Ethical AI
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Charter of our Commitment to Ethical AI
InnovFast
Artificial Intelligence (AI) is a powerful driver for innovation, but it raises ethical concerns among some users. To reassure our clients and strengthen their trust, it is essential to promote an ethical and responsible AI charter.
This approach illustrates our commitment to:
– Respect for human rights, by protecting personal data and identifying risks of abuse.
– Transparency and explainability, by documenting algorithms and clearly explaining their decisions.
– Eco-design, by reducing the environmental footprint of digital solutions.
– Security and resilience, with systems protected against cyberattacks and capable of functioning even in case of disruptions.
– Inclusion and accessibility, by ensuring that your tools are beneficial and accessible to all.
– Training and awareness, to help users understand and optimally use AI.
– Commitment and collaboration, by involving all stakeholders in the development of solutions.
– System interoperability, by allowing data transfer and reversibility without constraint.
Adopting these principles demonstrates our clear commitment to developing respectful, transparent, and human-centered AI, reassuring our clients about its responsible use.
Respect
for Human Rights
Due diligence: Identify and prevent risks of human rights violations related to the use of AI throughout the production chain.
Personal Data Protection: Comply with the General Data Protection Regulation (GDPR) to ensure the confidentiality and security of user information.
Transparency and Explainability
Algorithm Transparency: Document and clearly communicate the training data and operating mechanisms of the algorithms used.
Explainability: Be able to explain AI decisions in a way that is understandable to users, communicating potential bias risks.
Eco-design and Digital Sobriety
Resource Consumption Measurement: Adopt guidelines to evaluate and minimize the environmental footprint of the technologies used.
Energy Optimization: Prioritize technological solutions that consume less energy and resources, while reserving their use for priority objectives.
Security
and Resilience
Data Security: Implement rigorous security measures to protect data against cyberattacks and leaks.
System Resilience: Develop systems capable of maintaining their operation in case of disruptions.
Inclusion
and Accessibility
Digital Inclusion: Ensure that AI solutions are accessible and beneficial to all stakeholders, without discrimination.
Accessibility: Adapt tools so that they can be used by people with disabilities or specific needs.
Training and Awareness
Continuous Training: Offer regular training programs to users and developers for optimal understanding and use of AI tools.
Awareness of Ethical Issues: Promote awareness of the ethical and social impacts of AI among all stakeholders.
Engagement and Collaboration
Open Innovation Approach: Encourage open collaboration and the exchange of best practices among involved stakeholders, with multidisciplinary governance committees to oversee AI deployments.
User Participation: Include end-users in the tool development and improvement process.
System Interoperability
Data Reversibility: Design AI systems so that the data used to train models can be easily transferred to an alternative solution.
Algorithm Replicability: Sufficiently document the functioning of algorithms so that they can be reproduced or made available via Application Programming Interfaces (APIs).
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