PhD Trial Lecture and Public Defence: Reda Hemdan Mahmoud Hassan

PhD Trial Lecture and Public Defence: Reda Hemdan Mahmoud Hassan
Reda Hemdan Mahmoud Hassan will defend his thesis for the degree of PhD in Business on friday, May 30, 2025.
Reda Hemdan Mahmoud Hassan

Title of PhD thesis:

Complementarity, Judgment, and Responsibility In Human-AI Interaction

Title of trial lecture:

GenAI and new business models opportunities 

Trial lecture: 10.00
Public defence: 12.00

Assessment committee:

Professor Paavo Ritala, LUT University, Finland
Professor Patrick Mikalef, NTNU
Associate Professor Dolores Modic, Nord University Business School (coordinator) 

Supervisors:  

Main supervisor: Professor Sujith Nair, BI/Nord University Business School
Co-supervisor:  Associate Professor Karin Wigger, Linköpings University/ Nord University Business School

The trial lecture and defense are open to all interested attendees. The dissertation is available upon request by contacting Grete Knudsen, email: grete.i.knudsen@nord.no, Tel. +47 75 51 76 77.

Key Insights from the dissertation

As artificial intelligence (AI) becomes increasingly integrated into organizations, it is evolving from a tool for automation to a partner in innovation and decision-making. This dissertation examines the complementarity between humans and AI in two key areas: innovation search and judgment formation. It explores how AI interacts with humans to enhance problem-solving, and how organizations manage the challenges related to responsible AI development (RAI).

The dissertation argues that innovation arises through continuous knowledge exchange between humans and AI – an interactive process that changes how organizations explore and develop new solutions. In high-risk domains such as healthcare, the dissertation investigates how AI predictions can support expert assessments. It also emphasizes the importance of designing AI-human interactions that preserve the central role of humans and build trust through explainability.

The dissertation also contributes to research on responsible AI by moving from abstract ethical ideals to practical implementation. It shows how organizations define, adapt, and apply RAI practices to ensure that AI systems are transparent, fair, and human-centered.

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