validation, and deployment processes.
• Enable learners to identify and mitigate risks associated with biased, incomplete, or poorly structured data that can negatively impact AI model performance and decision-making outcomes.
• Develop skills to ensure data quality, consistency, and traceability in machine learning systems, enabling reproducibility and auditability of AI-driven results.
• Enhance understanding of ethical principles in AI governance, including fairness, transparency, accountability, and explainability of algorithmic systems.
• Build capacity to design governance frameworks that align AI and ML systems with regulatory requirements and emerging global AI governance standards.
• Strengthen ability to manage records lifecycle processes in AI environments, ensuring proper documentation and control of datasets used in model development and deployment.
• Improve competencies in implementing data versioning, lineage tracking, and metadata management in machine learning workflows.
• Enable participants to integrate governance mechanisms into AI model training pipelines to ensure responsible and controlled use of data assets.
• Prepare learners to evaluate AI systems for compliance, risk exposure, and ethical implications related to data usage and decision-making.
• Empower professionals to manage emerging AI technologies such as generative AI and large language models within structured governance frameworks.
• Develop strategic leadership capabilities for overseeing AI governance initiatives that ensure trustworthy, transparent, and accountable machine learning systems.

Comprehensive Course Outline

Module 1: Foundations of AI and Records Governance

• Core principles of AI systems and their dependence on structured data governance.
• Understanding the role of records in machine learning and AI development.
• Relationship between data quality and AI system performance.
• Evolution of AI governance frameworks in modern enterprises.

Module 2: Data Lifecycle in Machine Learning Systems

• Managing data from acquisition to deployment in AI pipelines.
• Ensuring structured governance across ML development stages.
• Addressing risks in unmanaged or unstructured datasets.
• Ensuring consistency in data used for model training and testing.

Module 3: Data Quality and Integrity in AI Systems

• Ensuring accuracy and reliability of datasets used in AI models.
• Managing data cleansing and validation processes.
• Identifying and correcting inconsistencies in training data.
• Strengthening trust in AI outputs through data governance.

Module 4: Metadata and Data Lineage Management

• Tracking data origins and transformations in AI systems.
• Implementing metadata frameworks for machine learning datasets.
• Ensuring transparency in data usage across AI pipelines.
• Supporting auditability through structured lineage systems.

Module 5: AI Model Training Governance

• Governing datasets used in supervised and unsupervised learning.
• Managing training data selection and preprocessing controls.
• Ensuring reproducibility of machine learning models.
• Preventing data leakage and model contamination risks.

Module 6: Bias and Fairness in AI Systems

• Identifying bias in datasets and machine learning models.
• Implementing fairness controls in AI governance frameworks.
• Addressing ethical risks in algorithmic decision-making.
• Ensuring equitable outcomes in AI-driven systems.

Module 7: Explainability and Transparency in AI

• Designing explainable AI systems for accountability.
• Ensuring transparency in model decisions and outputs.
• Managing interpretability of machine learning models.
• Supporting stakeholder trust through explainable governance.

Module 8: AI Ethics and Responsible Data Use

• Ethical considerations in AI and machine learning governance.
• Ensuring responsible use of data in algorithmic systems.
• Addressing societal impacts of AI-driven decisions.
• Aligning AI systems with ethical governance principles.

Module 9: Regulatory Frameworks for AI Governance

• Understanding global AI governance and compliance frameworks.
• Managing legal risks in AI and ML systems.
• Aligning AI systems with emerging regulatory requirements.
• Ensuring compliance in data-driven AI environments.

Module 10: AI in Risk and Decision Systems

• Using AI for predictive risk analysis and decision-making.
• Governing data used in automated decision systems.
• Ensuring accountability in AI-driven risk models.
• Managing operational risks in AI applications.

Module 11: Generative AI and Large Language Models

• Managing governance challenges in generative AI systems.
• Ensuring data integrity in large language model training.
• Addressing hallucination and misinformation risks in AI outputs.
• Governing usage of advanced generative technologies.

Module 12: AI Security and Data Protection

• Securing datasets used in AI model development.
• Protecting AI systems from adversarial attacks.
• Managing access controls in machine learning environments.
• Ensuring confidentiality and integrity of AI data assets.

Module 13: AI Model Deployment Governance

• Governing deployment of machine learning models in production.
• Ensuring controlled release of AI systems into operations.
• Monitoring model performance and data drift.
• Managing lifecycle governance of deployed AI systems.

Module 14: AI Monitoring and Performance Evaluation

• Tracking AI system performance over time.
• Identifying degradation in model accuracy and reliability.
• Ensuring continuous improvement of AI systems.
• Implementing monitoring dashboards and evaluation metrics.

Module 15: AI Governance Framework Design

• Designing enterprise-wide AI governance structures.
• Integrating records governance into AI ecosystems.
• Establishing accountability frameworks for AI systems.
• Aligning governance with organizational AI strategy.

Module 16: Future Trends in AI and Data Governance

• Emerging technologies shaping AI governance frameworks.
• Future risks in autonomous and adaptive AI systems.
• Evolution of global AI regulatory landscapes.
• Preparing organizations for next-generation AI systems.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification         

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.