Conveners
Data FAIR in Science
- Cosima Rughinis (University of Bucharest)
The digitalization of nutrition has radically transformed how individuals monitor their dietary intake but has also introduced invisible algorithmic risks. This study investigates the algorithmic behavior of MyFitnessPal and Cronometer in response to simulated nutritional scenarios, using user profiles that display energy or micronutrient vulnerabilities. Based on an exploratory-comparative...
We present a computational study of gender bias in four state-of-the-art large language models (LLMs), namely ChatGPT, Gemini, Le Chat, and DeepSeek, using a novel prompt-based framework to evaluate model responses to moral guidance questions. Our methodology integrates structured prompt variation, content analysis, sentiment scoring, subjectivity detection, part-of-speech tagging, and n-gram...
This study looks into parental digital mediation strategies through a qualitative thematic discourse analysis of 68 Reddit comments from r/Parenting. Using Nikken and Jansz's (2014) mediation framework and Furedi's (2002) concept of paranoid parenting, we explore both parental strategies and underlying anxieties. Empirical evidence shows that restrictive mediation dominates (46 out of 59...
This article investigates how young participants in the Innovation Labs Hackathon, Romania' largest national educational pre-accelerator program, define the concept of innovation at the entry point into an entrepreneurial support structure. Drawing on Bourdieu’s theory of fields and forms of capital, and the concept of boundary objects, we argue that the Innovation Labs program functions as a...
Artificial intelligence (AI) adoption among startups is accelerating, yet systematic understanding of how founders approach AI-related risks, justify implementation decisions, and establish governance frameworks remains limited. This study examines how startup founders interpret AI risks and opportunities, develop justification strategies for adoption, and negotiate boundaries of...
Algorithmic decision-making in recruitment offers substantial efficiency gains but raises persistent concerns about embedded bias, opacity, and fairness. This study outlines a framework for bias detection and mitigation in AI-driven hiring systems, combining technical and procedural strategies for transparency and accountability. We first review established methods such as explainability...
The rapid advancement of artificial intelligence and its widespread integration into everyday platforms have transformed large-scale digital systems into socio-technical infrastructures that shape public discourse and individual decision-making. As more users rely on algorithmically curated content—often controlled by opaque, transnational corporations—concerns about manipulation, bias, and...
