arXiv subjects

Gemma Galdon Clavell

Publications and source records attributed to Gemma Galdon Clavell.

4 recordsLinked to original sources

arXiv2026

The Eticas AI Risk Taxonomy: Open Infrastructure for Operationalizing AI Audits

The rapid deployment of AI systems across high-stakes domains has created urgent demand for standardized evaluation, yet the field remains fragmented across competing risk taxonomies that catalog risks without showing how an audit is executed. At least 74 AI risk taxonomies exist, and almost all stop at the catalog. The hard part of auditing is not naming a risk but operationalizing it: turning it into a test run against a real system, a measured value, a calibrated severity, and a defensible grade. This paper leads with that bridge. We present the operationalization layer Eticas has built and run, shown end to end on a single risk (PII leakage) against a public benchmark, and then the open taxonomy that makes the method scale. On GPT-4-0314, a disclosure risk that seven external frameworks require be controlled is measured at 0%, 51%, and 84% disclosure as adversarial conditioning increases, mapping through calibrated severity bands to a subcategory grade of E with a SYSTEMIC pattern. Around this example, the Eticas AI Risk Taxonomy v3.0.0 organizes 70 active subcategories across 10 categories and 21 sub-groups, with mappings to 18 external frameworks across compliance, reference, and academic tiers. Its established layer - categories, sub-groups, and the 32 established subcategories - is published under CC BY 4.0 as open semantic infrastructure with stable URIs and SKOS/JSON-LD distributions, and a worked subcategory example shows the operational layer down to its severity thresholds. The contribution is the demonstrated bridge from concept to graded finding, anchored by a clean separation of risks from the mechanisms by which they surface, and framed by an open-core model in which the conceptual scaffold is open and the methodology calibration is the practitioner layer. This is the infrastructure the AI auditing field needs: shared, open, and demonstrably operable.

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arXiv2026

Open Veins of Algorithmic Auditing: Why AI Assessment Lags Behind Its Deployment in the Global South

Artificial intelligence is being deployed across the Global South at a pace matching or exceeding the Global North, yet AI governance has not kept pace, and the gap is far wider in the South. Drawing on a decade of AI audit practice across Latin America, Sub-Saharan Africa, and Asia Pacific (the only fully published second-party audit of a deployed system in the region, Robot Laura in Brazil; two completed but unreleased national audits, of a child-welfare risk model and a public-employment matching algorithm; thirteen Responsible AI Assessments; and a regional landscape analysis), this paper documents patterns in the sparse field of Global South AI evaluation and explains why it so rarely occurs. We count fewer than twenty published second- and third-party audits of deployed systems across the region over the past decade, against hundreds of documented public-sector algorithms and multibillion-dollar national AI investments. We find four cross-cutting patterns: proxy targets that substitute predictability for validity, performance claims that collapse under prevalence analysis, populations scored by models that never saw them in training, and structural bias that persists even after protected attributes are removed. We then draw five lessons for evaluation, regulation, and funding that emerge outside the regulatory, linguistic, and data conditions of the North. We argue the gap is not, at root, a capacity problem but a funding problem: capacity follows funded demand, and no actor is currently required, or funded, to hold deployed systems to account. The actor best placed to close it is the small number of development and philanthropic funders behind most consequential AI in the region, whose funding conditions can require independent evaluation where no regulator yet does. We close with recommendations for funders, governments, and the design of evaluation itself.

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arXiv2025

Demographic Benchmarking: Bridging Socio-Technical Gaps in Bias Detection

Artificial intelligence (AI) models are increasingly autonomous in decision-making, making pursuing responsible AI more critical than ever. Responsible AI (RAI) is defined by its commitment to transparency, privacy, safety, inclusiveness, and fairness. But while the principles of RAI are clear and shared, RAI practices and auditing mechanisms are still incipient. A key challenge is establishing metrics and benchmarks that define performance goals aligned with RAI principles. This paper describes how the ITACA AI auditing platform developed by Eticas.ai tackles demographic benchmarking when auditing AI recommender systems. To this end, we describe a Demographic Benchmarking Framework designed to measure the populations potentially impacted by specific AI models. The framework serves us as auditors as it allows us to not just measure but establish acceptability ranges for specific performance indicators, which we share with the developers of the systems we audit so they can build balanced training datasets and measure and monitor fairness throughout the AI lifecycle. It is also a valuable resource for policymakers in drafting effective and enforceable regulations. Our approach integrates socio-demographic insights directly into AI systems, reducing bias and improving overall performance. The main contributions of this study include:1. Defining control datasets tailored to specific demographics so they can be used in model training; 2. Comparing the overall population with those impacted by the deployed model to identify discrepancies and account for structural bias; and 3. Quantifying drift in different scenarios continuously and as a post-market monitoring mechanism.

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arXiv2024

What we learned while automating bias detection in AI hiring systems for compliance with NYC Local Law 144

Since July 5, 2023, New York City's Local Law 144 requires employers to conduct independent bias audits for any automated employment decision tools (AEDTs) used in hiring processes. The law outlines a minimum set of bias tests that AI developers and implementers must perform to ensure compliance. Over the past few months, we have collected and analyzed audits conducted under this law, identified best practices, and developed a software tool to streamline employer compliance. Our tool, ITACA_144, tailors our broader bias auditing framework to meet the specific requirements of Local Law 144. While automating these legal mandates, we identified several critical challenges that merit attention to ensure AI bias regulations and audit methodologies are both effective and practical. This document presents the insights gained from automating compliance with NYC Local Law 144. It aims to support other cities and states in crafting similar legislation while addressing the limitations of the NYC framework. The discussion focuses on key areas including data requirements, demographic inclusiveness, impact ratios, effective bias, metrics, and data reliability.

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