XAI lintas disiplin

Dengan mencerminkan integrasi wawasan lintas disiplin dalam pengembangan sistem XAI, kita memahami pentingnya menciptakan solusi yang andal secara teknis, berlandaskan etika, dan bertanggung jawab secara sosial. Agar sistem AI, seperti asisten kesehatan, dapat mengomunikasikan keputusan diagnostik secara efektif kepada audiens yang beragam, penting untuk memasukkan perspektif dari ilmu sosial, humaniora, dan studi etika, sehingga teknologi selaras dengan nilai manusia dan norma sosial.

Anda sedang mengembangkan asisten kesehatan bertenaga AI yang ditujukan untuk menjelaskan keputusan diagnostik kepada berbagai jenis pengguna, termasuk tenaga medis dan pasien. Untuk memastikan penjelasan selaras dengan etika, mudah dipahami, dan dapat ditindaklanjuti, bagaimana sebaiknya wawasan lintas disiplin diintegrasikan ke dalam desain sistem?

Latihan ini adalah bagian dari kursus

Konsep Explainable Artificial Intelligence (XAI)

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Mulai berolahraga

Latihan ini adalah bagian dari kursus

Konsep Explainable Artificial Intelligence (XAI)

SkillTag.level.beginnerSkillTag.label

4.8+

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: Menyesuaikan AI dengan pengguna Exercise 2: XAI untuk pengguna akhir Exercise 3: Risiko tidak melibatkan pengguna akhir Exercise 4: XAI sejak tahap desain Exercise 5: Penyaringan pelamar kerja Exercise 6: Menerapkan XAI melalui desain Exercise 7: XAI untuk pembelajaran daring Exercise 8: XAI dalam praktik Exercise 9: Persyaratan XAI per industri Exercise 10: Memercayai sistem AI Exercise 11: Masa Depan XAI Exercise 12: XAI lintas disiplin

Latihan Saat Ini

Exercise 13: Pengembangan dan penerapan proaktif Exercise 14: Rekap