An approach based on experience and rigor
CRIM, the oldest AI research center in Quebec, was a pioneer in integrating trust principles into its work on speech recognition, language processing and biometrics. This experience has shaped a robust methodology.
An integrated approach right from the design stage
Artificial intelligence is profoundly transforming economic, social and technological sectors. But for this transformation to be beneficial, it must be based on trust built right from the design stage. This is the fundamental principle of the Trust by Design approach that CRIM has been applying since the late 1990s.
The approach
- A robust technical architecture
- Ethical, filtered or anonymized data
- Active security against threats
- Explainable and auditable models
- Strict compliance with current legislation (LPRPSP, LPRPDE, RGPD)
- A comprehensive methodology for concrete results
Trusted AI is more than just an afterthought. It can be integrated into every stage of a project’s lifecycle.
Planning
Identification of risks, ethical and regulatory requirements
Design
Integrating the principles of robustness, reliability and safety
Professional
Choice of models that can be explained and traced
Deployment
Control of use, transparency of decisions
Evolution
Continuous adaptation to changing circumstances
Benefits
- Better productivity
- Sales growth
- Enhanced services
- Increased user satisfaction
- Reduce legal and reputational risks
Your trusted AI ally
As a neutral, independent organization, CRIM is ideally positioned to support Quebec companies in the responsible integration of AI. Our goal: to help you design high-performance systems that are ethical and trustworthy.
CRIM's white paper on trusted AI
Our white paper explores the fundamental principles and best practices for integrating trusted AI into innovative projects.
This document offers an in-depth reflection on the ethical, technical and social challenges of AI, a methodological guide covering each stage of development, and concrete case studies covering automated decision-making, quality assurance, intelligent assistants and biometric verification.
The three key parts of the white paper
Trusted AI guiding principles
Understand the issues related to trust in AI systems and strategies for strengthening it.
Methodology guide
A detailed framework for implementing trusted AI at every stage of the development cycle, from planning to deployment.
Case studies
Real-world examples illustrating the impact of trusted AI in sectors such as decision-making, quality assurance, AI assistants and biometric verification.
