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The Pillars of Responsible AI: Navigating Ethical Frameworks and Accountability in an AI-Driven World

Unite.AI

In the rapidly evolving realm of modern technology, the concept of ‘ Responsible AI ’ has surfaced to address and mitigate the issues arising from AI hallucinations , misuse and malicious human intent. Balancing AI progress with societal values is vital for meaningful technological advancements that benefit humanity.

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Top Artificial Intelligence AI Courses from Google

Marktechpost

Introduction to AI and Machine Learning on Google Cloud This course introduces Google Cloud’s AI and ML offerings for predictive and generative projects, covering technologies, products, and tools across the data-to-AI lifecycle. It also introduces Google’s 7 AI principles.

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Explainable AI (XAI): The Complete Guide (2024)

Viso.ai

True to its name, Explainable AI refers to the tools and methods that explain AI systems and how they arrive at a certain output. Artificial Intelligence (AI) models assist across various domains, from regression-based forecasting models to complex object detection algorithms in deep learning.

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Learn how to assess the risk of AI systems

Flipboard

An important next step of the AI system risk assessment is to identify potentially harmful events associated with the use case. In considering these events, it can be helpful to reflect on different dimensions of responsible AI, such as fairness and robustness, for example.

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How Booking.com modernized its ML experimentation framework with Amazon SageMaker

AWS Machine Learning Blog

Essential ML capabilities such as hyperparameter tuning and model explainability were lacking on premises. Finally, the team’s aspiration was to receive immediate feedback on each change made in the code, reducing the feedback loop from minutes to an instant, and thereby reducing the development cycle for ML models.

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Establishing an AI/ML center of excellence

AWS Machine Learning Blog

Additionally, pay special attention to the changing nature of the risk and cost that is associated with the development as well as the scaling of AI. To provide ethical integrity , an AI/ML CoE helps integrate robust guidelines and safeguards across the AI/ML lifecycle in collaboration with stakeholders.

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Announcing the First Sessions for ODSC East 2024

ODSC - Open Data Science

Andre Franca | CTO | connectedFlow Explore the world of Causal AI for data science practitioners, with a focus on understanding cause-and-effect relationships within data to drive optimal decisions. Takeaways include: The dangers of using post-hoc explainability methods as tools for decision-making, and where traditional ML falls short.