Introduction to Saidot methodology
Using Saidot, you will be able to
Govern your AI systems effectively in every step of their lifecycle
Manage AI related risks and create actionable treatment plans
Identify and document dataset cards related to your AI systems
Understand and access relevant knowledge related to your AI systems, including policies, risks, model and evaluations
Provide transparency to your internal and external stakeholder
Enable effective communication and cross-functional collaboration in AI Governance tasks
Scope
AI governance means applying systematic processes and ensuring accountability throughout the AI system lifecycle to deliver high-quality, responsible AI.
AI governance is a framework and set of practices and processes designed to ensure that AI-based systems are developed and deployed responsibly and in compliance with regulatory standards. AI governance covers the full lifecycle of an AI system, from the initiation phase to its deployment, operations and monitoring, and finally retirement. It defines a set of practices focused on ensuring product quality, and responsibility and elevating positive impacts of AI.
At its core, AI governance is a means to achieving responsible AI within an enterprise. Responsible AI refers to developing and using artificial intelligence in a way that is ethical, technically robust, and in compliance with legal requirements.
Importance
AI is transforming business processes across industries from routine to the most creative and innovative tasks. AI is used to streamline processes, improve decision-making, increase efficiency, and create completely new innovations. Generative AI has been estimated to impact 40% of jobs globally. As AI is advancing faster than ever, it is becoming essential for businesses to adopt these technologies to stay competitive.
At the same time, we’re growingly aware of the limitations and risks of AI. From hallucinations to copyright and contractual infringements, privacy vulnerabilities, and third-party reliance, the companies' risk landscape is populated with AI-related risks more than ever.
As AI has quickly found its way at the centre of enterprise strategies, companies are turning to AI governance. Effective AI governance ensures that AI systems meet quality standards and deliver value to businesses while building trust between AI providers and users. Moreover, AI governance ensures compliance with the developing regulative landscape, such as the EU’s AI Act, protecting organisations from legal and reputational risks.
No AI system is similar, but good AI governance must rely on best practices gained through rigorous research, deep expertise and previous experience. We bring these methodologies to you, helping you avoid reinventing the wheel while maintaining adaptability.
Basic principles
Our platform and methodology aims for
Making AI governance work as effective and easy as possible with our extensive Saidot Library knowledge base and Governance platform with concrete step-by-step guidelines and recommendation features
Creating a safe space for AI innovations and thus better business performance, compliance and wellbeing by increasing the understanding of the AI regulation, risks and limitations
Allowing the AI teams to focus on the governance tasks that are relevant for a given phase in the AI System lifecycle.
Helping our customers to identify the risks that are most important to pay attention to and avoid over mitigating risks for the same reasons.
Supporting effective collaboration around AI Governance, enabling cross-functional communication, following up progress and doing the best effort with the knowledge available at the time
Our methodology is based on extensive experience and leading AI standards (ISO/IEC 22989, ISO/IEC 23894, ISO/IEC 42001, NIST RMF).