Implementing an unlearning approach to combat counter-knowledge in multiple sclerosis

Abstract

ABSTRACT In the social domain, the term counter-knowledge has been used to refer to misinformation, gossip, rumours, and conspiracy theories that masquerade as knowledge. An individual’s assimilation of such counter-knowledge can lead to inappropriate individual behaviours and organisational decision-making. This study proposes a framework for investigating the relationship between counter-knowledge and learning myopia at the individual level in the healthcare domain, focusing on Multiple Sclerosis (MS). Given that those suffering from MS can experience symptoms leading to both a slowing down of information processing and a limited capacity. It is argued that these symptoms are likely to lead to exacerbating other symptoms, such as anxiety and depression. In addition to investigating how social counter-knowledge results in individual counter-knowledge, the research proposes a framework for understanding the challenges of implementing machine unlearning approaches, and a set of strategies to disrupt this linkage is also proposed.