The Algorithmic Transparency Certification for Artificial Intelligence Systems, by Adigital, is a robust framework for ensuring AI systems operate with paramount transparency, explainability and accountability. Grounded in universal ethical principles, it assesses AI systems on various critical factors, preparing organizations for evolving regulations like the EU AI Act, enhancing societal trust, and fostering competitive market advantage. This certification embodies a dynamic tool for continuous improvement, driving AI innovation with a solid foundation of responsibility and ethical consideration.

Our software equips data scientists and AI engineers with powerful tools to help them create robust and explainable AI systems in conformity with ISO standards. Trust Saimple to help you build the foundation for reliable AI.

A software-based EU AI Act compliance solution for your clients and AI value chain actors

A Psychometric scale to measure how users perceive and trust technology which can be applied to a variety of domains such as business, healthcare, public sector, among many others.

Enterprise platform for governance, risk and compliance in AI. Regulatory compliance with global laws intersecting with AI.

GRACE is an AI governance platform that offers a central registry for all AI models, tools, and workflows for risk mitigation, compliance, and collaboration across AI teams and projects. It provides real-time compliance, transparency, and trust while promoting AI innovation. The platform works across all major cloud vendors and offers out-of-the-box frameworks for complying with EU AI Act, AI standards, data regulations, and AI frameworks.

A scalable and systematic solution empowering enterprise to adopt and scale AI with confidence and enhance business performance.

A fully independent and impartial audit to ensure compliance with the bias audit requirements for New York City Local Law 144 and upcoming regulations.

A bespoke AI risk audit solution tailor-made to identify your enterprise project’s AI risks, comprising deep technical, quantitative analysis.

A set of guides to help enterprises mitigate some of the most common AI risks, presenting step-by-step solutions to protect against technical risks.

The Holistic AI library is an open-source tool to assess and improve the trustworthiness of AI systems. The library offers a set of techniques to easily measure and mitigate Bias across a variety of tasks.

Zupervise is a unified risk transparency platform for AI governance.

Naaia, the first AIMS on the market in Europe, is the governance and management solution for AI systems without compromising ethics or compliance (AI ACT)

A standardised label / short datasheet that can be attached to AI products to show their charactaristics with regards to principles like transparency, accountability, privacy, fairness and reliability.

Model cards are the tool for transparent AI documentation. Model cards are essential for discoverability, reproducibility, and sharing. You can find a model card as the README.md file in any model repo. Under the hood, model cards are simple Markdown files with additional metadata.

The use of Artificial Intelligence (AI) is one of the most significant technological contributions that will permeate the life of western societies in the coming years, providing significant benefits, but also highlighting risks that need to be assessed and minimized. A reality as disruptive as AI requires that its technology and that the products and […]

We believe that trust, transparency and technology belong together. But as digitalization accelerates, it is getting more and more difficult to understand what’s happening with your data. Algorithms and other digital tools operate in the background and can leave you feeling insecure when using digital services. With the Digital Trust Label, we’re putting trust and […]

The IEEE CertifAIEd™ Program offers a risk-based framework supported by a suite of AI ethical criteria that can be contextualized to fit organizations’ needs– helping them to deliver a more trustworthy experience for their users. IEEE CertifAIEd Ontological Specifications for Ethical Privacy, Algorithmic Bias, Transparency, and Accountability are an introduction to our AI Ethics criteria. We […]

In a world increasingly dominated by AI applications, an understudied aspect is the carbon and social footprint of these power-hungry algorithms that require copious computation and a trove of data for training and prediction. While profitable in the short-term, these practices are unsustainable and socially extractive from both a data-use and energy-use perspective. This work […]

Denmark’s new labelling program for IT security and responsible use of data. The D-seal will create digital trust for customers & consumers and drive digital accountability in companies. The D‑seal is relevant to all types of business and is adapted to the individual company. The number of criteria that a company has to meet depends […]