This paper argues that a range of current AI systems have learned how to deceive humans.
We define deception as the systematic inducement of false beliefs in the pursuit of some …

Importance Artificial intelligence (AI) could support clinicians when diagnosing hospitalized
patients; however, systematic bias in AI models could worsen clinician diagnostic accuracy …

Decision Support Systems (DSS) based on Machine Learning (ML) often aim to assist lay
decision-makers, who are not math-savvy, in making high-stakes decisions. However …

Trust is an important factor in people's interactions with AI systems. However, there is a lack
of empirical studies examining how real end-users trust or distrust the AI system they interact …

As Artificial Intelligence (AI) continues to advance rapidly, it becomes increasingly important
to consider AI's ethical and societal implications. In this paper, we present a bottom-up …

Manual verification has become very challenging based on the increasing volume of
information shared online and the role of generative Artificial Intelligence (AI). Thus, AI …

In this work, we study the effects of feature-based explanations on distributive fairness of AI-
assisted decisions, specifically focusing on the task of predicting occupations from short …

When users perceive AI systems as mindful, independent agents, they hold them
responsible instead of the AI experts who created and designed these systems. So far, it has …

Explainable artificial intelligence (XAI) methods are being proposed to help interpret and
understand how AI systems reach specific predictions. Inspired by prior work on …

Since the explosion in popularity of ChatGPT, large language models (LLMs) have
continued to impact our everyday lives. Equipped with external tools that are designed for a …