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Blazing a trail: The Story Behind the Copilot for AI Governance

trail offers the simplest way to operationalize AI governance, empowering developers and businesses to create trustworthy AI solutions.

Responsible AI

Documenting AI Development

A central part of improving transparency, traceability and understanding of AI systems and their development process is documentation. When done right, documentation helps to facilitate collaboration, demonstrate trust and enable compliance with policies or regulation, making it a core aspect of any AI governance.

EU AI Act

Regulating GenAI: Implications of the EU AI Act

The EU AI Act extensively regulates general purpose AI (GPAI), such as GenAI. This article explains how the EU classifies its risks and what obligations providers and integrators need to meet.

EU AI Act

EU AI Act: How risk is classified

The EU AI Act classifies AI systems into four different risk levels: unacceptable, high, limited, and minimal risk. Each class has different regulation and requirements for organizations developing or using AI systems. This article explains how AI systems and GPAI are classified and gives examples for high-risk cases.

Responsible AI

Leveraging Documentation: Implementing ML Model Cards for Better Decision Making

Model Cards are short documents containing essential information about ML models. By embracing Model Cards, businesses can make informed decisions, streamline documentation processes, and enhance transparency, ultimately ensuring responsible and efficient use of machine learning models.

Responsible AI

The First Steps Towards a More Structured ML Development: Experiment Tracking

Experiment tracking is the practice of organizing, logging, and analyzing metadata and artifacts of machine learning experiments. In this article, we will cover why you should track experiments, what it means in machine learning and which aspects you should consider for comprehensive AI management. We also provide you with a brief overview of existing experiment tracking tools.

Responsible AI

Implementing AI Governance: from Framework to Practice

AI governance is the key to enable trustworthy, responsible and efficient AI systems. It is crucial to establish governance frameworks as soon as organizations implement AI, to ensure its development and use align with ethical and societal standards. In this post, we will explore what AI governance is, why it is important, and frameworks that have been developed to guide its implementation.

EU AI Act

EU AI Act: 10 things high-risk companies need to know!

The EU AI Act establishes requirements and obligations for providers of high-risk systems. We summarized them in 10 points that companies in high-risk sectors should be aware of.

EU AI Act

EU AI Act: A brief overview

As part of their AI strategy, the European Union proposed the EU AI Act to ensure AI is developed and used in a safe, reliable, and transparent way. The new regulation classifies AI systems by their risks and has significant implications for organizations that develop or use AI systems within the EU. This article briefly summarizes the EU AI Act and how you can prepare for it today.

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EU AI Act: A brief overview

Latest posts

Blazing a trail: The Story Behind the Copilot for AI Governance

Documenting AI Development

Regulating GenAI: Implications of the EU AI Act

EU AI Act: How risk is classified

Leveraging Documentation: Implementing ML Model Cards for Better Decision Making

The First Steps Towards a More Structured ML Development: Experiment Tracking

Implementing AI Governance: from Framework to Practice

EU AI Act: 10 things high-risk companies need to know!

EU AI Act: A brief overview