How can we ensure a trustworthy use of automated decision-making systems (ADMS) in the public administration? AlgorithmWatch has developed a concrete and practicable impact assessment tool for ADMS in the public sector. This publication provides a framework ready to be implemented for the evaluation of specific ADMS by public authorities at different levels.

Human rights impact assessments of AI systems require meaningful engagement of individuals and groups most affected.It is particularly relevant for products and services using AI because there is still not enough practical guidance on how to involve stakeholders in the design, development and deployment of AI systems.

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.

Fujitsu AI Ethics Impact Assessment assesses the potential risks and unwanted consequences of an AI system throughout its lifecycle and produces evidence which can be used to engage with auditors, approvers, and stakeholders. This is a process-driven technology that allows to: 1) map all interactions among the stakeholders and the components of the AI system; 2) assess the ethical risks emerging from such interactions; 3) understand the mechanisms whereby incidental events could occur, based on previous AI ethics incidents.

The Data Carbon Scorecard provides a rapid assessment of the environmental impact of your proposed data project enabling you to quickly gauge the CO2 implications of your data project and determine the ‘best’ course of action for the environment. Through answering just 9 questions, the scorecard provides you with a traffic light display of data CO2 hotspots and an overall Data CO2 Score for the whole project within minutes. The scorecard can be used by any individual, team, or organization.

QuantPi’s platform unites AI testing with AI governance. It is the cockpit of AI-first organizations to collaborate on – to efficiently and responsibly understand, enhance and steer their individual AI models as well as their complete AI landscape.

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

Aival software allows non-technical users to evaluate commercial AI products in terms of performance, fairness, robustness and explainability (healthcare / imaging)

The KIDD process aims to empower companies and their employees to participate in shaping and introducing AI applications, ensuring that newly introduced systems comply with jointly negotiated ethical requirements. The KIDD process is based on the assumption that the development of software applications that have far-reaching implications for companies and employees should not be the sole responsibility of developers or decision-makers, but that users and employees should be fully involved in the design of the digital system.

To accelerate the journey to net zero, our team has designed the first publicly available tool to be used on new data projects to assess and measure the data CO2 footprint along the dataflow, called the “Data Carbon Ladder”. Using this tool, organizations can reduce their data CO2 footprint by ensuring optimal dataset(s) selection, frequency of updates, storage location, and analytics for the project to not only succeed but to do so in the most environmentally friendly way.

Fast AI with Assurance, Integrity, and Reliability for comprehensive AI governance and oversight.

This paper addresses the complex and contentious issues surrounding AI governance, focusing on organizing overarching and systematic approaches for the transparency of AI algorithms, and proposes a practical toolkit for implementation. Designed to be highly user-friendly for both businesses and government agencies engaging in self- and co-regulation, the toolkit presents disclosure items in a list format, allowing for discretion and flexibility when considering AI algorithm providers, users, and risks.

FRAIA assesses the risks to human rights posed by algorithms and promotes measures to address them. It fosters dialogue among professionals involved in algorithm development or deployment. The client is accountable for implementing FRAIA to prevent uncertain outcomes of algorithm use. FRAIA mitigates risks of carelessness, ineffectiveness, or violation of citizens' rights.

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

The Human-Ai Paradigm for Ethics, Conduct and Risk (HAiPECR)

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.

The framework helps to identify and manage risks of algorithmic bias and discrimination and to promote equality in the use of AI. It is especially intended for public sector use in close collaboration between public procurers and technical AI developers.

Zupervise is a unified risk transparency platform for AI governance.

An AI governance and risk management platform that helps organisations understand and manage the risks that come with AI, whilst navigating the emerging regulatory landscape

Privacy Library Of Threats for AI/ML