Search arXiv⌕ Search

arXiv · 2610.32576

From Regulation to Engineering: Organizational Privacy Climate as Mediator

Abstract

Data protection legislation aims to protect privacy rights by regulating how information systems store and process personal information. While such legislation defines legal obligations, translating regulatory requirements into engineering practice is far from straightforward. As new frameworks such as the EU AI Act emerge, understanding these mechanisms becomes increasingly relevant. To investigate them, we conducted an online survey of 156 U.S. information system developers, examining the role of organizational privacy climate in the association between developers' perceptions of the GDPR and their privacy engineering practices. Using structural equation modeling, we found that organizational privacy climate mediates the relation between developers' perceived GDPR importance and their privacy attitudes, which in turn are associated with their privacy engineering practices. The mediation chain - regulation, climate, attitudes, practices - complements the direct effect of regulation on practice, revealing the organizational and attitudinal pathway through which regulatory intent reaches engineering behavior. We discuss implications for the next generation of privacy and AI regulation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Oshrat Ayalon, Eran Toch, Irit Hadar, Michael Birnhack. 2026-10-23. From Regulation to Engineering: Organizational Privacy Climate as Mediator. https://arxiv.org/abs/2610.32576

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Equity Bias: An Ethical Framework for AI Design

Equity Bias is a philosophical and practical framework for building more equitable AI systems. It treats AI as an interpretive agent that constructs meaning and knowledge rather than as a neutral calculator. Grounded in Gadamerian hermeneutics and epistemic injustice theory, Equity Bias views bias not as an error to eliminate but as a reflection of whose knowledge is encoded into systems. Since all knowledge is situated, neutral systems are not possible. While traditional approaches aim to reduce or remove bias, Equity Bias instead makes bias transparent and contestable. Its three-stage AI Life Cycle (Equity Archaeology, Co-Creating Meaning, and Ongoing Accountability) turns these principles into practice. Equity Bias gives developers, researchers, policymakers, and communities affected by AI a method for examining whose knowledge a system encodes and who has the power to contest it. By placing the politics of knowledge at the centre of equitable AI design, Equity Bias offers a distinct approach to AI ethics.

cs.CY↗

Proceedings of the 2nd International Workshop on Low Carbon Computing (LOCO 2026)

This volume contains the proceedings of the 2nd International Workshop on Low Carbon Computing (LOCO 2026), held at Lancaster University, United Kingdom, on 10-11 September 2026. LOCO provides an interdisciplinary forum for research, practical tools, early-stage work, radical ideas, and critical perspectives addressing the reduction of greenhouse gas emissions associated with computing. The proceedings cover topics including carbon measurement and reporting, sustainable software engineering, energy-efficient computing, carbon-aware systems, hardware longevity, embodied carbon, circular computing, resource management, frugal and sufficiency-oriented computing, sustainable artificial intelligence, scientific computing, and the wider environmental effects of digital technologies. Full workshop papers were evaluated through a non-blind peer-review process by members of the LOCO 2026 Programme Committee. Submissions were assessed for originality, relevance to the workshop, quality, and potential to stimulate constructive discussion. The accepted lightning-talk contributions are published separately as a companion volume, LOCO 2026 Lightning Talk Abstracts: 2nd International Workshop on Low Carbon Computing (arXiv:2609.13355).

cs.CY↗

GenAIT: Development and Validation of an Objective Generative AI Literacy Test for High School Students

There is growing international interest in generative AI (GenAI) literacy and its assessment among high school students, but objective assessment in this population remains underdeveloped. This article reports the iterative development and validation of the GenAI Literacy Test (GenAIT), an 18-item multiple-choice test measuring high school students' conceptual knowledge about GenAI, with content spanning technical, practical, and human-impact domains. Expert review of relevance, clarity, and comprehensiveness provided evidence of content validity. In a large-scale survey of 7432 Estonian high school students, we evaluated the psychometric functioning of the Estonian-language GenAIT using confirmatory factor analysis, classical test theory, and item response theory. Results supported approximate unidimensionality, broadly adequate reliability for group-level research (marginal reliability = .72, KR-20 = .69), and good fit of a three-parameter logistic model (RMSEA = .013, TLI = .987, CFI = .990, SRMSR = .021). Measurement precision was sufficient for the majority of students but varied substantially across the latent trait, with lower precision for lower scoring students. GenAIT is therefore more suitable for group-level research than high-stakes individual classification. GenAIT scores were unrelated to perceived usefulness and perceived ease of use, and negatively associated with LLM use frequency, suggesting that frequent use and favorable perceptions of AI should not be treated as proxies for conceptual understanding.

cs.CY↗