Initiatives to implement AI technologies in organizations fail at an alarming rate. We argue that leading the adoption of AI is not a simple engineering exercise but rather represents a behavioral exercise where change management principles—the process by which organizations plan, implement, and embed changes in practices—are employed. However, many AI initiatives in business focus predominantly on the AI systems themselves, assuming humans will fall in line. To solve this, we integrate ideas from change management with scholarship on human-centered artificial intelligence to offer a behavioral approach that accounts for the impact of AI adoption on humans at all stages of implementation and change management (design, adoption, and management). We argue that this approach is necessary to curtail the staggeringly high failure rate of AI adoption initiatives and ensure the successful long-term integration of AI in organizations.

Artificial intelligence (AI) systems are being increasingly adopted by society, governments, and organizations in various decision-making contexts. For example, organizations use AI systems to deci...

A key insight from research on organizational justice is that fairness is in the eye of the beholder. With increasing discussions – especially among computer scientists and policymakers – about the potential biases and unfairness of decisions made by Artificial Intelligence (AI) systems, there is a critical need to consider how decision-subjects perceive the fairness of AI-led decision-making. Drawing upon theoretical and empirical perspectives on perceived fairness in organizational justice scholarship, this review categorizes and analyzes perceptions of AI fairness as they impact the effective implementation of AI in workplaces and beyond. Specifically, we review existing empirical research on AI fairness according to distinct dimensions of perceived fairness – distributive, procedural, interpersonal, and informational – with a focus on its potential to inform organizational decision-making. In doing so, we provide new insights and offer directions for future interdisciplinary research in this burgeoning field.

Organizational decision-makers often need to make difficult decisions.One popular way today is to improve those decisions by using information and recommendations provided by data-driven algorithms (i.e., AI advisors).Advice is especially important when decisions involve conflicts of interests, such as ethical dilemmas.A defining characteristic of ethical decision-making is that it often involves a thought process of exploring and imagining what would, could, and should happen under alternative conditions (i.e., what-if scenarios).Such imaginative "counterfactual thinking," however, is not explored by AI advisors -unless they are pre-programmed to do so.Drawing on Fairness Theory, we identify key counterfactual scenarios programmers can incorporate in the code of AI advisors to improve fairness perceptions.We conducted an experimental study to test our predictions, and the results showed that explanations that include counterfactual scenarios were perceived as fairer by recipients.Taken together, we believe that counterfactual modelling will improve ethical decision-making by actively modelling what-if scenarios valued by recipients.We further discuss benefits of counterfactual modelling, such as inspiring decision-makers to engage in counterfactual thinking within their own decision-making process.