Despite the growing consensus that stakeholders affected by AI systems should participate
in their design, enormous variation and implicit disagreements exist among current …

Machine learning (ML) depends on data to train and verify models. Very often, organizations
outsource processes related to data work (ie, generating and annotating data and …

Child welfare agencies across the United States are turning to data-driven predictive
technologies (commonly called predictive analytics) which use government administrative …

In recent years, discussions of responsible AI practices have seen growing support for
'participatory AI'approaches, intended to involve members of the public in the design and …

The topic of algorithmic fairness is of increasing importance to the Human–Computer
Interaction research community following accumulating concerns regarding the use and …

In this paper we make the case for an expanded understanding of transparency. Within the
now extensive FAccT literature, transparency has largely been understood in terms of …

Ethical principles for algorithms are gaining importance as more and more stakeholders are
affected by" high-risk" algorithmic decision-making (ADM) systems. Understanding how …

Past research has demonstrated that the explicit use of protected attributes in machine
learning can improve both performance and fairness. Many machine learning algorithms …

There is a fast-growing literature in addressing the fairness of AI models (fair-AI), with a
continuous stream of new conceptual frameworks, methods, and tools. How much can we …

The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace,
making it difficult for novel researchers and practitioners to have a bird's-eye view picture of …