ALTAI: Practical Checklist for Trustworthy AI
ALTAI translates seven trustworthy AI requirements into an actionable self-assessment checklist, helping developers mitigate risks and ensure user benefits—refined after 350+ stakeholder pilots.
Implementing Trustworthy AI Principles
ALTAI operationalizes the seven key requirements from the 2019 Ethics Guidelines for Trustworthy AI—human agency and oversight, technical robustness and safety, privacy and data governance, transparency, diversity/non-discrimination/bias fairness, societal/environmental well-being, and accountability—into a dynamic checklist. AI developers and deployers use it to perform self-assessments, identifying concrete steps to embed these principles during development and deployment. This reduces unnecessary risks, ensuring AI delivers benefits like improved decision-making without exposing users to harms such as bias or privacy breaches.
The checklist's strength lies in its actionability: instead of abstract ethics, it provides targeted questions and mitigations, e.g., verifying robustness against adversarial attacks or ensuring explainability for high-risk systems. Piloting with over 350 stakeholders refined it from a prototype into a reliable tool, cutting through hype to focus on production-ready practices.
Development and Validation Process
Originating from the High-Level Expert Group on AI (AI HLEG), ALTAI built on their April 2019 Ethics Guidelines presented to the European Commission. Released July 17, 2020, after extensive piloting, it addressed feedback to make principles practical for real-world AI systems. This iterative process—prototype to tool—ensures the list works across AI use cases, from simple classifiers to complex agents, prioritizing what scales for small teams without heavy compliance overhead.
Trade-offs: While comprehensive, self-assessment relies on honest implementation; it's not a certification but a starting point for internal audits, complementing external regulations like the EU AI Act.
Accessing and Applying ALTAI
Download the PDF checklist directly or use the interactive web-based tool for guided assessments. Start by mapping your AI system against the seven requirements, scoring maturity levels, and actioning gaps—e.g., implement data minimization for privacy or fallback mechanisms for robustness. This fits indie builders and technical founders shipping AI features, taking minutes to initial run but yielding long-term risk reduction. Content is thin on specifics (seven requirements referenced but not detailed here), so pair with the full Ethics Guidelines for depth.