PHASE $11.$ Input from User #$1($Direct Input or via TAI CARD$-$at registration$)$ AI System Lifecycle Phase: Identify the current phase of the AI system's lifecycle $($e$.$g$.,$ development, deployment, monitoring$).$ TAI Principle for Testing: Select the Trustworthy AI principle$($s$)$ to be assessed $($e$.$g$.,$ fairness, transparency, robustness$).$ Domain of Application: Define the intended domain or application area for the AI system $($e$.$g$.,$ healthcare, finance$).$ Type of Input Data: Specify the types of input data the AI system uses $($text$,$ images, sensor data, etc.$).$ Type of AI System: Identify the specific type of AI system $($e$.$g$.,$ generative AI$,$ predictive models$)$ Intended use: $2.$ system feedback $-$ GENERIC$=>-$ LEGAL $=>$AI ACT Risk classification$-$ ETHICAL RISKS $=>-$ MITIGATION ACTION$=>-$ INDICATIVE PROMPTS $($LLM$-$BASED FEEDBACK$)=>$ PHASE $2$ specific info $3.$ Input from User #$2-$ customize the prompt library $=>$ with additional input $($text$.)=>$ customized prompts$/$suggested prompts$-$ non$-$proprietary info about the AI System $-2.$ TAI Prompt Library Risks Identification & Categorization:o Sources: Gather risks from diverse papers and standards $($e$.$g$.,$ EU AI Act, US NIST frameworks, HLEG guidelines$).$o Frameworks: Align with existing regulatory frameworks $($e$.$g$.,$ GDPR$,$ DSA, DMA$).$ Keyword Identification: Develop a set of keywords associated with identified risks, facilitating prompt generation. Prompt Classification:o General Prompts: Broad prompts applicable across multiple scenarios.o Adversarial Prompts: Designed to test the AI systems resilience against attacks.o Targeted Prompts: Specific prompts focused on identified weaknesses or concerns. Prompt Validation:o Literature$-$Validated Prompts: Incorporate prompts that have been validated in academic or industry research.o Ongoing Validation: Continuously update and refine prompts based on new research and feedback. $3.$ TAI Assessment Process Feedback Mechanism:o Personalized Prompts: The system generates a set of prompts tailored to the user's specific needs and input data. TAI Measurement Criteria:o Metrics Definition: Define measurable criteria that align TAI principles with concrete metrics $($e$.$g$.,$ accuracy, fairness$).$o Regular Updates: Frequently update these criteria to adapt to new standards and research.o Risk$-$Linked Metrics: Establish a direct link between risks and metrics, enabling quantifiable assessments. $4.$ Usage Internal Evaluation:o Prompt Response Evaluation: Users assess their AI systems based on the generated prompts.o Comparison & Evaluation: Compare and evaluate multiple LLMs or AI systems to gauge overall trustworthiness. Output Evaluation:o Trustworthiness Analysis: Evaluate AI outputs based on the TAI criteria, identifying areas of strength and vulnerability. $5.$ Reporting Summary of Risks:o Risk Identification: Provide a comprehensive summary of identified risks.o Potential Improvement Areas: Highlight areas where the AI system can be improved to better align with TAI principles. TAI Lifecycle Alignment:o Alignment Reporting: Ensure that the AI systems development and deployment stages align with TAI principles throughout the lifecycle. Mitigation Strategies:o Actionable Insights: Provide specific, actionable recommendations for mitigating identified risks. Visual Scheme Representation $($Overview$)$ Phase A:$1.$ Identify TAI Risks: Use relevant papers, guidelines $($e$.$g$.,$ HLEG, NIST$)$ to identify risks$\mathrm{.}2.$ Categorize Risks: Align identified risks with TAI principles$\mathrm{.}3.$ Define Prompts: Create prompts that reflect these risks for user assessment. Phase B:$1.$ Align with Frameworks: Ensure that identified risks and associated prompts align with regulatory frameworks $($e$.$g$.,$ AI Act, GDPR$)\mathrm{.}2.$ Utilize TAI Prompt Library: Leverage the prompt library to evaluate AI systems and identify risk areas. Legal, Ethical, Robust Framework EU:o Legal: Incorporate regulatory frameworks like the AI Act, GDPR$,$ DSA $($Digital Services Act$),$ DMA $($Digital Markets Act$),$ Cybersecurity Act.o Ethical: Reference ethical guidelines from bodies like HLEG $($High$-$Level Expert Group on AI$)$ and the Council of Europe.o Robust: Consider robustness standards such as those from NIST $($National Institute of Standards and Technology$)$ and other EU$-$specific frameworks $($e$.$g$.,$ ENISA for cybersecurity$).$ Other Countries:o Legal: Include relevant country$-$specific laws and regulations.o Ethical: Use guidelines from organizations like the OECD and UNESCO.o Robust: Implement frameworks like the NIST Risk Management Framework for assessing robustness.Flowchart: Trustworthy AI $($TAI$)$ Self$-$Assessment Tool Process$[$Inputs from User$]-$ AI System Lifecycle Phase$-$ TAI Principle for Testing$-$ Domain of Application$-$ Type of Input Data $($Text$,$ Image, etc.$)-$ Type of AI System $($e$.$g$.,$ Generative AI$)+--------------------------------------+|$ TAI Prompt Library $||-$ Risks Identification & Categorization $||-$ Sources $($Papers$,$ Regulations$)||-$ Keyword Identification for Risks $||-$ Prompt Classification $||-$ General Prompts $||-$ Adversarial Prompts $||-$ Targeted Prompts $||-$ Prompt Validation $||-$ Literature$-$Validated Prompts $||-$ Ongoing Validation $|+--------------------------------------+[$TAI Assessment Process$]-$ Personalized Feedback Mechanism$-$ Set of Prompts Tailored to User Needs$-$ TAI Measurement Criteria $-$ Metrics Definition $-$ Regular Updates $-$ Risk$-$Linked Metrics $[$Usage$]-$ Internal Evaluation$-$ Comparison Across Multiple LLMs$-$ Output Evaluation $-$ Trustworthiness Analysis $[$Reporting$]-$ Summary of Identified Risks$-$ Potential Improvement Areas$-$ TAI Lifecycle Alignment$-$ Mitigation Strategies Do the fuctionalliry of the tool how can intergrate rags