Department of Information Systems, The University of Haifa, Haifa, Israel.
Department of Learning and Instructional Sciences, The University of Haifa, Haifa, Israel.
PLoS One. 2024 Aug 26;19(8):e0308552. doi: 10.1371/journal.pone.0308552. eCollection 2024.
The collective intelligence of crowds could potentially be harnessed to address global challenges, such as biodiversity loss and species' extinction. For wisdom to emerge from the crowd, certain conditions are required. Importantly, the crowd should be diverse and people's contributions should be independent of one another. Here we investigate a global citizen-science platform-iNaturalist-on which citizens report on wildlife observations, collectively producing maps of species' spatiotemporal distribution. The organization of global platforms such as iNaturalist around local projects compromises the assumption of diversity and independence, and thus raises concerns regarding the quality of such collectively-generated data. We spent four years closely immersing ourselves in a local community of citizen scientists who reported their wildlife sightings on iNaturalist. Our ethnographic study involved the use of questionnaires, interviews, and analysis of archival materials. Our analysis revealed observers' nuanced considerations as they chose where, when, and what type of species to monitor, and which observations to report. Following a thematic analysis of the data, we organized observers' preferences and constraints into four main categories: recordability, community value, personal preferences, and convenience. We show that while some individual partialities can "cancel each other out", others are commonly shared among members of the community, potentially biasing the aggregate database of observations. Our discussion draws attention to the way in which widely-shared individual preferences might manifest as spatial, temporal, and crucially, taxonomic biases in the collectively-created database. We offer avenues for continued research that will help better understand-and tackle-individual preferences, with the goal of attenuating collective bias in data, and facilitating the generation of reliable state-of-nature reports. Finally, we offer insights into the broader literature on biases in collective intelligence systems.
群体的集体智慧有可能被用来应对全球性挑战，如生物多样性丧失和物种灭绝。为了使智慧从人群中涌现出来，需要满足一定的条件。重要的是，人群应该多样化，人们的贡献应该相互独立。在这里，我们研究了一个全球性的公民科学平台-iNaturalist-在这个平台上，公民报告野生动物观察结果，共同制作物种时空分布的地图。像 iNaturalist 这样的全球性平台围绕着地方项目组织起来，这破坏了多样性和独立性的假设，因此引起了对这种集体生成的数据质量的担忧。我们花了四年时间深入研究了一个在 iNaturalist 上报告野生动物目击情况的当地公民科学家社区。我们的民族志研究包括使用问卷、访谈和分析档案材料。我们的分析揭示了观察者在选择何地、何时以及监测何种类型的物种以及报告哪些观察结果时的细微考虑。在对数据进行主题分析之后，我们将观察者的偏好和限制组织成四个主要类别：可记录性、社区价值、个人偏好和便利性。我们表明，虽然一些个人偏见可以“相互抵消”，但其他偏见在社区成员中是共同的，这可能会使观察结果的综合数据库产生偏差。我们的讨论提请注意广泛共享的个人偏好可能以空间、时间，以及至关重要的是，分类学偏差的方式在集体创建的数据库中表现出来。我们提供了继续研究的途径，这将有助于更好地理解和解决个人偏好问题，目标是减轻数据的集体偏差，并促进可靠的自然状况报告的生成。最后，我们深入探讨了集体智慧系统中偏见的更广泛文献。
