Explainable and Interpretable AI/ML in Applications
Explainable AI (XAI) plays a crucial role in the field of medicine, offering transparent and interpretable insights into AI-driven diagnoses and treatment recommendations. As AI models become more sophisticated, they can identify patterns and correlations in medical data that may not be immediately apparent to human experts. However, the »black box« nature of some AI algorithms raises concerns about trust and accountability in healthcare decision-making. By employing XAI techniques, medical practitioners can understand the reasons behind AI predictions, gaining valuable insights into patient outcomes and treatment plans. This transparency not only enhances the accuracy and reliability of AI-assisted medical decisions but also enables doctors to make more informed choices and provide patients with clearer explanations regarding their health conditions.
In the agricultural domain, XAI emerges as a critical tool for optimizing farming practices and ensuring sustainable food production. As agriculture increasingly integrates AI technologies to analyze complex datasets, the ability to interpret AI models becomes vital for effective decision-making. By leveraging XAI, farmers and agronomists can gain transparency into crop yield predictions, pest and disease outbreaks, and optimal resource allocation. Understanding the underlying factors influencing these AI-driven insights empowers farmers to implement targeted interventions and precision agriculture techniques. Moreover, explainable AI enhances the communication between AI systems and farmers, fostering trust and encouraging widespread adoption of AI-driven solutions in agriculture. As a result, XAI facilitates smarter farming practices, leading to increased productivity, reduced environmental impact, and more resilient food systems.
In the course of the digitalization of accounting processes, new possibilities arise by using machine learning techniques, but algorithms for decision support on financial and accounting data must meet exceedingly high ethical and regulatory requirements with respect to transparency and interpretability. Anomaly detection algorithms are utilized for efficiently checking billing transactions and accounting auditing to identify fraud and search for data errors. In practical applications, these anomalies are often not fully known before. Instead, the detection is based on learning underlying patterns in the data. Interpretability techniques help to understand these patterns and the decision boundaries of the algorithm to identify actual anomalies more efficiently, facilitate decision makers to handle anomalies adequately and communicate with the AI system. This way XAI enables a collaborative learning process of decision makers and AI system and strengthens the trust in the results.
Fraunhofer Institute for Industrial Mathematics ITWM