Fairness and Transparency of Machine Learning for Trustworthy Cloud Services
By Nuno Antunes, Leandro Balby, F. Figueiredo, Nuno Lourenço, Wagner Meira Jr, W. Santos. 2018
26 citations recorded by the source corpus.
Machine learning is nowadays ubiquitous, providing mechanisms for supporting decision making that leverages big data analytics. However, this recent rise in importance of machine learning also raises societal concerns about the dependability and trustworthiness of systems which depend on such automated predictions. Within this context, the new general data protection regulation (GDPR) demands that organizations take the appropriate measures to protect individuals' data, and use it in a privacy-preserving, fair and transparent fashion. In this paper we present how fairness and transparency are supported in the ATMOSPHERE ecosystem for trustworthy clouds. For this, we present the scope of fairness and transparency concerns in the project and then discuss the techniques that are being developed to address each of these concerns. Furthermore, we discuss how fairness and transparency are used with other quality attributes to characterize the trustworthiness of cloud systems.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Information Systems
Citations in the corpus
- Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI (2022, background): “Often, the goal has been to attain greater visibility into ML models and datasets by exposing source code [4], contribution trails [7], introducing ML-drive data analysis methods [18], and introducing diverse oversight [17].”