--- title: "What is explainable AI (XAI)? Meaning in hiring | Findem" description: "Explainable AI shows the evidence and reasons behind an AI output. Learn what it means in hiring, why regulators care, and how Findem applies it." url: "https://www.findem.ai/glossary/explainable-ai" --- # What is explainable AI? ← Back to glossary AI and people intelligence Explainable AI (XAI) is AI that shows the evidence and reasons behind its outputs, in terms the person using it can understand. In hiring, that means a recruiter can see why a candidate was recommended or ranked, check that reasoning against the record, and decide whether to act on it. ## How explainable AI is defined NIST's [Four Principles of Explainable Artificial Intelligence](https://doi.org/10.6028/NIST.IR.8312) describes four properties of an explainable AI system: - **Explanation:** The system delivers or contains evidence or reasons for its outputs and processes. - **Meaningful:** The explanation is understandable to the people it is meant for. - **Explanation accuracy:** The explanation correctly reflects how the system produced the output. - **Knowledge limits:** The system operates only under the conditions it was designed for, and only when it reaches sufficient confidence in its output. The [NIST AI Risk Management Framework](https://airc.nist.gov/airmf-resources/airmf/3-sec-characteristics/) lists "explainable and interpretable" among the characteristics of trustworthy AI. It also draws a useful line between three related ideas. Transparency answers what happened in a system. Explainability answers how a decision was made. Interpretability answers why, and what the output means to the person using it. ## Why explainable AI matters for talent teams Hiring, promotion, and mobility decisions shape people's careers. A recommendation no one can explain is hard to trust and harder to defend. Explainability changes how teams work with AI: - Recruiters and hiring managers can check a recommendation against the evidence before they act on it, instead of accepting or ignoring a score. - Teams can document why a candidate advanced, which supports audits and helps answer candidates' questions. - Leaders can judge whether the AI is producing the value they expect. Regulation is moving in the same direction. The EU AI Act lists AI systems used for the recruitment or selection of people, including systems that analyze and filter job applications and evaluate candidates, as high-risk ( [Annex III, point 4](https://ai-act-service-desk.ec.europa.eu/en/ai-act/annex-3) ). [Article 86](https://ai-act-service-desk.ec.europa.eu/en/ai-act/article-86) gives a person affected by a significant decision based on a high-risk system's output the right to clear and meaningful explanations of the AI system's role and the main elements of the decision. US rules vary by state and city; [AI hiring laws in 2027](https://www.findem.ai/blog/ai-hiring-laws-2027) covers recent requirements. This page is a general overview, not legal advice. ## How Findem approaches explainable AI Findem builds AI that people can inspect. Users can see what information shaped each recommendation, and a person reviews the results before they influence a hiring decision. Findem's AI does not reject candidates or make hiring decisions. That starts with the data behind each recommendation. [Success Signals](https://www.findem.ai/why-findem/success-signals) are verified patterns in a person's background, behavior, and impact, labeled by recruiters, search professionals, and people leaders. They are structured, explainable patterns, not black-box scores, so teams can see what a recommendation rests on. Findem applies 75 to 100 Success Signals to each profile. Governance covers the rest. Findem's responsible AI program is built on the NIST AI Risk Management Framework. For each AI use case in the platform, Findem documents model selection, system boundaries, and known limitations, and it discloses its use of commercial foundation models from OpenAI, Anthropic, and Google. Findem also publishes a summary of its most recent [independent bias audit by Warden AI](https://www.findem.ai/blog/findem-ai-bias-audit-warden-ai). ## Further reading - [From the black box to the boardroom](https://www.findem.ai/blog/ai-transparency-black-box-to-boardroom) - [AI governance in HR: A practical guide](https://www.findem.ai/blog/ai-governance-in-hr-guide) ## Related terms - [Success Signals](https://www.findem.ai/glossary#term-success-signals) - [Assistive AI](https://www.findem.ai/glossary#term-assistive-ai) - [Agentic AI](https://www.findem.ai/glossary#term-agentic-ai) - [Domain-specific AI for talent](https://www.findem.ai/glossary#term-domain-specific-ai-for-talent) ## Sources - [Four Principles of Explainable Artificial Intelligence (NISTIR 8312)](https://doi.org/10.6028/NIST.IR.8312) NIST, September 2021 - [AI Risk Management Framework: AI risks and trustworthiness](https://airc.nist.gov/airmf-resources/airmf/3-sec-characteristics/) NIST AI Resource Center, section 3 - [EU AI Act, Annex III: High-risk AI systems](https://ai-act-service-desk.ec.europa.eu/en/ai-act/annex-3) European Commission AI Act Service Desk - [EU AI Act, Article 86: Right to explanation of individual decision-making](https://ai-act-service-desk.ec.europa.eu/en/ai-act/article-86) European Commission AI Act Service Desk - [Responsible AI at Findem](https://www.findem.ai/why-findem/responsible-ai) - [Success Signals](https://www.findem.ai/why-findem/success-signals) Responsible AI Discover how Findem promotes responsible use of AI to support fair, transparent, and inclusive hiring — designed to assist, not replace, human judgment. [See how Findem approaches responsible AI](https://www.findem.ai/why-findem/responsible-ai) [Request a demo](https://www.findem.ai/request-demo) ## Frequently asked questions. **What is the difference between explainable AI and AI transparency?** Transparency is about what happened in an AI system, such as what data it used and how it was configured. Explainability is about how the system reached a specific output. NIST's AI Risk Management Framework treats them as separate characteristics that support each other, and adds interpretability: what an output means in the user's context. **Why does explainable AI matter in hiring?** AI that ranks or filters candidates can shape who gets considered for a job. Explanations let recruiters check recommendations against the evidence, document why a candidate advanced, and spot errors before they affect a decision. **Is explainable AI required by law?** Some rules point that way. The EU AI Act treats AI used for recruitment or selection as high-risk and gives affected people a right to clear and meaningful explanations of the AI system's role in certain decisions. Requirements differ by jurisdiction, so check with counsel for your situation. **Does Findem's AI make hiring decisions?** No. Findem's AI does not reject candidates or make hiring decisions. It prepares and explains recommendations, and a person reviews the results before they influence a hiring decision. **How does Findem make its recommendations explainable?** Users can see what information shaped each recommendation. Recommendations draw on Success Signals, which are structured, expert-labeled patterns rather than black-box scores. Findem also documents the model selection, system boundaries, and known limitations of each AI use case in its platform.