Arborus Orange

INTERNATIONAL CHARTER FOR INCLUSIVE AI

By Arborus and Orange

INTRODUCTION

Artificial Intelligence (AI) is an excellent driver of development and progress in the fields of education, healthcare, the environment and industry, and it is an opportunity for reducing inequalities.

However, the potential of AI can be fully realized only by designing, deploying and operating it in a responsible and inclusive way, because AI must aim to serve everybody.

OUR OBJECTIVE

Create a framework of trust for all concerning the way in which AI systems are designed and used.

This Charter is an initial step before committing to the international GEEIS-AI label

It serves as a reference not only for companies developing technology but also for all those that implement AI, so that diversity is respected and the whole data value chain is based on a responsible approach that enables discriminatory biases to be identified and controlled.

OUR COMMITMENT

Arborus has been promoting gender equality and inclusion in the workplace on an international scale for 25 years, especially through the implementation of the Gender Equality European & International Standard (GEEIS).

The Orange Group, a founding member of the Arborus endowment fund and holder of the GEEIS label since 2011 in 17 countries, is one of the experts that make up the European Commission & Artificial Intelligence High-Level Expert Group (AI HLEG).

Creating trustworthy technology for people together !

OUR PLEDGE : THE COMPANIES SIGNING THIS CHARTER COMMIT TO:

Promoting gender equality and diversity in the teams that work on AI-based solutions.

Evaluating and reacting to all forms of discrimination that could arise from biased or stereotyped data.

Ensuring that data is of the right quality to guarantee the fairest possible AI solutions : data that is unified, consistent, verified, traceable and usable.

Providing awareness-raising training on stereotypes and bias that can lead to discrimination to designers, developers, and everyone involved in producing AI, to make them aware of their responsibilities

Raising user awareness of the risks of bias and stereotyping that can lead to discrimination in AI-based solutions (including HR, finance, customer relations, marketing, etc.), and encouraging them to integrate control points and iterative assessments in specifications.

Choosing carefully their suppliers and performing iterative supplier assessments to ensure that the entire AI value-chain is non-discriminatory.

Verifying AI-based solutions and continuously adapting processes.