AI tools as science policy advisers? The potential and the pitfalls

The potential for AI to revolutionize conservation: a horizon scan

Artificial Intelligence (AI) is an emerging tool that could be leveraged to identify the effective conservation solutions demanded by the urgent biodiversity crisis. We present the results of our horizon scan of AI applications likely to significantly benefit biological conservation. An international panel of conservation scientists and AI experts identified 21 key ideas. These included species recognition to uncover 'dark diversity', multimodal models to improve biodiversity loss predictions, monitoring wildlife trade, and addressing human-wildlife conflict. We consider the potential negative impacts of AI adoption, such as AI colonialism and loss of essential conservation skills, and suggest how the conservation field might adapt to harness the benefits of AI while mitigating its risks.

Artificial Intelligence: A Challenge to Scientific Communication

Recent years have seen formidable advances in artificial intelligence. Developments include a large number of specialised systems either existing or planned for use in scientific research, data analysis, translation, text production and design with grammar checking and stylistic revision, plagiarism detection, and scientific review in addition to general-purpose AI systems for searching the internet and generative AI systems for texts, images, videos, and musical compositions. These systems promise more ease and simplicity in many aspects of work. Blind trust in AI systems with uncritical, careless use of AI results is dangerous, as these systems do not have any inherent understanding of the content they process or generate, but only simulate this understanding by reproducing statistical patterns extracted from training data. This article discusses the potential and risk of using AI in scientific communication and explores potential systemic consequences of widespread AI implementation in this context.

Leveraging AI to improve evidence synthesis in conservation

Systematic evidence syntheses (systematic reviews and maps) summarize knowledge and are used to support decisions and policies in a variety of applied fields, from medicine and public health to biodiversity conservation. However, conducting these exercises in conservation is often expensive and slow, which can impede their use and hamper progress in addressing the current biodiversity crisis. With the explosive growth of large language models (LLMs) and other forms of artificial intelligence (AI), we discuss here the promise and perils associated with their use. We conclude that, when judiciously used, AI has the potential to speed up and hopefully improve the process of evidence synthesis, which can be particularly useful for underfunded applied fields, such as conservation science.

A synthesis of evidence for policy from behavioural science during COVID-19

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations ('claims') detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms 'physical distancing' and 'social distancing'. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.

A horizon scan of global biological conservation issues for 2024

We present the results of our 15th horizon scan of novel issues that could influence biological conservation in the future. From an initial list of 96 issues, our international panel of scientists and practitioners identified 15 that we consider important for societies worldwide to track and potentially respond to. Issues are novel within conservation or represent a substantial positive or negative step-change with global or regional extents. For example, new sources of hydrogen fuel and changes in deep-sea currents may have profound impacts on marine and terrestrial ecosystems. Technological advances that may be positive include benchtop DNA printers and the industrialisation of approaches that can create high-protein food from air, potentially reducing the pressure on land for food production.