Judicial Decision-Making and Explainable AI (XAI) – Insights from the Japanese Judicial Systemdoi:10.17951/sil.2023.32.4.157-173Yamada, YachikoStudia Iuridica Lublinensia
Many of today’s decision-making tools are powered by or augmented with AI and can produce highly accurate results, but they can also be highly complex. This level of complexity can lead to a ‘black box’ situation, where it may not be intuitive to deduce what factors lead to a particul...
when a model's decision-making processes aren't clear, trust in the model can be an issue. An explainable AI model aims to address this problem, outlining the steps in its decision-making and providing supporting evidence for the model's outputs. A truly explainable model offers...
XAI is the method of making AI decision-making processes understandable and accessible to human users.
gives users the tools they need to identify undesirable results and intervene in the decision-making process. Besides,understanding the processis a great way to discover and repair design flaws in algorithms. Fair, Accountable & Transparent AI ...
AI algorithms' results are not understandable, even by engineers who perform the algorithms. In supply chain management, AI algorithms are referred to as “black boxes,” which reflects the difficulty in considering them as decision-making tools. XAI adds explainability and interpretability to AI-...
What is Explainable AI? Explainable AI refers to the ability of AI systems to provide logical explanations for results, making the AI-driven decision-making process transparent and understandable to humans. Traditionally, AI algorithms such as deep neural networks are viewed as ‘Black Boxes’, prod...
Transparent and Accurate COVID-19 Diagnosis: Integrating Explainable AI with Advanced Deep Learning in CT Imaging This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle ... MM Hassan,SA Alqahtani,MS Alra...
When artificial intelligence (AI) is used to make high-stakes decisions, some worry that this will create a morally troubling responsibility gap—that
Why explainable AI matters It is crucial for an organization to have a full understanding of the AI decision-making processes with model monitoring and accountability of AI and not to trust them blindly. Explainable AI can help humans understand and explain machine learning (ML) algorithms, deep ...