Dependence on high-quality, reliable data

Need for external validation of AI models

Scalability of AI solutions to large and evolving datasets

Learning from pre-existing data may perpetuate biases

Risk of biased training data leading to unfair or inaccurate outcomes

Poor data quality reduces the accuracy and reliability of AI predictions

Models may fail under new conditions without validation

Scalability is essential for adapting to growing and diverse food safety datasets

Bias can result in inequitable decision-making and compromised food safety evaluations

Incompatibility with legacy food safety infrastructure

High costs and complex regulatory standards hinder adoption

Environmental burden of AI systems (energy use, e-waste)

Technical barriers delay deployment in real-world food systems

Cost and regulatory hurdles limit adoption, especially in low-resource settings

Sustainability concerns must be addressed to avoid unintended harms

“Black box” nature of AI decisions limits interpretability

Risks to data privacy and security

Need for informed consent in the collection of personal or health-related data

Lack of transparency undermines trust and compliance

Privacy violations erode consumer confidence

Ethical safeguards and accountability frameworks are essential for responsible AI deployment