The UN Sustainable Development Goals (SDGs) are a vision for achieving a sustainable future. Reliable, timely, comprehensive, and consistent data are critical for measuring progress towards, and ultimately achieving, the SDGs. Data from citizen science represent one new source of data that could be used for SDG reporting and monitoring. However, information is still lacking regarding the current and potential contributions of citizen science to the SDG indicator framework. Through a systematic review of the metadata and work plans of the 244 SDG indicators, as well as the identification of past and ongoing citizen science initiatives that could directly or indirectly provide data for these indicators, this paper presents an overview of where citizen science is already contributing and could contribute data to the SDG indicator framework. The results demonstrate that citizen science is “already contributing” to the monitoring of 5 SDG indicators, and that citizen science “could contribute” to 76 indicators, which, together, equates to around 33%. Our analysis also shows that the greatest inputs from citizen science to the SDG framework relate to SDG 15 Life on Land, SDG 11 Sustainable Cities and Communities, SDG 3 Good Health and Wellbeing, and SDG 6 Clean Water and Sanitation. Realizing the full potential of citizen science requires demonstrating its value in the global data ecosystem, building partnerships around citizen science data to accelerate SDG progress, and leveraging investments to enhance its use and impact.

Introduction Technologies that allow computers and machines to perform tasks normally requiring human intelligence are often referred to as artificial intelligence (AI). These technologies allow machines to complete tasks with traits or capabilities ordinarily associated with human cognition, such as reasoning, problem solving, common-sense knowledge management, planning, learning, translation, perception, vision, speech recognition, and social intelligence (Kaplan and Haenlein 2019). Research in AI is rapidly increasing, as indicated when c omparing the annual publishing rate of papers focused on AI between 1996 and 2017 against the publishing rates of papers focused on any topic or against the publishing rates of papers in the field of computer science (see the growth of annually published papers by topic in Shoham et al. [2018; p. 9]). This growth in AI publications has prompted researchers to critically explore the potential promises and risks of AI (Scherer 2016; Webb 2019; Yudkowsky 2008) as well as ethics and responsibilities (Miller 2019; Cowls and Floridi 2018; Scherer 2016; Dawson et al. 2019). AI has been used in citizen science projects for about 20 years. It was first used in this context in 2000, in collaborative AI databases such as the Generic Artificial Consciousness (GAC)/Mindpixel Digital Mind Modeling Project (McKinstry 2009) and the Open Mind Common Sense project (Singh et al. 2002). In these models, usersubmitted propositions were meant to create a database of common-sense knowledge that could function as a kind of digital brain. This relationship between collective knowledge and algorithmic processing evolved in many directions and, in 2019, is predominantly represented by machine learning, especially applied to computer vision, which includes diverse methods of automatically identifying objects from digital photographs. For example, the iNaturalist platform, a citizen science project and online social network, is designed to enable citizen scientists and ecologists alike to upload observations from the natural world, such as images of animals and plants (Van Horn et al. 2018). The platform is one among many (Wäldchen et al. 2018) that include an automated

Advancing technology represents an unprecedented opportunity to enhance our capacity to conserve the Earth's biodiversity. However, this great potential is failing to materialize and rarely endures. We contend that unleashing the power of technology for conservation requires an internationally coordinated strategy that connects the conservation community and policy-makers with technologists. We argue an international conservation technology entity could (1) provide vision and leadership, (2) coordinate and deliver key services necessary to ensure translation from innovation to effective deployment and use of technology for on-the-ground conservation across the planet, and (3) help integrate innovation into biodiversity conservation policy from local to global scales, providing tools to monitor outcomes of conservation action and progress towards national and international biodiversity targets. This proposed entity could take the shape of an international alliance of conservation institutions or a formal intergovernmental institution. Active and targeted uptake of emerging technology can help society achieve biodiversity conservation goals.

Well-designed citizen science projects can improve the capacity of the scientific community to detect and understand declines in threatened species, and with the emergence of frameworks to guide good design, there is an opportunity to test whether projects are aligned with best practice. We assessed the current landscape of citizen science projects for threatened species conservation via a content analysis of the online communique of citizen science projects across Australia. Only 2% of projects stated clear research questions, although approximately 86% had implied project objectives aimed at threatened species conservation. Most projects were focused on field-based monitoring activities with half using structured ecological survey methods. Most reviewed projects (65%) shared data with open access biodiversity databases and the vast majority use at least one social media platform to communicate with potential and existing participants (up to 81%). Approximately 50% present citizen-sourced data summaries or publications on their websites. Our study shows there is a very strong foundation for public participation in threatened species conservation activities in Australia, yet there is scope to further integrate the principles of citizen science best practice. Improved integration of these principles will likely yield better outcomes for threatened species as well as for the citizen scientists themselves.

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<p>The contribution of citizen science to addressing societal challenges has long been recognized. The United Nations (UN) Sustainable Development Goals (SDGs), as an overarching policy framework and a roadmap to guide global development efforts until 2030 for achieving a better future for all, could benefit from the potential that citizen science offers. However, there is a lack of knowledge on the value of citizen science, particularly in addressing the data needs for SDG monitoring, among the UN agencies, national statistical offices, policy makers and the citizen science community itself. To address this challenge, we launched a Community of Practice on Citizen Science and the SDGs (SDGs CoP) in November 2018 as part of the EU Horizon 2020 funded WeObserve project.</p><p>The SDGs CoP brings together citizen science researchers, practitioners, UN custodian agencies, broader data communities and other key actors to develop an understanding on how to demonstrate the value of citizen science for SDG achievement. The initial focus and the main objective of the SDGs CoP has been to conduct a research study to understand the contribution of citizen science to SDG monitoring and implementation. In this talk, we will present the work of the SDGs CoP. We will first discuss existing data gaps and needs for measuring progress on the SDGs, and then provide an overview on the results of a systematic review that we undertook within the CoP, showing where citizen science is already contributing and could contribute data to the SDG framework. We will provide concrete examples of our findings to demonstrate how citizen science data could inform the SDGs. We will also touch on the challenges for and barriers to the uptake of citizen science data for the SDG monitoring processes, and how to bring this source of data into the scope of official statistics.</p>

Innovation has the potential to enable conservation science and practice to keep pace with the escalating threats to global biodiversity, but this potential will only be realized if such innovations are designed and developed to fulfill specific needs and solve well‐defined conservation problems. We propose that business‐world strategies for assessing the practicality of innovation can be applied to assess the viability of innovations, such as new technology, for addressing biodiversity conservation challenges. Here, we outline a five‐step, “lean start‐up” based approach for considering conservation innovation from a business‐planning perspective. Then, using three prominent conservation initiatives – Marxan (software), Conservation Drones (technology support), and Mataki (wildlife‐tracking devices) – as case studies, we show how considering proposed initiatives from the perspective of a conceptual business model can support innovative technologies in achieving desired conservation outcomes.

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