https://arxiv.org/abs/2209.13578 Artificial intelligence (AI) systems are increasingly used for providing advice to facilitate human decision making. While a large body of work has explored how AI systems can be optimized to produce accurate and fair advice and how algorithmic advice … Continued
Explanations have been framed as an essential feature for better and fairer human-AI decision-making. In the context of fairness, this has not been appropriately studied, as prior works have mostly evaluated explanations based on their effects on people’s perceptions. We … Continued
https://psyarxiv.com/yahvf/ Abstract Many online content providers use “personalization” algorithms to generate recommendations that are individually fine-tuned for users’ interests. However, these algorithms have also been criticized because tailoring content to specific users necessarily restricts the diversity of content, leading to … Continued
Artificial Intelligence (AI) covers a broad spectrum of computational problems and use cases. Many of those implicate profound and sometimes intricate questions of how humans interact or should interact with AIs. Moreover, many users or future users do have abstract … Continued
https://psyarxiv.com/qhwvx/ Designing trustworthy algorithmic decision-making systems is a central goal in system design. Additionally, it is crucial that external parties can adequately assess the trustworthiness of systems. Ultimately, this should lead to calibrated trust: trustors adequately trust and distrust the … Continued
https://psyarxiv.com/qv3ya/ Abstract To understand how human experiences emerge, change and are interlinked requires understanding which conditions are necessary for thoughts, feelings and behaviors. Linear models which are currently the default for statistical analyses in psychological research do not allow to … Continued
https://psyarxiv.com/jymtu/ Abstract Psychological science moves towards specification of effect sizes in formulating hypotheses, performing power-analyses, and when consideration of the relevance of findings. This development has sparked an appreciation for the wider context in which such effect sizes are found, … Continued
Many high-stake decisions follow an expert-in-loop structure in that a human operator receives recommendations from an algorithm but is the ultimate decision maker. Hence, the algorithm’s recommendation may differ from the actual decision implemented in practice. However, most algorithmic recommendations … Continued
Explaining artificial intelligence (AI) predictions is increasingly important and even imperative in many high-stakes applications where humans are the ultimate decision-makers. In this work, we propose two novel architectures of self-interpretable image classifiers that first explain, and then predict (as … Continued
Deep generative models have the potential to fundamentally change the way we create high-fidelity digital content but are often hard to control. Prompting a generative model is a promising recent development that in principle enables end-users to creatively leverage zero-shot … Continued