https://arxiv.org/abs/2109.07239 Pandemics and natural disasters over the years have changed the behavior of people, which has had a tremendous impact on all life aspects. With the technologies available in each era, governments, organizations, and companies have used these technologies to … Continued
Large language models can be used for collaborative storytelling. In this work we report on using GPT-3 \cite{brown2020language} to co-narrate stories. The AI system must track plot progression and character arcs while the human actors perform scenes. This event … Continued
http://arxiv.org/abs/2110.00840 Francis Bacon popularized the idea that science is based on a process of induction by which repeated observations are, in some unspecified way, generalized to theories based on the assumption that the future resembles the past. This … Continued
As a technical sub-field of artificial intelligence (AI), explainable AI (XAI) has produced a vast collection of algorithms in recent years. However, explainability is an inherently human-centric property and the field is starting to embrace inter-disciplinary perspectives and human-centered approaches. … Continued
Missing data are ubiquitous in the era of big data and, if inadequately handled, are known to lead to biased findings and have deleterious impact on data-driven decision makings. To mitigate its impact, many missing value imputation methods have been … Continued
Departing from the claim that AI needs to be trustworthy, we find that ethical advice from an AI-powered algorithm is trusted even when its users know nothing about its training data and when they learn information about it that warrants … Continued
When machine-learning algorithms are deployed in high-stakes decisions, we want to ensure that their deployment leads to fair and equitable outcomes. This concern has motivated a fast-growing literature that focuses on diagnosing and addressing disparities in machine predictions. However, many … Continued