The black-box nature of current artificial intelligence (AI) has caused some to question
whether AI must be explainable to be used in high-stakes scenarios such as medicine. It has …

Artificial intelligence is similar to human intelligence, and robots in organisations always
perform human tasks. However, AI encounters a variety of biases during its operational …

Machine learning is increasingly used to inform decision making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …

Rising concern for the societal implications of artificial intelligence systems has inspired a
wave of academic and journalistic literature in which deployed systems are audited for harm …

In this provocative, consequential book, Couldry and Mejias theorize the dynamics of
change in contemporary capitalism as grounded in a new form of data colonialism. They …

As machine learning black boxes are increasingly being deployed in domains such as
healthcare and criminal justice, there is growing emphasis on building tools and techniques …

Explainable machine learning offers the potential to provide stakeholders with insights into
model behavior by using various methods such as feature importance scores, counterfactual …

Game-theoretic formulations of feature importance have become popular as a way to"
explain" machine learning models. These methods define a cooperative game between the …

Recent work on interpretability in machine learning and AI has focused on the building of
simplified models that approximate the true criteria used to make decisions. These models …

Big Data analytics and artificial intelligence (" Al") draw non-intuitive and unverifiable
inferences and predictions about the behaviors, preferences, and private lives of individuals …