Artificial Intelligence Governance and Algorithmic Risk Training Course

Introduction

As Artificial Intelligence (AI) rapidly integrates into every facet of society and industry, the need for robust governance frameworks and effective algorithmic risk management has become critically important. The widespread deployment of AI systems, from automated decision-making to predictive analytics, brings immense opportunities but also introduces complex ethical, societal, and operational challenges. This training course is designed to empower professionals with the knowledge and practical skills to navigate this intricate landscape, ensuring the responsible, fair, and transparent development and deployment of AI.

This comprehensive program will delve into the principles of ethical AI, the identification and mitigation of algorithmic bias, and the establishment of sound governance structures. Participants will learn how to assess AI's impact, implement risk management strategies, and ensure compliance with emerging regulations. By equipping individuals with a deep understanding of AI governance and algorithmic risk, this course aims to foster a culture of responsible innovation, enabling organizations to harness the transformative power of AI while minimizing its potential harms.

Introduction

This 5-day intensive training course provides a deep dive into the critical aspects of AI governance and algorithmic risk. It blends theoretical foundations with hands-on exercises, case studies, and discussions to ensure participants gain practical skills applicable to real-world scenarios. The course will explore international best practices, regulatory trends, and the ethical considerations that underpin responsible AI development.

Duration: 5 Days

Target Audience:

  • AI developers, data scientists, and machine learning engineers
  • Business leaders and project managers overseeing AI initiatives
  • Compliance officers, legal professionals, and risk managers
  • Policy makers and regulatory affairs specialists
  • Ethics officers and responsible AI advocates
  • Consultants and academics interested in AI ethics and governance
  • Anyone involved in the design, deployment, or oversight of AI systems

Objectives: Upon completion of this course, participants will be able to:

  • Comprehend the fundamental concepts of AI governance and algorithmic risk.
  • Identify and categorize various types of algorithmic bias and their societal impacts.
  • Apply ethical frameworks and principles to the design and deployment of AI systems.
  • Develop and implement effective AI governance policies and procedures within an organization.
  • Conduct comprehensive AI risk assessments and formulate mitigation strategies.
  • Understand the evolving global regulatory landscape for AI and ensure compliance.
  • Implement mechanisms for transparency, explainability, and accountability in AI.
  • Foster a culture of responsible AI innovation and ethical decision-making.

Course Modules:

Module 1: Foundations of AI and Ethical AI Principles

  • Overview of Artificial Intelligence, Machine Learning, and Deep Learning
  • Understanding the AI lifecycle from data collection to deployment
  • Introduction to key ethical principles for AI: fairness, accountability, transparency, privacy
  • The societal impact of AI: opportunities and challenges
  • Historical context and evolution of AI ethics and regulation

Module 2: Understanding and Identifying Algorithmic Bias

  • Definition and sources of algorithmic bias: data, algorithm, and human bias
  • Types of bias: historical, representation, measurement, and aggregation bias
  • Methods for detecting bias in datasets and AI models
  • Tools and techniques for bias auditing and analysis
  • Case studies of real-world examples of algorithmic bias and their consequences

Module 3: AI Governance Frameworks and Structures

  • Key components of an effective AI governance framework
  • Establishing roles, responsibilities, and accountability for AI systems
  • Developing internal policies, guidelines, and codes of conduct for AI
  • The role of AI ethics committees and review boards
  • Integrating AI governance with existing enterprise governance structures

Module 4: Algorithmic Risk Management and Impact Assessments

  • Identifying and classifying AI-related risks: ethical, privacy, security, operational, reputational
  • Conducting Algorithmic Impact Assessments (AIAs) and Ethical Impact Assessments (EIAs)
  • Developing risk registers and mitigation plans for AI systems
  • Stress testing AI models for robustness and fairness
  • Continuous monitoring and post-deployment risk management

Module 5: Transparency, Explainability, and Interpretability (XAI)

  • The importance of transparency and explainability in AI systems
  • Techniques for achieving model interpretability (e.g., LIME, SHAP)
  • Communicating AI decisions to diverse stakeholders
  • Designing AI systems for human oversight and intervention
  • Balancing explainability with proprietary concerns and complexity

Module 6: Data Governance for AI

  • Principles of data privacy and security in AI applications
  • Compliance with data protection regulations (e.g., GDPR, CCPA)
  • Ensuring data quality, integrity, and representativeness for AI training
  • Managing data provenance and lineage for auditability
  • Ethical considerations in data collection, storage, and sharing for AI

Module 7: Regulatory Landscape and Compliance

  • Overview of global AI regulations and standards (e.g., EU AI Act, NIST AI Risk Management Framework)
  • Industry-specific regulations and guidelines for AI
  • Developing compliance strategies for AI systems
  • Certification and auditing for ethical AI and regulatory adherence
  • Legal implications of AI development and deployment

Module 8: Building a Culture of Responsible AI

  • Strategies for embedding ethical considerations into the AI lifecycle
  • Fostering interdisciplinary collaboration between technical and non-technical teams
  • Training and awareness programs for AI ethics and governance
  • Stakeholder engagement and public trust building
  • Future trends and challenges in AI governance and algorithmic risk management

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

 

Artificial Intelligence Governance And Algorithmic Risk Training Course in Madagascar
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