Agricultural AI cant improve food security

Two sorts of bias are worthy of highlighting in the context of agriculture.

First, initially at least, the main markets for AI tools for agriculture will be the United States, Europe, and (perhaps) China. There is a danger that farmers in other regions will inherit AI systems trained on data about crop yields or soil chemistry in the US, Europe, or China, rendering them highly problematic in the local context elsewhere (Keogh & Henry, ); efforts to correct this by retraining the systems using data about crops grown under local conditions will still embed a preference for some crops over others.

Second, the farming practices and environments that produce this data are themselves likely to be industrial farming practices. Yet in many parts of the world there are vibrant traditions of small scale and indigenous farming, which are unlikely to generate the sorts of data that are used to train AI systems because the technologies of digital agriculture play little role therein. If the circumstances, challenges, and experiences of small landholders and indigenous peoples are not represented in the data used to train agricultural AI then these systems will, in an important sense, be biased against such traditions. As noted above, this is likely to mean that agricultural AI will serve indigenous and small-scale farmers poorly – if they work at all in these contexts. However, the exclusion of these perspectives from a technology that is widely touted as playing a key role in the future of agriculture might also be criticised on political grounds as effectively denying the right of small-scale and indigenous farmers to contribute to shaping that future. It would also mean that society would fail to benefit from the valuable knowledge of the local climate, flora, and fauna contained in these traditions