About This Course

In an era where Industry 4.0 is reshaping the landscape of manufacturing and business operations, understanding the legalities of data processing and AI applications becomes pivotal. This course offers an in-depth exploration of the legal aspects concerning the processing of both personal and non-personal data within these advanced environments. It emphasises the challenges inherent in these processes and underscores the importance of robust data management systems.

Description

The course covers the legal aspects of the processing of personal and non-personal data in complex Industry 4.0 environments, the challenges involved and the importance of solid data management mechanisms. It also covers the legal aspects of automated decision-making processes and the transparency of AI systems. The course will present the basics of EU legislation regarding data exchange, data management, reuse of public sector data, as well as legal requirements for data used for training and validation of AI systems. Key learning outcomes are:

• Understanding the Legal Landscape of Data Processing:

  • Grasp the complexities of handling personal and non-personal data in Industry 4.0 settings.

  • Examine key legal principles, including data privacy, security, and compliance requirements.

• Data Management in Industry 4.0:

  • Learn about the integration of IoT, cloud computing, and cyber-physical systems in data handling.

  • Analyse case studies on data management best practices and pitfalls.

• Challenges in Data Processing:

  • Discuss real-world scenarios highlighting the challenges in data processing within Industry 4.0.

  • Explore the balance between technological advancement and legal compliance.

• Legal Frameworks for Automated Decision-Making:

  • Delve into the legal aspects of AI and machine learning, focusing on automated decision-making.

  • Understand the implications of algorithmic decisions in various industries.

  • Discuss ethical considerations and the impact of AI decisions on society.

• EU Legislation on Data Exchange and Management:

  • Analyse EU legislation relevant to data exchange and management, including GDPR.

  • Examine the legal basis for the reuse of public sector data.

• AI System Validation and Training Data:

  • Investigate legal requirements and ethical considerations in using data for AI training and validation.

  • Learn about biases in training data and their legal implications.

Contents

Section 1: Introduction

• Smart Factory Data Complexity

Section 2: Machine Data as Non-personal data, Mixed Data Sets and the Contextual Nature of Data

• Non-personal data, Mixed Data Sets

• The Contextual Nature of Personal Data

• Key Takeaways

Section 3: Privacy Challenges in the Modern Manufacturing Environment

• Data Quality and Accuracy

• Challenges in Processing Personal Data

• Sensitive Data

• Automated Decision Makin

• Key Takeaways

Section 4: Managing and Sharing Data in the EU

• Challenges Related to “Data Ownership” in the EU

• Key Takeaways on “Data Ownership”

• EU Legislation on Data

  • Data Governance Act, Data Act

  • AI Act

  • Legal Framework for Data Key Takeaways

Section 5: Quiz