| Ethical responsibility | Ambiguity in accountability for AI-generated decisions and outputs | Establish clear regulatory frameworks for AI accountability and liability | Dimitrakopoulou and Garre (2025) Manning et al. (2022) |
| Data privacy and security | Exposure of sensitive operational and quality data; hallucination risks | Implement encryption, access control, and compliance with data protection laws (e.g., GDPR) | Christakis (2024) Demirer et al. (2024) |
| Food safety compliance | Integration with HACCP systems and avoidance of false negatives | Embed AI within certified food safety protocols with redundancy mechanisms | Gaye et al. (2025) Revelou et al. (2025) |
| System transparency | Difficulty in interpreting black-box AI models and tracing decisions | Use explainable AI (XAI) methods and maintain audit trails | Arrighi et al. (2025) |
| Environmental impact | Energy and resource waste in over-processing or trial-and-error cycles | Utilize AI-based simulation and optimization to reduce waste and emissions | Amani and Sarkodie (2022) Rakholia et al. (2025) |
| Workforce implications | Resistance to AI adoption; skill gaps among operators | Provide structured retraining programs and inclusive AI deployment planning | Freire et al. (2024) Song et al. (2025) |