Responsible AI: Interpretability & Transparency - Deutsch

External: Coursera Courses ↗ · Coursera

Open Course on External: Coursera

Free to audit · Opens on External: Coursera

Responsible AI: Interpretability & Transparency - Deutsch

Coursera · Intermediate ·🛡️ AI Safety & Ethics ·4mo ago

Key Takeaways

Applies interpretability and transparency techniques to responsible AI development using practical tools and methods

Original Description

In diesem Kurs werden Konzepte in Bezug auf die Interpretierbarkeit und Transparenz von künstlicher Intelligenz vorgestellt. Sie erfahren, warum die Transparenz der KI für Entwickler-Teams wichtig ist. Dabei lernen Sie praktische Techniken und Tools kennen, mit denen Sie sowohl die Interpretierbarkeit als auch die Transparenz von Daten und KI-Modellen optimieren können.
Watch on External: Coursera ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related Reads

📰
Every AI Security Scanner We Tried Disagreed With Each Other.
AI security scanners can produce conflicting results, highlighting the need for careful evaluation and validation of their findings
Medium · LLM
 📰
Protecting Enterprise Data in Generative AI: Understanding the Shared Responsibility Model
Learn how to protect enterprise data in generative AI using the shared responsibility model
Medium · LLM
 📰
The Hugging Face Hack: What It Teaches Us About AI Security
The Hugging Face hack highlights AI security vulnerabilities without a traditional attack, emphasizing the need for proactive security measures
Medium · Cybersecurity
 📰
Microsoft Swapped Out 80% of Its Frontier Models and Got Better Results for Half the Price
Microsoft improved security system accuracy by replacing 80% of frontier models with a smaller one, reducing costs by half
Medium · Cybersecurity
Up next
Your AI Output Is Wrong and You Don't Know It Yet
Kevin Farugia AI Automation
Watch →