Fifty Shades of Grey

Abstract

Environmental data science is uniquely placed to respond to essentially complex and fantastically worthy challenges related to arresting planetary destruction. Trust is needed for facilitating collaboration between scientists who may share datasets and algorithms, and for crafting appropriate science-based policies. Achieving this trust is particularly challenging because of the numerous complexities, multi-scale variables, interdependencies and multi-level uncertainties inherent in environmental data science. Virtual Labs-easily accessible online environments provisioning access to datasets, analysis and visualisations-are socio-technical systems which, if carefully designed, might address these challenges and promote trust in a variety of ways. In addition to various system properties that can be utilised in support of effective collaboration, certain features which are commonly seen to benefit trust-transparency and provenance in particular-appear applicable to promoting trust in and through Virtual Labs. Attempting to realise these features in their design reveals, however, that their implementation is more nuanced and complex than it would appear. Using the lens of affordances, we argue for the need to carefully articulate these features, with consideration of multiple stakeholder needs on balance, so that these Virtual Labs do in fact promote trust. We argue that these features not be conceived as widgets that can be imported into a given context to promote trust; rather, whether they promote trust is a function of how systematically designers consider various (potentially conflicting) stakeholder trust needs. CCS CONCEPTS• Human-centered computing → Computer supported cooperative work; HCI theory, concepts and models.

Accountable AI for Healthcare IoT Systems

“…Along similar lines, the term trustworthiness is used as a means of warranting trust by providing formal statements on the quality of the systems [26]. Thus the two major factors influencing the trustworthiness of an AI system are the performance of the system and the verification mechanisms referred to as accountability in our conceptual mapping depicted in Figure 3 [27].…”

Providing assurance and scrutability on shared data and machine learning models with verifiable credentials

“…In order to gain confidence in the quality and suitability of a shared dataset or machine learning (ML) model, potential users need to be able to develop and maintain trust in claims made by the originators of the asset under consideration. Thornton et al 1 have, for example, considered challenges faced in progressing research in the environmental science field. In seeking to maximize collaboration and adoption of shared data resources in a science gateway, Thornton and colleagues identify the pivotal role played by trust between participants in the community, and the importance participants placed on trusting the quality of the work of their peers.…”