Explainable AI fosters collaboration between people and machines
Today most machine learning applications do not allow you to understand the operations and the basis of algorithms, making machine learning a kind of black box.
Explainable Artificial Intelligence (XAI) was born as a direct consequence of the need to give an answer to this issue, with a set of techniques and approaches useful for understanding, presenting and providing a transparent view of AI models, thus infusing greater confidence in the algorithms used and giving important feedbacks for improving results.
Organizations are relying more and more on Artificial Intelligence (AI) and machine learning models. This leads to increasing attention on the reliability, correctness and ethics of the results: Artificial.
Intelligence has to be explainable and above all it must be understandable to end users.
For these reasons, among our core focus fields there is the explainability of the AI models results. Because we think that analytics and artificial intelligence should help, but not completely replace, human experience and understanding.
At LARUS we are specialized in developing solutions utilizing context and the relationships between data to overcome the limitations of the traditional black box problem in artificial intelligence.
By using Graph-AI, we are able to provide explanations for the results offered by artificial intelligence, putting people at the center of the tools and giving them the power to make decisions. This approach allows for a more transparent and trustworthy use of AI, as the reasoning behind the results is made clear to the user.
Additionally, thanks to the strong partnership with the laboratories of Fujitsu Research of America, LARUS is at the forefront of the use of graph AI and is dedicated to creating tools that empower people and enhance the capabilities of artificial intelligence.
Galileo.XAI, our flagship product born after a decade of experience in graph technologies, is the sample case of the explainability of AI models results. Our insight data platform simplifies the understanding of big data and displays information in a clear and intuitive way, highlighting the hidden relationships existing between data and observable events and exploit connections and graphs as a basis for artificial intelligence algorithms in a transparent and explainable way, avoiding the black box effect of the results and increasing the level of reliability of the investigations. The reason is that analytics and artificial intelligence should help, but not completely replace, human experience and understanding.
Explainable AI Through Knowledge Graph
Explainable AI consent humans to understand why conclusion was reached or how machines make decisions or specific judgments.
Discover our solution Galileo.XAI
Galileo.XAI uses graphs as an input for explainable AI algorithms and is able to make predictions by capturing feedbacks capable of fueling improvement processes always in a Human Centered way.
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