About this Research Topic
Decisions for public services in education and health care are increasingly influenced, if not made, by the use of digital technologies to supplement, or even replace professional judgement. The status of a profession is dependent on establishing claims to specialist knowledge acquired through extensive training, experience sustained through life-long learning and a commitment to a code of ethics. Integral to a professional’s daily work is the requirement to draw upon evidence to exercise judgement in determining the best course of action in complex, problematic situations.
The daily work of professionals, such as teachers and doctors, involves situations in which differences in estimations of the value of sources of information and its interpretation may be most apparent. As digitalization makes it easier to accumulate more data faster, these professionals regularly come into contact with misinformation and the decisions, increasingly assisted by algorithms, made by them are often called into question.
Previous research surrounding attitudes towards human versus algorithmic judgement suggests that people are more likely to erroneously avoid algorithms after seeing them err. Instead, they choose decisions made by inferior human forecasters, even among those who saw the algorithms outperform their human counterparts. Relative to the lay public, professionals are less likely to rely on algorithms, and more likely to disagree with each other. This highlights the tension between algorithms and experts and calls for further research into the best ways forward to integrate human and algorithmic judgements in key decision-making processes.
This Research Topic calls for articles that advance our understanding of the roles professionals play in decision-making processes, how and why the public view predictions/forecasts made by both algorithms and experts in specific ways. It also aims to detect any changing attitudes to expertise in different societies and understand how the shifts, if any, in the estimation of expertise and the roles of professions in decision making impact on public perceptions of trust. We hope that the insights derived from cross-professional comparisons will help societies better respond to the challenges and opportunities that data and algorithms are increasingly likely to pose and create in the years to come.
We particularly invite original research that investigates cross-professional comparisons of human-algorithm joint decision-making frameworks that tap into the benefits data and algorithms have to offer and local knowledge and moral values human experts hold dear. The contributions may propose new theoretical frameworks or demonstrate novel methodologies that integrate human and algorithmic judgements to better serve the public. We also welcome reviews of empirical research on the characteristics of professional learning that involves the use of data and algorithms, or the attitudes the public holds towards professionals and algorithms that make predictions to facilitate decision-making processes.
Keywords: Algorithms, Data, Decision Making, Evidence Informed Practice, Prediction, Professional Learning, Trust
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