Software Architecture for Responsible Artificial Intelligence Systems: Practice in the Digitization of Industrial Drawings

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History

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Input output devices

by Doris McKnight

Software Architecture for Responsible Artificial Intelligence Systems: Practice in the Digitization of Industrial Drawings

4. Conclusion and Future Work

Future improvements (interaction methods, expanded AI stack)

Expanding AI models to support various industrial applications

More interactive options (e.g., voice control)

Benefits of AI in drawing digitization

Reduces time and errors in industrial drawing digitization

2. Key Components

Quality attributes (Usability, High performance, Accuracy)

Security & Privacy: Protection through federated learning

Performance: Efficient processing for large, multi-page drawings

Usability: Non-AI professionals should navigate easily

Role of AI technologies (OCR, computer vision, etc.)

Knowledge Graphs for relational data

OCR for text extraction

Computer Vision for element detection

Responsible AI software architecture

Responsible AI Principles: Aligns with EU's seven AI ethics principles (e.g., transparency, accountability).

3. Architecture Design

Collaborative multimodel fusion

Integrates specialized AI models (e.g., text and object detection)

Federated Learning for privacy

Federated Learning: Secures data by training models locally

Architectural Patterns: MVC, Layered Architecture

Layered Architecture: Separates concerns, enhancing usability and style adaptability

Model View Controller (MVC) for modular design

1. Introduction

Challenges in digitizing historical drawings

Complexity in historical drawing formats, domain-specific knowledge, and high error risk in manual digitization.

Purpose of responsible AI in industrial drawing digitalization