The Ethics of Environmental AI: Modelling Public Perceptions

AI systems will play a large role in enabling an environmentally sustainable future. The need to process vast datasets in real time has led to proposals that such systems must become our primary tools for optimising efficiency in critical domains; for example, agricultural output, energy usage, transport provision, and water resource management. However, AIs present their own potential risks and environmental costs and there are major ethical and societal implications that need to be addressed before we start to put these systems to work.

When employing wide-reaching systems for improved efficiency there is a risk that overall cost reduction (whatever those costs are) will come at the expense of a disproportionate distribution of relative costs. We have already seen that AIs can learn human biases and how algorithmic efficiency can end up reinforcing pre-existing social inequality. The interventions of AIs may bring net environmental or health benefits but there will always be trade-offs and our cost-benefit calculations need to account for the different forms that potential costs may take.

The cost-benefit analyses that we face in this area are going to be very hard to operationalise. How should we balance projected environmental gains against potential ethical, social, and environmental risks? Many factors under consideration require different evaluation metrics, often making the trade-offs incommensurable. Consider the implications of a 'smart irrigation' initiative: how should we weigh an expected 50% increase to rice yield against ecologist's warnings that it risks the extinction of a unique local species? (Does this change if the animal at risk is "super cute?") These complications only increase for ethical concerns: should we bring in a system that reduces food wastage, by modelling the dietary needs across the population, if it is expected to give a major underestimate approximately once a month? This might give a net gain for a small net cost, but will the costs cause disproportionate harm to vulnerable groups (e.g., the poorest families)? Will using historical consumption data improve accuracy or just perpetuate existing inequalities?

Public perception of these issues is an important part of this puzzle as such questions will never have categorically correct answers. It provides a crucial input for regulation and policy making with benefits beyond making the 'right' decision. Crowdsourcing preferences gives the use of AI systems ethical defensibility; it promotes democratic values of freedom/equality, recognising that everyone has a right to consultation on these matters and enabling people to easily voice their concerns. My research will focus on fairness and risk perception, aiming to better understand public preferences regarding the acceptability of moral trade-offs for environmental gains.

The manner in which environmental AI systems are likely to be designed and implemented (by private companies, governmental departments, environmental groups etc.) make it impossible to directly acquire moral judgments of individual proposals on a case-by-case basis. Accordingly, the ultimate goal of this project is to use the more general data I collect to build a computational framework that can accurately model the mechanisms underlying people's preferences and provide predictions. In novel scenarios, where we have no a priori indication of public opinion, this tool could provide some level of
ethical guidance as at least the public's (modelled) views can be considered.

Inequality and power imbalances pose major ethical concerns for the implementation of environmental AIs, with far-reaching ramifications originating in the decisions made by individuals designing the systems and those in authority who enact them. This proposed project has the potential to aid in such matters and go some way towards mitigating the risks of 'climate injustice.'