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Introduction
Artificial Intelligence (AI) is transforming entrepreneurship by profoundly revolutionizing the way value is created, captured, and delivered within society. Since its role in letting business opportunities emerge within different industries, AI is reshaping academicians' and practitioners’ perspectives on its contribution to business processes. Indeed, AI technologies enhance opportunity recognition skills, product and service development, big data analysis, and organizational operations. By positively impacting different sides of human life, AI reshapes entrepreneurial processes, which mainly exist to improve it (Giuggioli and Pellegrini, 2023). AI broadens the landscape of emerging new pathways towards social, economic, and environmental innovation (Dwivedi et al., 2021). Nonetheless, AI also brings significant challenges. AI rapidly processes a huge amount of data to always provide new, tailored, and reliable content, but its decisions are not always fair. Indeed, a growing body of research and empirical evidence reveals that AI-generated outputs are often susceptible to systematic inaccuracies due to AI’s biases (Di Vaio et al., 2020; Nishant et al., 2024). Addressing these issues is fundamental since experts belonging to different contexts are increasingly relying on AI to make decisions and implement significant changes. AI biases can occur in the dataset, the algorithm, and the user interaction (Mehrabi et al., 2021). The biases’ negative outcomes differ in intensity, not only leading to wrong operational results, but also promoting prejudices towards specific communities of people underrepresented or misrepresented. Thus, the same technology that can boost societal value-creation can rapidly destroy it, perpetuate existing inequalities, and generate discrimination. For these reasons, practitioners and academicians are invited to work together to promote a responsible approach to AI. The urgency of changing the direction of the AI revolution towards more inclusive and valuable actions is expanding entrepreneurship horizons (Jeremiah, 2024). Future research could provide theoretically and practically useful insights by integrating knowledge on entrepreneurship and AI bias. This Special Issue considers the two faces of the relationship between entrepreneurship and AI biases. The first is related to the occurrence of AI biases within entrepreneurial settings. The second is related to the new entrepreneurial opportunities that the necessity to mitigate bias risks unlocks.
List of Topic Areas
- AI Bias and Entrepreneurial Processes
- AI Bias in Innovation Processes across industries (e.g. healthcare)
- Entrepreneurship and mitigation of AI Bias
- AI Bias, Entrepreneurial and Organizational Culture
- AI, Big Data Management and Business Opportunities
- Biased AI adoption in entrepreneurial setting
- AI bias and entrepreneurial decision-making
- AI and the future of entrepreneurship
- AI Bias, Entrepreneurship and Sustainable Society
- Methodologies for AI Bias management
Submission Information
Submissions are made using ScholarOne Manuscripts. Registration and access are available here:
Author guidelines must be strictly followed. Please see:
Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to “Please select the issue you are submitting to”.
Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.
Key Dates
Opening date for manuscript submissions: 1 July 2026
Closing date for manuscript submissions: 31 August 2026
References
Di Vaio, A., Palladino, R., Hassan, R., & Escobar, O. (2020). Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. Journal of Business Research, 121, 283-314.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International journal of information management, 57, 101994.
Giuggioli, G., & Pellegrini, M. M. (2023). Artificial intelligence as an enabler for entrepreneurs: a systematic literature review and an agenda for future research. International Journal of Entrepreneurial Behavior & Research, 29(4), 816-837.
Jeremiah, F. (2024). The human-AI dyad: Navigating the new frontier of entrepreneurial discourse. Futures, 103529.
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM computing surveys (CSUR), 54(6), 1-35.
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