Blinding as a Solution to Institutional Corruption
Author: Christopher T. Robertson
Publisher:
Published: 2013
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOKThis Working Paper provides a framework for understanding how blinding may be a solution for institutional corruption, a situation where a funding dependency causes biased outcomes and thus a lack of trust. A funding dependency can be disaggregated into three functions: a subsidy, a selection of the decision maker, and an identification between the funder and the decision maker. These dynamics are shown in the settings of litigation witnesses, biomedical scientists, and candidates for public offices, all of whom may be biased by funding dependencies. Drawing from a long history of blinding in the biomedical sciences, blinding operates by allowing the subsidy function, while eliminating the selection and identification functions, which cause bias. In some settings, even the funders themselves will have self-interested reasons to prefer blinding over the status quo, which suggests the potential for market-based solutions to institutional corruption. Blinding is motivated by a recognition that a subsidy-dependency is sometimes unavoidable for practical reasons (including constitutional, political, economic realities), making a ban on conflicting interests unattainable. Blinding is also motivated by a recognition that other solutions to institutional corruption, such as professionalism and mandatory disclosure of conflicting interests, require strong assumptions about psychological capacities, which often fail. Blinding has its own limitations: some biasing functions cannot be disaggregated from the subsidy, and even if blinding works to eliminate bias, it may fail to rescue a dependent institution from perceptions of illegitimacy. Still, blinding should be understood as a primary tool in the fight against institutional corruption. The Working Paper concludes by showing how blinding strategies are a primary mechanism of the civil and criminal jury trial institution, which suggests other similar applications to minimize bias in other institutions.
