As AI models advance, particularly in generative capabilities, their complexity makes them less understandable, presenting challenges for explainability. Current XAI techniques struggle to clarify the workings of large language models and GANs due to their non-rule-based design and high-dimensional outputs. There's a pressing need for new tools that address the probabilistic nature of these models. Accountability in AI is also critical, particularly in fields like finance and healthcare where decisions directly affect lives. Future developments may involve human-centered reasoning to create models that emulate human thought processes, enhancing explainability.
AI models like large language models (LLMs) and GANs are harder to explain despite their increasing accuracy, pushing the field of explainable AI to evolve.
The evolution of Explainable AI (XAI) is crucial as AI systems increasingly impact human decisions, necessitating transparency and accountability in their operations.
Key elements of XAI involve fairness, transparency, and accountability, demanding collaboration among ethicists, regulators, and developers to create responsible AI.
The future of AI explainability likely hinges on developing models that reason similarly to humans, pushing toward concept-based and human-centered XAI approaches.
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