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The problem of evaluating automated large-scale evidence aggregators

Wüthrich, Nicolas and Steele, Katie (2017) The problem of evaluating automated large-scale evidence aggregators. Synthese. ISSN 0039-7857

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Identification Number: 10.1007/s11229-017-1627-1


In the biomedical context, policy makers face a large amount of potentially discordant evidence from different sources. This prompts the question of how this evidence should be aggregated in the interests of best-informed policy recommendations. The starting point of our discussion is Hunter and Williams’ recent work on an automated aggregation method for medical evidence. Our negative claim is that it is far from clear what the relevant criteria for evaluating an evidence aggregator of this sort are. What is the appropriate balance between explicitly coded algorithms and implicit reasoning involved, for instance, in the packaging of input evidence? In short: What is the optimal degree of ‘automation’? On the positive side: We propose the ability to perform an adequate robustness analysis (which depends on the nature of the input variables and parameters of the aggregator) as the focal criterion, primarily because it directs efforts to what is most important, namely, the structure of the algorithm and the appropriate extent of automation. Moreover, where there are resource constraints on the aggregation process, one must also consider what balance between volume of eviden

Item Type: Article
Official URL:
Additional Information: © 2017 The Authors © CC BY 4.0
Divisions: Philosophy, Logic and Scientific Method
Subjects: B Philosophy. Psychology. Religion > B Philosophy (General)
H Social Sciences > H Social Sciences (General)
Date Deposited: 23 Jan 2018 11:50
Last Modified: 30 May 2024 00:04

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