On Cyber-Farming, AI Ethics and More

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Last week, MIT Media lab's article “The Future of Agriculture is Computerized" focused on 1) enhancing the human disease-fighting properties of herbs and 2) helping growers adapt to changing climate conditions.  

  • Caleb Harper, a principal research scientist, said "Our goal is to design open-source technology at the intersection of data acquisition, sensing, and machine learning, and apply it to agricultural research in a way that hasn't been done before"

  • Researcher John de la Parra said, "When you grow things in a field, you have to rely on the weather and other factors to cooperate, and you have to wait for the next growing season. With systems like ours, we can vastly increase the amount of knowledge that can be gained much more quickly."

  • Initial results included learning that exposing basil plants to light 24 hours a day can generate better flavor.

  • Separately, in a report on Artificial Intelligence ethics guidelines, the European Commission said, "Artificial Intelligence (AI) can benefit a wide-range of sectors, such as healthcare, energy consumption, cars safety, farming, climate change and financial risk management. AI can also help to detect fraud and cybersecurity threats, and enables law enforcement authorities to fight crime more efficiently.

  • "However, AI also brings new challenges for the future of work, and raises legal and ethical questions ….

  • "The ethical dimension of AI is not a luxury feature or an add-on. It is only with trust that our society can fully benefit from technologies.”

  • In their report “Credit Denial in the Age of AI”, the Brookings Institution said “AI coupled with ML and big data, allows for far larger types of data to be factored into a credit calculation. Examples range from social media profiles, to what type of computer you are using, to what you wear, and where you buy your clothes.

  • "If there are data out there on you, there is probably a way to integrate it into a credit model. But just because there is a statistical relationship does not mean that it is predictive, or even that it is legally allowable to be incorporated into a credit decision.

  • In the article “A.I. Is Changing Insurance”, the NY Times said, “Some of the changes heralded by these new technologies will be better for everyone, like faster claims processing. But the use of data collection and artificial intelligence also raises serious questions about what McKinsey calls “personalized pricing” and what the State Farm patent application calls “personalized recommendations” and “insurance discounts …. 

  • "As machine learning works its way into more and more decisions about who gets coverage and what it costs, discrimination becomes harder to spot.”


  • The value of AI and the need for trust:  As Al creates values in many industries, developing trusts for these systems will require ) incorporating the views of multiple stakeholders 2) using quality data and 3) addressing privacy and security concerns.

  • Regarding agriculture: Leveraging large data sets and the statistical power of AI/machine learning techniques will help uncover many beneficial approaches to agriculture and beyond.

  • Regarding credit denials: As the industry seeks AI-driven credit approval approaches, it is important to understand the difference between causation and correlation in the decision-making process. 

  • Regarding insurance: Historically, the business of insurance focused on sharing risk across broad groups of people. “Personalized” services, driven by an individual's profit/risk potential, may place "at risk" individuals at increased levels of financial uncertainty as well.

Paul Dravis