Cross disciplinary XAI

Reflecting on the integration of cross-disciplinary insights into the development of XAI systems, we understand the significance of creating solutions that are technically competent, ethically sound, and socially responsible. For AI systems, like a healthcare assistant, to effectively communicate diagnostic decisions to a diverse audience, it's crucial to incorporate perspectives from social sciences, humanities, and ethical studies, ensuring the technology aligns with human values and societal norms.

You are developing an AI-powered healthcare assistant intended to explain diagnostic decisions to a wide range of users, including medical professionals and patients. To ensure the explanations are ethically aligned, easily comprehensible, and actionable, how should cross-disciplinary insights be integrated into the system's design?

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Explainable Artificial Intelligence (XAI) Concepts

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Latihan ini adalah bagian dari kursus

Explainable Artificial Intelligence (XAI) Concepts

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Mulai Kursus Gratis

We delve into Explainable AI (XAI), emphasizing its role in rendering AI systems transparent, interpretable, and trustworthy. We explore AI's capabilities in prediction and content generation, underscoring the necessity for clear decision-making processes. Additionally, we investigate methods to make complex AI models more comprehensible to a wide range of audiences.

Exercise 1: Why Explainable AI matters Exercise 2: Transparency or interpretability Exercise 3: Interpretable scenarios Exercise 4: XAI objectives Exercise 5: The technicalities of XAI Exercise 6: Understanding the fundamentals of XAI Exercise 7: Accuracy and interpretability Exercise 8: Communicating about XAI Exercise 9: Audience segmentation Exercise 10: Balancing accuracy and simplicity

We explore Explainable AI (XAI) techniques, categorizing them into model-specific, model-agnostic, local, and global explanations to clarify AI decision-making. We discuss regression and classification for model-specific insights and introduce SHAP and LIME to interpret black box models. Additionally, we address the complexity of Large Language Models (LLMs), emphasizing the need for transparency in their decision-making processes.

Exercise 1: XAI techniques Exercise 2: Local or global Exercise 3: Model-specific or model-agnostic Exercise 4: Model-specific explanations Exercise 5: Analogies for regression and classification Exercise 6: Black box models in decision making Exercise 7: Model-agnostic explanations Exercise 8: SHAP, LIME or Both?Exercise 9: Role of SHAP and LIME in XAI Exercise 10: Explainability of LLMs Exercise 11: Understanding LLMs Exercise 12: Reasoning of LLMs

We explore the transformative impact of XAI in making artificial intelligence more accessible and user-friendly across various sectors. By integrating explainability from the outset, we ensure AI systems are transparent, fostering trust and facilitating a deeper collaboration between humans and machines. Through real-world case studies, we highlight how XAI demystifies complex AI decisions, empowering users with diverse technical backgrounds to leverage AI insights for more informed decision-making.

Exercise 1: Tailoring AI to the user Exercise 2: XAI for the end-user Exercise 3: Risks of not including the end-user Exercise 4: XAI by design Exercise 5: Job applicant screening Exercise 6: Applying XAI by design Exercise 7: XAI for online learning Exercise 8: XAI in action Exercise 9: XAI requirements per industry Exercise 10: Trusting the AI system Exercise 11: Future of XAI Exercise 12: Cross disciplinary XAI

Latihan Saat Ini

Exercise 13: Proactive development and deployment Exercise 14: Recap