Explainable AI is important to build trustworthy and understandable systems. To aid understanding, explanations need to be relatable, but current techniques remain overly technical with obscure information. Drawing from the …
Just as AI performance degrades with data corruptions, we found that so does explanation faithfulness. For example, blurring an image can provide privacy, but this leads to heatmap explanations becoming …
Previously, we improved creativity by automatically directing the crowd with diverse prompts Directed Diversity (Cox et al., CHI’21). Here, we further improve creativity by providing real-time feedback on the ideations. …
The successful deployment of artificial intelligence (AI) in many domains from healthcare to hiring requires their responsible use, particularly in model explanations and privacy. Explainable artificial intelligence (XAI) provides more …
Crowd sourcing can obtain many creative ideas, but without careful coordination, many ideators may generate the same ideas. This leads to redundancy and limits the diversity of ideas from the …
Explanations of artificial explanations can be simplified by controlling the number of variables, but the complexity in how they are visualized can still impede quick interpretation. We quantitatively modeled the …
OD Bundling is a popular technique to visualize key patterns in movement flows, but the curves illustrated can be misleading by suggesting paths that do not match true or plausible …
We will be presenting our work on the conceptual XAI Framework of Reasoned Explanations at CHI 2019 next week on Tuesday! Come to our talk if you are in Glasgow! …
Which explanations should AI provide? We identified pathways to tailor explanation techniques based on theories of human reasoning processes. Our paper on a framework for human-centric explanations for XAI has …
Most food logging apps help individuals to track their diets and hopefully eat more healthily. Even with some social sharing features, this is quite a lonely activity. Yet social support …