Artificial intelligence (AI) is becoming increasingly important for the public sector. As the
number of studies in this field increases, this study provides a systematic overview of the …

In this paper, I distinguish three types of harm that can arise in the context of artificial
intelligence (AI): individual harm, collective harm and societal harm. Societal harm is often …

Responding to growing criticism that the use of artificial intelligence in public services
reinforces unethical activities such as discrimination, the paper presents two new cases from …

This paper introduces reviewability as a framework for improving the accountability of
automated and algorithmic decisionmaking (ADM) involving machine learning. We draw on …

Technology adoption is crucial to economic growth, yet levels of technology adoption vary,
with limited adoption in many countries. Countries wield considerable technology adoption …

Contemporary social scientific scholarship is being transformed by the challenges
associated with the changing nature of, and responses to, questions of crime, security and …

This research proposes a framework for the negative impacts of artificial intelligence (AI) in
government by classifying 14 topics of its dark side into five socio technical categories. The …

Arguments in favor of tempering algorithmic decision making with human judgment often
appeal to concepts and criteria derived from legal philosophy about the nature of law and …

The mounting evidence of unintended harmful social consequences of automated
algorithmic decision-making (AADM), powered by AI and big data, in transformative services …

In this article, we focus on the growing evidence of unintended harmful societal effects of
automated algorithmic decision-making in transformative services (eg social welfare …