Explainable AI (XAI) Quiz

Explainable AI (XAI) and Its Significance

• XAI focuses on enhancing transparency and understandability of AI decision-making for humans
• Increasingly important as AI systems become more complex, particularly in healthcare, finance, and autonomous vehicles
• Importance of XAI lies in building trust and accountability, as well as addressing ethical concerns related to biased decisions
• XAI methods include model interpretability techniques and rule-based explanations
• Model interpretability techniques involve feature importance, decision trees, and model-agnostic methods
• Rule-based explanations aim to create interpretable rules defining AI decision-making processes
• Challenges in XAI include the trade-off between accuracy and interpretability, especially in complex deep learning models
• Deep learning models, particularly neural networks, pose a challenge in creating understandable explanations for their decisions
• XAI plays a critical role in ensuring that AI decisions are comprehensible and can be trusted by users
• The need for XAI is driven by the increasing use of AI in decision-making processes across various industries
• XAI addresses concerns about biased and unfair decisions made by AI systems
• XAI enables stakeholders to examine and rectify underlying biases in AI models