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Recently we see a rising number of methods in the field of eXplainable Artificial Intelligence. To our surprise, their development is driven by model developers rather than a study of needs for human end users. The analysis of needs, if … Continued

State of the art Artificial Intelligence (AI) techniques have reached an impressive complexity. Consequently, researchers are discovering more and more methods to use them in real-world applications. However, the complexity of such systems requires the introduction of methods that make … Continued

https://arxiv.org/pdf/2107.13509.pdf Explainability of AI systems is critical for users to take informed actions and hold systems accountable. While “opening the opaque box” is important, understanding who opens the box can govern if the Human-AI interaction is effective. In this paper, … Continued

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

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