Risks of Using Artificial Intelligence in Agricultural Credit Valuation
While examining the use of artificial intelligence in agricultural loans, it is emphasized that models may miss operational context and should serve as decision-support tools.
Mariah Beverly, Kimberly L. Morgan, and Elizabeth Canales analyzed the role and potential risks of artificial intelligence in agricultural credit evaluation processes.
The Place of Artificial Intelligence in Agricultural Banking
Mariah Beverly, Kimberly L. Morgan, and Elizabeth Canales comprehensively discuss the application of artificial intelligence technologies in the agricultural banking sector.
Disadvantages of Historical Data
While artificial intelligence provides efficiency and speed, relying on historical data in credit scoring can create disadvantages for beginning farmers.
Unrepresented Sectors
Due to the lack of sufficient representation data, specialty crop producers and farmers operating in niche agricultural sectors can be adversely affected.
Lack of Operational Context
It is emphasized that artificial intelligence models may overlook operational contexts, such as temporary financial declines resulting from health issues or market fluctuations.
Importance of the Human Factor
It is stated that human lenders are much better equipped and competent compared to artificial intelligence in interpreting such situational factors.
Decision Support Tool Recommendation
The authors conclude that artificial intelligence should be used as a tool to support human judgment rather than being positioned as an autonomous decision-maker.