Human-computer collaborative learning in citizen science

该项目在环境公民科学的框架内探索人类和机器之间协作学习的潜力。“公民科学”一词包括公众参与科学和向公众进行科学交流。虽然公民科学并不新鲜，但由于公民在数据收集和注释方面获得数字技术带来的机会，公民科学重新受到了关注。虽然绝大多数公民科学项目的目标是收集数据，但我们建议向一种新的公民科学转型，在这种科学中，公众和技术不仅被视为传感器或数据记录器，而且被视为集体和赋权的人类-人工智能，可以在科学学习中相互帮助。我们将专注于从图像中识别物种的任务。ISpot等公民科学项目邀请公众提交野生动物的照片。这些在物种水平上被识别，并在被贡献给科学之前得到验证。我们将探索人工智能作为一种自动识别图像中物种的手段。虽然这可以节省人类的精力，但我们担心这可能会对自然爱好者造成影响。技术的引入往往与降低技能的担忧联系在一起。对于博物学家来说，物种识别技能的磨练是记录活动的关键动机。因此，设计为公民和机器提供学习机会的技术至关重要，共同创造技术也是至关重要的，以确保它不仅对用户友好，而且对他们的动机做出回应。我们的方法将涉及公民与人工智能合作，以达成物种识别。人工智能将缩小选择范围，并告知公民如何区分选项。反过来，公民将通过提供身份识别来帮助机器学习。我们将在协作物种鉴定方面研究这种学习相互作用，但也将探索通过更好地交流复杂的公民科学数据来促进更广泛的科学学习、环境意识和数据素养的技术。为此，我们将开发能够通过语言交流复杂数据的自然语言生成技术。我们的拟议工作方案力求为以下各方面带来可量化的惠益：(A)科学，例如，通过生产新知识和监测具有挑战性的时空尺度上的关键科学进程；(B)不同的利益攸关方，包括公民本身，例如，通过在正规和非正规教育背景下进行有意义的科学学习，促进可持续性；(C)更广泛的社会，例如，通过更好地从社会上了解当前的可持续性问题，促使个人和社会采取行动支持环境。

This project explores the potential for collaborative learning between humans and machines within the framework of environmental citizen science. The term `citizen science' encompasses public participation in science and scientific communication to the public. Although not new, citizen science has gained renewed attention because of the opportunities arising from citizens' access to digital technologies in terms of data collection and annotation. While the vast majority of citizen science projects are aimed at data gathering, we instead propose a transformational shift to a new citizen science in which the public and technology are regarded not just as sensors or data recorders, but as a collective and empowered human--artificial intelligence that can help each other in science learning.We will focus on the task of species identification from images. Citizen science projects such as iSpot invite the public to submit photos of wildlife. These are identified to species level and verified before being contributed to science. We will explore artificial intelligence as a means to automatically identify species in images. While this can save human effort, we are concerned about impact this might have on nature lovers. The introduction of technology is often associated with concerns of de-skilling. For naturalists, the honing of species identification skills is a key motivator of the recording activity. Hence, designing technology that provides opportunities for learning for both citizens and machines is essential, as is co-creating the technology to ensure that it is not only user friendly but responds to their motivations. Our approach will involve citizens collaborating with AI to arrive at a species identification. AI will narrow down the choices and inform the citizen about how to distinguish the options. The citizen in turn will through providing an identification help the machine in its learning. We will study this learning interplay with respect to collaborative species identification, but will also explore technologies that foster wider science learning, environmental consciousness and data literacy through better communication of complex citizen science data. For this we will develop technology for Natural Language Generation that can communicate complex data through language. Our proposed work programme seeks to bring about quantifiable benefits to (a) science, e.g., through the production of new knowledge and through monitoring key scientific processes at challenging temporal-spatial scales; (b) diverse stakeholders including the citizens themselves, e.g., through meaningful science learning for sustainability in formal and informal education contexts; and (c) wider society, e.g., through better societal understanding of current sustainability issues, leading to individual and societal action in support of the environment.