{
  "id": 285117,
  "title": "What “Explanation” Means to an Auditor",
  "url": "https://urgent.news/2026/08/07/what-explanation-means-to-an-auditor",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-07T23:44:35.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/multigrid/what-explanation-means-to-an-auditor-4ao6"
  },
  "original_language": "en",
  "account": "An auditor's explanation varies significantly from a researcher's perspective. For a researcher, the aim is to understand the model's computation, ideally in a way that makes it predictive of interventions. For a regulator, the focus is on a specific decision made about an individual, expressed in terms of the inputs that led to that decision.\n\nThe most common compliance mistake is providing what researchers deem an explanation, i.e., something like a circuit diagram or set of ablation results, when regulators require something entirely different. Regulators want plain language, the main factors that influenced the decision, and increasingly, information on what would have changed the outcome.\n\nThe General Data Protection Regulation (GDPR) doesn't explicitly grant a \"right to explanation,\" but Article 22 and the subsequent recitals highlight the importance of providing meaningful information about the logic involved in automated decisions that have legal or similarly significant effects. This includes the data used, their relative importance, and the consequences of the decision.\n\nMore recently, the EU AI Act has further clarified this requirement. It mandates two separate explanations: one for the deployer and another for the individual affected by a decision made by a high-risk system. The deployer explanation focuses on the documentations and design aspects of the system, while the individual explanation seeks clear and meaningful explanations of how the system contributed to the decision and the main factors involved.\n\nIn practice, this means that organizations need to maintain a decision record for each instance, including the inputs used, the model and its version, the output, the threshold or rule applied, the timestamp of the decision, and any human review. Additionally, they must provide reasons for the decision in a controlled vocabulary, ranked and expressed in plain language. In cases involving high-risk systems, providing counterfactual explanations can be an effective way to satisfy this requirement without disclosing the model itself.",
  "summary": "A researcher explaining a model wants a mechanism. A regulator wants something a specific person can act on: why this decision, about me, and what would have to change. Those are different artefacts, and the most common compliance mistake is producing the first when the obligation asks for the second. This page describes what the instruments say and how the requirement is shaped. It is not legal…",
  "key_points": [
    "Auditors' explanations differ from researchers', focusing on decision inputs.",
    "Regulators require plain language, main factors influencing decisions.",
    "GDPR and EU AI Act mandate explanations for automated decisions with legal effects."
  ],
  "editors_take": "The divergence in what constitutes an adequate explanation for auditors versus researchers highlights the need for organizations to tailor their transparency efforts to specific regulatory requirements and stakeholder needs.",
  "illustration": "https://urgent.news/ill/285117.png",
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}