首页|Studies Conducted at Institute of Mathematics and Computer Science on Artificial Intelligence Recently Published [EXplainable Artificial Inte lligence (XAI)-From Theory to Methods and Applications]
Studies Conducted at Institute of Mathematics and Computer Science on Artificial Intelligence Recently Published [EXplainable Artificial Inte lligence (XAI)-From Theory to Methods and Applications]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news originating from the Ins titute of Mathematics and Computer Science by NewsRx correspondents, research st ated, "Intelligent applications supported by Machine Learning have achieved rema rkable performance rates for a wide range of tasks in many domains. However, und erstanding why a trained algorithm makes a particular decision remains problemat ic." Financial supporters for this research include Coordenacao De Aperfeicoamento De Pessoal De Nivel Superior-brasil (Capes)-finance Code 001; Sao Paulo Research F oundation; National Council For Scientific And Technological Development; Fapesp ; Cnpq. The news journalists obtained a quote from the research from Institute of Mathem atics and Computer Science: "Given the growing interest in the application of le arning-based models, some concerns arise in the dealing with sensible environmen ts, which may impact users' lives. The complex nature of those models' decision mechanisms makes them the so-called 'black boxes,' in which the understanding of the logic behind automated decision-making processes by humans is not trivial. Furthermore, the reasoning that leads a model to provide a specific prediction c an be more important than performance metrics, which introduces a trade-off betw een interpretability and model accuracy. Explaining intelligent computer decisio ns can be regarded as a way to justify their reliability and establish trust. In this sense, explanations are critical tools that verify predictions to discover errors and biases previously hidden within the models' complex structures, open ing up vast possibilities for more responsible applications. In this review, we provide theoretical foundations of Explainable Artificial Intelligence (XAI), cl arifying diffuse definitions and identifying research objectives, challenges, an d future research lines related to turning opaque machine learning outputs into more transparent decisions."
Institute of Mathematics and Computer Sc ienceArtificial IntelligenceCyborgsEmerging TechnologiesMachine Learning
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