AI, agriculture and the future of food - The Washington Post

Robots. Drones. Artificial Intelligence.

All three are touted as potential saviors for farmers, and are already being deployed on large farms, where they assist with such tasks as managing crops, milking cows and helping farmers make decisions about their land.

But agricultural AIcould have disastrous, unintended consequences.

At least that’s the message of a new analysis in Nature Machine Intelligence. When an international group of researchers examined AI in agriculture, they found a variety of possible risks — and they’re urging farmers to consider them before it’s too late.

“So far no one seems to have asked the question, ‘Are there any risks associated with a rapid deployment of agricultural AI?’,” said Asaf Tzachor, a researcher at the Centre for the Study of Existential Risk at the University of Cambridge, in a news release.

The potential benefits are huge. Increases in farm productivity could help feed the approximately 2.4 billion people around the world who experience food insecurity and malnutrition and revolutionize the way farmers use their land.

That could come at a cost. The analysis points out potential flaws in the agricultural data that fuels AI-powered systems and the possibility that autonomous systems could place productivity over the environment. That could lead to inadvertent errors causing overfertilization, dangerous pesticide use, inappropriate irrigation or erosion, risking crop yields, water supplies and soil. And wide-scale crop failures could exacerbate food insecurity.

Cybersecurity is another potential failure point. The researchers said cyberattacks could disrupt entire food systems. The more reliant farm systems are on intelligent machines, the more disruption could be created if they malfunction or are destroyed.

Then there are people — and without inclusive technology, the researchers warn, AI could simply increase inequities that already exist in farming. As big farmers profit, small-scale farmers in the global South, for instance, might be locked out of farming gains altogether.

Potential solutions mentioned by the researchers include data sharing, citizen input and digital “sandboxes” where developers can forecast potential failure points for farm AI.

“Technological modernization in farming has achieved much,” the researchers write. But irresponsible developers could “ignore and thereby perpetuate drivers of nutritional insecurity, exploitation of labor, and environmental resources depletion.”

Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities

Nature Machine Intelligence

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