Explainability in Process Mining: A Framework for Improved Decision-Making
In an era where data-driven insights are shaping the future of organizations, Process Mining (PM) has emerged as a transformative force, offering unprecedented opportunities to analyze and optimize complex processes. However, the full potential of PM remains untapped due to persistent challenges in...
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| Formato: | tesis doctoral |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Universidad de Santiago de Compostela (USC) |
| Repositorio: | Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela |
| Idioma: | inglés |
| OAI Identifier: | oai:minerva.usc.gal:10347/38244 |
| Acesso em linha: | https://hdl.handle.net/10347/38244 |
| Access Level: | acceso abierto |
| Palavra-chave: | Explainable AI Process Mining AI Governance 120304 Inteligencia artificial |
| Resumo: | In an era where data-driven insights are shaping the future of organizations, Process Mining (PM) has emerged as a transformative force, offering unprecedented opportunities to analyze and optimize complex processes. However, the full potential of PM remains untapped due to persistent challenges in understanding and explainability of implemented process technology, such as AI systems. This thesis establishes a structured investigation to explore the role of Explainable AI (XAI) in overcoming these barriers, fostering greater adoption and engagement with PM solutions, and ultimately bridging the gap between advanced technologies and human understanding. |
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