Initiative ETHICAL PUBLIC ROBOTS AND AI (EPURAI)
A. Ozkes (SKEMA Business School)
EPURAI is a research initiative that aims to bring together perspectives on how citizens can participate in the design and policymaking of automated services. The goal is to study public oversight and adoption regarding these systems. The focus is on ethical perspectives vis-à-vis rules and regulations for deploying intelligent systems and robots in public and commercial services. Theoretical contributions come from social choice theory, game theory, and decision theory, while empirical approaches include laboratory experiments, field experiments, agent-based modeling, and mass surveys. Workshops, seminars, and collaborations with researchers and practitioners from various fields, including computer science, economics, and psychology, are organized to facilitate discussions. Specific topics include ethics, human-AI interactions, transparency, fairness, autonomy, job displacement, equity, bias, privacy, and co-engineering processes. Our efforts are financed through project funding by ANR (for EPURAI – Ethical Public Robots and AI) and CNRS (for MOSCAI – Morality, AI, and Social Choice) in France and receives institutional support from SKEMA Business School, Université Paris-Dauphine, University of Cincinnati, and WU Vienna.
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A review-based recommender system: Recommending products according to the helpfulness and positive sentiment of reviews
M. Haikel-Elsabeh (Institut Mines-Télécom Business School), Maria Olmedilla Fernandez (SKEMA Business School), J.C Romero (Université Paris Dauphine-PSL)
We propose a recommender system adapted to eWOM communities that provides recommendations to users based on the sentiment and the helpfulness that users express in their product online reviews. The goal of this work is to understand how to transform and apply machine learning algorithms to review data from eWOM communities so as to create an optimized recommender system.
AI and Human Resource Management
Yujie Cai (SKEMA Business School), Guoyang Zheng (Peking University)
We examine the impact of AI on the people management functions in modern organisations. Based on platform enterprises and other firms that adopt AI techniques, we explore the impact of AI characteristics (e.g., intensity, access, and transparency) on the human behaviours within various teams in modern organisations. The people elements (e.g., personal traits, individual behaviours), management practices, leadership behaviours and organisational climate have been included in the framework. This research has implications on the Human-Computer Interaction and the humane implementation of the AI systems.
When Information Disclosure is Required by Algorithms: Presence-Privacy Tradeoff in GeoMatching Applications
Zhenzhen Zhao (SKEMA Business School)
Using geolocation and user profile information, GeoMatching applications make connections between two individuals. To make connections possible, users must disclose their information. Such information disclosure depends upon the algorithm that the app proposed. This research aims to investigate information disclosure from an algorithm perspective. We explain the relationship between the disclosure requirement and the user’s attitude toward using GeoMatching by introducing the presence-privacy trade-off.
A hybrid recommender system based on eWOM and social behaviour to improve users’ product preferences
Maria Olmedilla Fernandez (SKEMA Business School), M. Haikel-Elsabeh (Institut Mines-Télécom Business School), J.C Romero (Université Paris Dauphine-PSL)
Traditional recommender systems make use of the overall rating as an input to recommend items, which leads to the cold-start problem and data sparsity. The goal of this work is to reduce the undesirable outcomes caused by such problems and to optimize the predictive outcomes of the recommendations in the context of eWOM communities.
We propose a hybrid recommender system that combines Social and eWOM variables as input and uses the Kmeans algorithm for dimensionality reduction and the collaborative filtering SVD++ algorithm to optimize the accuracy of recommendations.
Applying NLP techniques to characterize what makes an online review trustworthy
Maria Olmedilla Fernandez (SKEMA Business School), J.C Romero (Université Paris Dauphine-PSL), M.R. Martínez-Torres (Universidad de Sevilla), S. Toral (Universidad de Sevilla)
Trustworthiness of online reviews is a key aspect not only for the users that want to make more informed decisions regarding the products but also for the websites whose credibility might be affected.
This work proposes a classification system using two Natural Language Processing (NLP) models that can predict helpful and truthful online reviews. After using a keyword extractor model we can also characterize their most important features.
Expressions of Public Values in AI Patenting
Philip Shapira (University of Manchester and Georgia Institute of Technology), Barbara Ribeiro (SKEMA Business School) and Sergio Pelaez (Georgia Institute of Technology)
AI patent applications and awards have burgeoned in recent years as inventors and assignees seek public acknowledgement of proprietary intellectual property rights for AI applications. Public concerns about the consequences of AI have also risen. This research is developing a method to identify how public values are expressed in AI patents and to assess developments and implications.
Consumer Psychology on AI
Alican Mecit (SKEMA Business School) and Margherita Pagani (SKEMA Business School)Consumer psychology on AI focuses on how individuals react to, interact with, and think about algorithms and AI-powered tools in the marketplace. Research in the area studies diverse topics such as mind perception, agency, and trust that are based on the cognitive and affective impressions of consumers related to AI and other automated tools.
Enhanced Creativity using Artificial Intelligence
Margherita Pagani (SKEMA Business School) and Yoram (Jerry) Wind (The Wharton School of the University of Pennsylvania)Despite the growing adoption of Artificial Intelligence (AI) systems in business processes, how AI systems impact managers’ creativity is not yet well understood and measured. Using a mix-method approach we explore the influence of AI systems on human creativity. and identify an interpretative conceptual framework of the impact of AI on human creativity.
The Augmented Entrepreneur? The Influence of Artificial Intelligence on Human Entrepreneurial Creativity
Nathan Sorin (SKEMA Business School) and Margherita Pagani (SKEMA Business School)
With the recent progress in AI research, and in particular natural language processing (NLP), creativity scholars have revisited the assumption that machines are incapable of being creative. As creative AI systems tend to be used by practitioners to augment rather than automate creative tasks, our research aims to provide a theoretical understanding of the phenomenon as well as practical guidelines for creators by investigating how creative AI influences idea generation in an entrepreneurial context.