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AI is playing a key part in reshaping and developing businesses, and in improving people’s daily lives. Different concepts, tools and technologies that use algorithms—such as blockchain, machine and deep learning, natural language processing, generative AI, etc.—are beginning to show exciting possibilities that will support and optimise human intelligence. If guided by an ethical and responsible approach, this field can offer numerous rewards.

How to make AI explainable?

• Have you ever wondered how AI reaches its decisions? The challenge is crucial: to enable humans to understand the results of an Artificial Intelligence system.
• Explainable AI techniques combine methods and processes designed to show the operating logic of an algorithm and provide clear explanations to users of how AI makes decisions.
• There are already myriad techniques depending on context, target audience and the impact of the algorithm. And that’s without factoring in the development of generative AI systems, which raises new challenges in terms of explainability techniques.
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Explainability of artificial intelligence systems: what are the requirements and limits?

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Data and AI Ethics Council, guarantor of responsible AI at Orange

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A group of people is attending a presentation of BrainBox AI at the Orange OpenTech event. Two presenter stands in front of a screen displaying graphs and information on the topic. The participants are listening attentively and appear engaged in the discussion.

BrainBox AI to cut commercial real estate emissions by up to 40%

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A group of mining workers is listening to a colleague who is explaining something. They are wearing yellow safety helmets and masks. The environment is dark, with rocky walls visible in the background. The guide is using a headlamp to light his way.

AI fed on data from gas sensors and smart cameras prevents workplace accidents

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Orange OpenTech 2024: AI is here

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Factiverse: reliable AI fact-checking in more than 100 languages

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