Who do you trust? The digital destruction of shared situational awareness and the COVID-19 infodemic. (December 2020) - Bunker, Deborah
Highlights: Centrally managed digital communication and service platforms simultaneously disrupt and destroy what we trust in and value. Digital destruction creates dissonant mental models. Shared situational awareness for crisis management relies on aligned mental models. Dissonant mental models produce conflicting situational awareness and poor crisis management. Analytics and artificial intelligence research should focus on identification of trusted information sources for mental model realignment. Abstract: Developments in centrally managed communications (e.g. Twitter, Facebook) and service (e.g. Uber, airbnb) platforms, search engines and data aggregation (e.g. Google) as well as data analytics and artificial intelligence, have created an era of digital disruption during the last decade. Individual user profiles are produced by platform providers to make money from tracking, predicting, exploiting and influencing their users' decision preferences and behavior, while product and service providers transform their business models by targeting potential customers with more accuracy. There have been many social and economic benefits to this digital disruption, but it has also largely contributed to the digital destruction of mental model alignment and shared situational awareness through the propagation of mis-information i.e. reinforcement of dissonant mental models by recommender algorithms, bots and trusted individual platform users (influencers). To mitigate this process of digital destruction, new methods and approaches to the centralized management of these platforms are needed to build on and encourage trust in the actors that use them (and by association trust in their mental models). The global 'infodemic' resulting from the COVID-19 pandemic of 2020, highlights the current problem confronting the information system discipline and the urgency of finding workable solutions.