Drawing on information processing theory and resource-based view (RBV), this study
examines the impact of industry 4.0 on supply chain performance (SCP). The study also …

We propose measurement modeling from the quantitative social sciences as a framework for
understanding fairness in computational systems. Computational systems often involve …

Forming a reliable judgement of a machine learning (ML) model's appropriateness for an
application ecosystem is critical for its responsible use, and requires considering a broad …

Large language models produce human-like text that drive a growing number of
applications. However, recent literature and, increasingly, real world observations, have …

The use of machine learning to guide clinical decision making has the potential to worsen
existing health disparities. Several recent works frame the problem as that of algorithmic …

Accountability is widely understood as a goal for well governed computer systems, and is a
sought-after value in many governance contexts. But how can it be achieved? Recent work …

There has been a surge of recent interest in sociocultural diversity in machine learning
research. Currently, however, there is a gap between discussions of measures and benefits …

This paper provides an empirical and conceptual account on seeing machine learning
models as part of a sociotechnical system to identify relevant vulnerabilities emerging in the …

Computer scientists are trained in the art of creating abstractions that simplify and
generalize. However, a premature abstraction that omits crucial contextual details creates …

Machine learning algorithms are increasingly used to shape high-stake allocations, sparking
research efforts to orient algorithm design towards ideals of justice and fairness. In this …