Quality Assessment and Biases in Reused Data

Abstract

This article investigates digital and non-digital traces reused beyond the context of creation. A central idea of this article is that no (reused) dataset is perfect. Therefore, data quality assessment becomes essential to determine if a given dataset is “good enough” to be used to fulfill the users’ goals. Biases, a possible source of discrimination, have become a relevant data challenge. Consequently, it is appropriate to analyze whether quality assessment indicators provide information on potential biases in the dataset. We use examples representing two opposing sides regarding data access to reflect on the relationship between quality and bias. First, the European Union open data portal fosters the democratization of data and expects users to manipulate the databases directly to perform their analyses. Second, online behavioral advertising systems offer individualized promotional services but do not share the datasets supporting their design. Quality assessment is socially constructed, as there is not a universal definition but a set of quality dimensions, which might change for each professional context. From the users’ perspective, trust/credibility stands out as a relevant quality dimension in the two analyzed cases. Results show that quality indicators (whatever they are) provide limited information on potential biases. We suggest that data literacy is most needed among both open data users and clients of behavioral advertising systems. Notably, users must (be able to) understand the limitations of datasets for an optimal and bias-free interpretation of results and decision-making.

Data Reuse in Agricultural Genomics Research: Present Challenges and Future Solutions

“…No dataset is perfect 5,6 , but that does not mean it is not suitable for reuse. As data are made publicly available regardless of the quality metrics, data quality assessment and standardization are important considerations 6 .…”

“…No dataset is perfect 5,6 , but that does not mean it is not suitable for reuse. As data are made publicly available regardless of the quality metrics, data quality assessment and standardization are important considerations 6 . Statisticians are well aware of this issue 36 , which is particularly problematic in the life sciences likely due to the complexity of biological systems, number of variables, and scale of experiments.…”

“…However, no dataset is perfect and data producers can only strive to satisfy the requirements for its initial use and reuse. Some researchers have identified the risks and challenges associated with data reuse in the life sciences 5,6 , which informs agricultural data management 7 , but a detailed assessment of the reuse issue in this area has not been conducted yet.…”

“…Recognizing the value of reusable datasets and ubiquity of FAIR principles might lead one to believe they are universally accepted and applied. However, as any data stakeholder can testify, no dataset is without flaws 6 and a multitude of problems can present themselves to a potential re-user.…”