Guiding agribusiness producer decisions regarding futures contracts

A computer system for guiding agribusiness producer decisions by an agribusiness producer, the computer system comprising:

• a bus system;
• a storage device connected to the bus system, wherein the storage device stores program instructions;
• and
• a processor connected to the bus system, wherein the processor executes the program instructions to:
• calculate a first risk coefficient and a first profit coefficient corresponding to selling a commodity via a traditional cash market and a second risk coefficient and a second profit coefficient corresponding to selling the commodity via a futures market using a set of trained artificial intelligence models comprising a deep analysis and data optimization component decreasing artificial intelligence model bias using a fairness measure module that iteratively applies a fairness model to each of the set of trained artificial intelligence models until each artificial intelligence model has Jess than a predetermined maximum level of bias, wherein the fairness model executes validation and corrections on each of the set of trained artificial intelligence models, wherein the fairness model provides an interaction to eliminate bias by correcting each of the set of trained artificial intelligence models until ready to be used on its corresponding data dimension and for each target objective of profit and risk regarding futures market percentage of negotiation until ready to deliver coefficients for profit and risk;
• calculate, using a first objective function, a minimized level of risk based on the first risk coefficient and the second risk coefficient corresponding to selling the commodity in the traditional cash market and the futures market, respectively, and information in a profile received from a producer of the commodity;
• calculate, using a second objective function, a maximized level of profit based on the first profit coefficient and the second profit coefficient corresponding to selling the commodity in the traditional cash market and the futures market, respectively, and information in the profile received from the producer of the commodity;
• and
• send a recommendation to a graphical user interface comprising a dashboard display that includes calculations of the minimized level of risk and the maximized level of profit corresponding to the commodity, a first percentage of the commodity to sell via the futures market and a second percentage of the commodity to sell via the traditional cash market, estimated profit and associated risk level, a justification button that links to indexed recommendation justification document information used to derive the estimated profit and associated risk level, and a feedback button that enables the producer of the commodity to provide the interaction including a feedback regarding the recommendation, wherein the recommendation, the justification button and the feedback button are used by the agribusiness producer to control risk by using the feedback button to send the interaction including a feedback as to whether the recommendation was valuable or not, the feedback being processed and utilized as input to retrain the artificial intelligence models.

A computer program product for guiding agribusiness producer decisions by an agribusiness producer, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:

• calculating, by the computer, a first risk coefficient and a first profit coefficient corresponding to selling a commodity via a traditional cash market and a second risk coefficient and a second profit coefficient corresponding to selling the commodity via a futures market using a set of trained artificial intelligence models comprising a deep analysis and data optimization component decreasing artificial intelligence model bias using a fairness measure module that iteratively applies a fairness model to each of the set of trained artificial intelligence models until each artificial intelligence model has less than a predetermined maximum level of bias, wherein the fairness model executes validation and corrections on each of the set of trained artificial intelligence models, wherein the fairness model provides an interaction to eliminate bias by correcting each of the set of trained artificial intelligence models until ready to be used on its corresponding data dimension and for each target objective of profit and risk regarding futures market percentage of negotiation until ready to deliver coefficients for profit and risk;
• calculating, by the computer, using a first objective function, a minimized level of risk based on the first risk coefficient and the second risk coefficient corresponding to selling the commodity in the traditional cash market and the futures market, respectively, and information in a profile received from a producer of the commodity;
• calculating, by the computer, using a second objective function, a maximized level of profit based on the first profit coefficient and the second profit coefficient corresponding to selling the commodity in the traditional cash market and the futures market, respectively, and information in the profile received from the producer of the commodity;
• and
• sending, by the computer, a recommendation to a graphical user interface comprising a dashboard display that includes calculations of the minimized level of risk and the maximized level of profit corresponding to the commodity, a first percentage of the commodity to sell via the futures market and a second percentage of the commodity to sell via the traditional cash market, estimated profit and associated risk level, a justification button that links to indexed recommendation justification document information used to derive the estimated profit and associated risk level, and a feedback button that enables the producer of the commodity to provide feedback regarding the recommendation, wherein the recommendation, the justification button and the feedback button are used by the agribusiness producer to control risk by using the feedback button to send the interaction including a feedback as to whether the recommendation was valuable or not, the feedback being processed and utilized as input to retrain the artificial intelligence models.