The Need for Transparency in AI
In today’s fast-paced digital landscape, artificial intelligence (AI) has become a powerful tool across various sectors, including finance, law, and healthcare. Yet, as these technologies evolve, the decisions made by AI systems often remain shrouded in mystery. This lack of transparency can lead to mistrust among users. Enter Explainable AI (XAI), which aims to demystify the processes behind machine decisions, ensuring that clarity replaces confusion.
This book provides a comprehensive review of the latest research in the area of explainable artificial intelligence (XAI) in health informatics. It focuses on how explainable AI models can work together with humans to assist them in decision-making, leading to improved diagnosis and prognosis in healthcare. This book includes a collection of techniques and systems of XAI in health informatics and gives a wider perspective about the impact created by them. The book covers the different aspects, such as robotics, informatics, drugs, patients, etc., related to XAI in healthcare.
What is Explainable AI?
IA explicável refere-se a métodos e técnicas que permitem aos humanos compreender por que uma IA tomou uma decisão específica. Ao contrário dos sistemas de IA tradicionais, que operam como uma caixa-preta, a IA Explicável se concentra em fornecer insights sobre o processo de tomada de decisão. Essa transparência permite que os usuários entendam não apenas o resultado, mas também a lógica por trás dele, fomentando a confiança nas soluções de IA, e trazendo cada vez mais entusiastas para esse mundo extremamente fascinante e inovador.
Regulatory Implications and Benefits
For industries like finance and healthcare, the importance of Explainable AI cannot be overstated. As regulatory bodies increasingly demand transparency in machine decision-making, having a clear understanding of AI’s reasoning becomes essential, not merely optional. By implementing XAI, organizations can not only adhere to compliance requirements but also enhance their operational processes, boosting their reputation and enabling better decision-making overall.
In conclusion, embracing Explainable AI is a crucial step towards a future where technology and human understanding align seamlessly. As we continue to leverage AI’s potential, ensuring clarity in how these systems operate will help forge stronger bonds of trust between users and technology.
This book uncovers and presents various real-life applications in the areas of transportation, smart cities, manufacturing, agriculture, disaster management, finance, health care and in other areas by using cutting-edge advanced Machine Learning (ML) techniques such as Deep Learning and Explainable AI (XAI) models using IoT sensor data. The book provides various examples of analyzing large amounts of data, detecting patterns, and making predictions in real-time applications and detailed case studies with practical solutions using various state-of-the-art machine learning and IoT sensor data and all these aspects will benefit the stakeholders. The book is useful for academics, researchers, upper-undergraduate, master and Ph.D. students, engineers and practitioners in sensor/IoT and AI/ML technologies, methods, applications, and related areas, and it also offers valuable insights by suggesting future research directions and providing recommendations within the fields of AI and IoT.
