Artificial intelligence: risks and their minimization practices

Relevance. The study focuses on minimizing the risks associated with artificial intelligence (AI) application in vegetable farming, aligning with global trends such as Responsible AI and AgriTech Sustainability.

Methods. A quantitative risk assessment methodology was proposed, including: risk identification (technical, economic, agrotechnical, social, legal); probability (P) and potential damage (L) assessment for enterprises of different scales (large, medium, small); risk calculation and ranking using the criterion R=L×P; proposing mitigation strategies (prevention, insurance, reserve funding).

Results. The study yielded the following conclusions. Large enterprises face the highest potential losses (up to $4 million USD), primarily due to economic and agrotechnical risks. Mediumsized enterprises are most vulnerable to high initial costs and supplier dependency (estimated damage ~$2.1 million USD). Small enterprises are exposed to monopolization and bankruptcy risks, despite lower absolute losses (~$952,000 USD). An analysis of global risk mitigation approaches revealed differences in strategies. The U.S. relies on market mechanisms (insurance, model calibration). The European Union emphasizes strict regulation (GDPR, AI Act). China adopts state platforms and centralized control. Russia focuses on pilot projects under government programs and import substitution. In the Russian context, risk mitigation faces challenges such as: Legal uncertainty due to unclear AI liability criteria and difficulties in proving insurance cases. Technological limitations due to the lack of unified risk assessment methods and insufficient data for actuarial calculations. Market barriers, including high premiums and insurers' excessive caution.

Conclusion. The study summarizes key findings and provides the following recommendations for AI risk mitigation. For large enterprises: Implement backup systems, insure against cyber risks, and monitor environmental impacts. For medium enterprises: Focus on ROI analysis, partner with reliable suppliers, and train personnel. For small enterprises: Use localized AI solutions, participate in government support programs, and collaborate to reduce dependency. At the government level: Develop regulatory frameworks for AI in agriculture, incentivize risk insurance, and support AgriTech R&D.

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