Wednesday Research Seminar Series - A Dynamic Simulation of Ethical vs. Non-Ethical AI Adoption: Balancing Regulation, Reputation, and Market Forces

We are pleased to invite you to this week's Wednesday Research Seminar. This week, we are delighted to host our colleague, Dr. Nishant Das. He will present his research work on "A Dynamic Simulation of Ethical vs. Non-Ethical AI Adoption: Balancing Regulation, Reputation, and Market Forces".

“A Dynamic Simulation of Ethical vs. Non-Ethical AI Adoption: Balancing Regulation, Reputation, and Market Forces”
Nishant Das

Abstract

AI governance is challenged by complex and rapidly evolving landscape where technology outpaces regulatory policies. Academic discourse on the topic remains largely qualitative, focusing on broad AI policy frameworks and leaving critical empirical insights into how diverse stakeholders and incentives truly interact underexplored. This paper offers a simulation-based approach to examine how policy interventions may shape the ethical vs.\ non‐ethical trajectories of AI adoption. This paper presents a dynamic diffusion‐of‐innovation model with ethical vs.\ non‐ethical adoption, capturing how regulatory policies, reputational effects, and first‐mover advantages shape AI markets. While ethical AI incurs higher compliance costs, it may yield long‐term reputational gains; conversely, non‐ethical adopters benefit from near‐term cost savings and faster deployment, risking monopolistic lock‐in. By endogenizing budgets, reputational risks \& rewards, and regulatory feedback, the model reveals threshold effects: excessive regulation can stifle innovation, whereas lax enforcement fosters unethical behavior that undermines market trust. Scenario analyses (e.g., varying enforcement levels, tax incentives, and reputational penalties) identify conditions that allow ethical AI to flourish without crippling technological progress. Sensitivity tests show how small parameter shifts—such as higher compliance subsidies or reputational damage—reshape market trajectories. This unified, simulation‐based framework offers an actionable lens for policymakers seeking to balance AI innovation with mitigating its potential harms.

Presenter Bios

Dr. Nishant Das specialises in applying Data Science to understand and enhance decision-making processes, particularly in management and policy contexts. His expertise extends to Natural Language Processing, with a focus on behavioural applications such as sentiment and emotion detection in text, and aligning AI cognition with human behaviour. His research interests also include algorithmic bias & fairness and AI ethics. He holds a degree in Engineering Physics, specializing in Nuclear Engineering, from McMaster University, Canada. After seven years as a Data Scientist and Management Consultant in Dubai, Dr. Das pursued a PhD in Behavioral Finance from IESE Business School, Barcelona, and was a visiting researcher at Haas School of Business, UC-Berkeley.  He is committed to conducting research that is practical and enhances contemporary management practices and has provided extensive machine learning consultancy, including the development of proprietary predictive algorithms for a range of private and public sector clients in the GCC region.