The Ethics of Environmental AI: Modelling Public Perceptions

人工智能系统将在实现环境可持续未来方面发挥重要作用。实时处理海量数据集的需求促使人们提议，此类系统必须成为我们在关键领域优化效率的主要工具；例如，农业产出、能源使用、交通供应和水资源管理。然而，认可机构本身存在潜在的风险和环境成本，在我们开始实施这些系统之前，需要解决重大的伦理和社会影响。在采用广泛的系统来提高效率时，存在这样一种风险，即总体成本降低(无论这些成本是什么)将以相对成本的不成比例分配为代价。我们已经看到，人工智能可以学习人类的偏见，以及算法效率如何最终加剧先前存在的社会不平等。认可机构的干预措施可能会带来净的环境或健康效益，但总会有取舍的地方，我们的成本效益计算需要考虑潜在成本可能采取的不同形式。我们在这一领域面临的成本效益分析将很难付诸实施。我们应该如何在预期的环境收益与潜在的道德、社会和环境风险之间取得平衡？许多正在考虑的因素需要不同的评估指标，这往往使权衡变得不可通约。想想“智能灌溉”计划的含义：我们应该如何权衡预期的水稻增产50%与生态学家的警告，即它有可能使当地一个独特的物种灭绝？(如果处于危险中的动物是“超级可爱”，这一点会改变吗？)出于伦理考虑，这些复杂性只会增加：如果预计大约每月一次，我们是否应该引入一个减少食物浪费的系统，通过对整个人群的饮食需求进行建模，来给出一个严重的低估？这可能会以很小的净成本带来净收益，但这些成本是否会对弱势群体(例如，最贫穷的家庭)造成不成比例的伤害？使用历史消费数据是会提高准确性，还是只会延续现有的不平等？公众对这些问题的看法是这一谜题的重要组成部分，因为这些问题永远不会有绝对正确的答案。它为监管和政策制定提供了至关重要的投入，其好处不仅仅是做出“正确”的决定。众包偏好使人工智能系统的使用具有道德防御性；它促进自由/平等的民主价值观，承认每个人都有就这些问题进行协商的权利，并使人们能够轻松地表达他们的关切。我的研究将集中在公平性和风险认知上，旨在更好地理解公众对道德换取环境收益的可接受性的偏好。环境人工智能系统可能设计和实施的方式(由私营公司、政府部门、环保组织等)使其不可能在个案的基础上直接获得对个别提案的道德判断。因此，这个项目的最终目标是使用我收集的更一般的数据来建立一个计算框架，可以准确地对人们偏好背后的机制进行建模并提供预测。在我们没有民意先验指示的新奇场景中，这个工具可以提供一定程度的伦理指导，因为至少可以考虑公众(模拟的)意见。不平等和权力失衡对环境人工智能的实施构成了主要的伦理问题，其深远的影响源于设计系统的个人和制定系统的权威人士所做的决定。这个拟议中的项目有可能在这类问题上提供帮助，并在一定程度上减轻“气候不公平”的风险。

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.'