Artificial intelligence is changing the way our food is produced. Advancements such as machine learning, image recognition, and predictive modeling are being used in food production to boost productivity and efficiency.
Some farms are using AI-driven robots and autonomous vehicles. AI also helps us understand the biology of crops and provides operational recommendations from agronomist-trained algorithms, which allows producers to use fewer chemical and water inputs.
As we digitize the agricultural supply chain, we reduce food waste, improve our ability to respond quickly to potential recalls and develop supply chain transparency. AI will help eliminate inefficiencies for the producer and in the supply chain and bring the transparency and convenience to consumers they crave.
Here are the top ethical issues we must address to get the best out of AI:
Trust and transparency in business practices
AI, machine learning and blockchain allow for transparency in how we conduct business in the food supply chain, which can build consumer trust. This includes not only food safety but also pesticide levels and the nutrition claims of food items.
Use of data and insights
We must communicate that data and insights belong to the owner and are used with permission. Otherwise, we can’t get enough data to make AI work. It is a switch in mindset for producers that sharing information (anonymously) with their neighbors will benefit them by being able to see patterns and make predictions over larger data sets.
Cyber attacks pose a real threat. Plans need to be in place—and communicated—for protection of data by the companies that aggregate and use data. As technology becomes more powerful, the more it can be used for nefarious reasons as well as for good.
We shouldn’t forget that AI systems are created by humans, who can be biased and judgmental. Many AI systems will continue to be trained using bad data, making this an ongoing problem. As we work to develop AI systems we can trust, it’s critical to develop and train these systems with data that is unbiased and to develop algorithms that can be easily explained. This is a bigger problem when data sets are used for decision-making involving people, but scientific bias and bad assumptions can also be programed into data sets around food production.
AI can boost agriculture’s productivity to unprecedented levels, which will be necessary to overcome the current and future issues in food production. As an industry, we should continue to focus on who has access to technology and how development is funded. New innovative tools tend to be very expensive to develop and adopt. Global food production and the ability to feed all people will benefit by funding technologies that are available to producers of all crops – both in corporate and subsistence farms.