Impact of AI and Big Data on Business and Society

Últimos artículos

• Concolic Testing on Individual Fairness of Neural Network Models

  Ming-I Huang, Chih-Duo Hong, Fang Yu · 4 de mayo de 2026

  This paper introduces PyFair, a formal framework for evaluating and verifying individual fairness of Deep Neural Networks (DNNs). By adapting the concolic testing tool PyCT, we generate fairness-specific path constraints to systematically explore DNN behaviors. Our key innovation is a dual network a…

• Who Benefits from AI? Self-Selection, Skill Gap, and the Hidden Costs of AI Feedback

  Christoph Riedl, Eric Bogert · 22 de abril de 2026

  Feedback from artificial intelligence (AI) is increasingly easy to access and research has already established that people learn from it. But individuals choose when and how to seek such feedback, and more engaged and motivated individuals may seek it more, creating an illusion of effectiveness that…

• From Vision to Validation: A Theory- and Data-Driven Construction of a GCC-Specific AI Adoption Index

  Mohammad Rashed Albous, Abdel Latef Anouze · 1 de diciembre de 2025

  Artificial intelligence (AI) is rapidly transforming public-sector processes worldwide, yet standardized measures rarely address the unique drivers, governance models, and cultural nuances of the Gulf Cooperation Council (GCC) countries. This study employs a theory-driven foundation derived from an …

• Achieving Fairness with a Simple Ridge Penalty

  Marco Scutari, Francesca Panero, Manuel Proissl · 18 de noviembre de 2025

  In this paper we present a general framework for estimating regression models subject to a user-defined level of fairness. We enforce fairness as a model selection step in which we choose the value of a ridge penalty to control the effect of sensitive attributes. We then estimate the parameters of t…