Information visualization designers strive to design data displays that allow for efficient
exploration, analysis, and communication of patterns in data, leading to informed decisions …

In the face of complex decisions, people often engage in a three-stage process that spans
from (1) exploring and analyzing pertinent information (intelligence);(2) generating and …

Algorithmic transparency entails exposing system properties to various stakeholders for
purposes that include understanding, improving, and contesting predictions. Until now, most …

Calls for heightened consideration of fairness and accountability in algorithmically-informed
public decisions-like taxation, justice, and child protection-are now commonplace. How …

Several strands of research have aimed to bridge the gap between artificial intelligence (AI)
and human decision-makers in AI-assisted decision-making, where humans are the …

The increasing use of data-driven decision support systems in industry and governments is
accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of …

Clinical needs and technological advances have resulted in increased use of Artificial
Intelligence (AI) in clinical decision support. However, such support can introduce new and …

It has been widely suggested that a key goal of visualization systems is to assist decision
making, but is this true? We conduct a critical investigation on whether the activity of …

Automated agents that interact proficiently with people can be useful in supporting, training
or replacing people in complex tasks. The inclusion of people presents novel problems for …

The attraction effect is a well-studied cognitive bias in decision making research, where
one's choice between two alternatives is influenced by the presence of an irrelevant …