Explainable AI report: transparency, audits and accountability
Source-linked evidence on explainable AI, transparency, audits, model documentation and disclosure rules, updated daily. Coverage counts are signals of attention—not measures of importance, harm or consensus.
Prepared by the ethics.ai evidence desk · automatically refreshed · editorial scope reviewed against the methodology and corrections policy
Tracks interpretability research, explanations, audit access, model and system documentation, and disclosure law. It does not assume that an explanation is faithful or useful without supporting evidence.
Questions to take into the evidence
Which explanation methods are being evaluated with users?
What information do auditors and affected people receive?
Once upon a time, “Severance” star Adam Scott auditioned for the beloved sitcom “The Office.” Years later, he’s reliving the memories of that failed audition after his daughter’s acting teacher had the class compare Scott’s audition tape to John Krasinski’s successful tape. “He put it to the class like, ‘Okay. Let’s discuss why he got the job, […]
In an audit, the GSA’s Office of the Inspector General said the Federal Acquisition Service’s product catalog and data reporting system had varying names and part numbers for the same item. The post GSA acquisition product pricing data flawed, may cause agencies to overpay, watchdog says appeared first on FedScoop .
Darüber, wie bei X entschieden wird, welche Beiträge in den algorithmischen Timelines landen, wurde viel spekuliert. Nun soll man das selbst prüfen können.
arXiv:2608.12320v1 Announce Type: new Abstract: This article reviews and updates the framework for accountability in AI based on account- ability ecosystems. We update the framework in light of the latest developments since the release of Large Language Models for general public use. We propose three interlinked updates to the original AI accountability ecosystem: (i) reorienting the accountability ecosystem to AI infrastructure and supply chains, (ii) providing greater emphasis on outcomes moni
arXiv:2608.13022v1 Announce Type: new Abstract: Algorithmic fairness evaluation commonly assesses AI systems as bounded technical components, abstracting away the organizational context in which they operate. We present, to our knowledge, the first independent end-to-end fairness audit of a semi-automated hiring system operated by Barcelona Activa, a public employment agency using the third-party TalentClue platform for candidate search and shortlisting. We analyze approximately 497,000 candidat
Large language models have become central infrastructures of contemporary digital economies while raising persistent ethical concerns regarding linguistic inequality, opacity, data governance, and the concentration of technological power. Much of the current debate on AI ethics focuses on normative principles such as fairness, transparency, and accountability. While these principles remain essential, they often do not sufficiently explain why ethically problematic outcomes persist under competit
Explainable Artificial Intelligence (XAI) has emerged as a response to the need to understand and make transparent the decisions of machine learning models, particularly in sensitive contexts such as accounting and financial auditing. In this domain, XAI enables the interpretation of results generated by automated systems applied to fraud detection, risk management, financial analysis, and regulatory compliance, thereby strengthening the trust of auditors and regulators. The objective of this st
This report is assembled automatically from source metadata and keyword classifications. It summarizes what the tracked source fleet published; it does not independently validate every linked claim. Source-fleet growth can inflate historical comparisons. Cite the individual evidence record and original publisher for substantive claims.
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