Guided Unlearning of Cognitive Pitfalls in Georeferenced Social Sensing

社交媒体平台加速了社交传感的繁荣，并为大地理空间数据的生成和传播做出了重大贡献，为科学，商业，政治和社会带来了好处。与此同时，社交媒体平台也成为滥用、操纵、伪造或阴谋论的温床。目前的研究重点主要是学习新知识，而不是反思数据和人类思维中的缺陷。该提案解决了社会感知中偏见引起的认知陷阱。对于有关新冠肺炎疫情和气候变化的地理参考社交媒体数据的选定应用场景，将开发并原型实施一个互动平台，用于引导认知偏见的学习。忘却是一种激进的学习方法。与传统的学习或知识积累不同，传统的学习或知识积累是基于对学习者增加新的东西，而遗忘过程是基于有意识地减去已经存在的不需要的东西，无论是天生的还是后天习得的。它往往是反直觉的，因此需要认知。既没有现成的通用配方，也没有经验上可行的技术途径。该项目有三个目标：（1）提高地理参考社会感知价值链的透明度。将制定道德上允许的机制，解释如何为特定目的创建、地理参照、处理、传播和分享关于特定现象的社交媒体数据，以及谁在数据从上游流向下游的哪个阶段参与其中。(2)支持用户对地理参考社会感知中认知偏差的整体理解。将设计一种指导性的遗忘方法沿着一套可视化分析工具，并使用概念化的地理参考数据流进行测试。它允许用户全面了解认知偏见可能在哪里以及哪些地方蔓延，以及一些看似有益的日常生活心理捷径如何在不确定和复杂的情况下成为误导性陷阱。(3)评估使用者接受引导性遗忘训练后的批判性推理能力。一些指导性的遗忘实验将设计和实施有偏见的数据为选定的现实世界的情况。我们将收集初步发现和/或提出新的问题，依靠两种比较：未经培训的用户解决方案和基准解决方案之间的比较，以及未经培训的用户解决方案和经过培训的用户解决方案之间的比较。

Social media platforms have accelerated the prosperity of social sensing and made a substantial contribution to the generation and dissemination of big geospatial data for the benefits of science, business, politics and society. At the same time, social media platforms have also become the breeding bed for abuse, manipulation, fake or conspiracy theory. The current research focus is mainly set on learning new knowledge rather than reflecting upon flaws in data and in human minds. This proposal addresses bias-induced cognitive pitfalls in social sensing. For selected application scenarios of georeferenced social media data about Covid-19 pandemic and Climate Change, an interactive platform for guided unlearning of cognitive biases will be developed and prototypically implemented. Unlearning is a radical method of learning. Unlike conventional learning or knowledge accumulation, which is based on the addition of what is new to the learner, an unlearning process is based on the conscious subtraction of something undesirable that already exists, either innately or learned. It is often counter-intuitive and therefore cognitively demanding. Neither a ready-made general recipe nor an empirically feasible technical pathway is available. The project has three objectives: (1) To improve the transparency with regard to the value chain of georeferenced social sensing. Ethically permissible mechanisms will be developed to explain how social media data about a given phenomenon for a given purpose is created, georeferenced, processed, disseminated and shared, and who is involved at which stage of the data flow from upstream to downstream. (2) To support users’ holistic understanding of cognitive biases in georeferenced social sensing. A guided unlearning approach along with a set of visual analytical tools will be designed and tested with conceptualized georeferenced data flows. It allows users to get a comprehensive picture of where and which cognitive biases may creep in and how some seemingly beneficial mental shortcuts for the everyday life may become misleading pitfalls in uncertain and complex situations.(3) To assess users’ capacity of critical reasoning after the training of guided unlearning. A number of guided unlearning experiments will be designed and implemented with biased data for selected real-world scenarios. We will collect preliminary findings and/or raise new questions, relying on two kinds of comparison: between untrained user solutions and benchmark solutions, and between untrained and trained user solutions.