text: 24,19,1487451884701073411,801793180899360768,Hendrik Schuff,"Check out our new paper! (w/ @alon_jacovi, Heike Adel, @yoavgo and Thang Vu) Human Interpretation of Saliency-based Explanation Over Text <LINK> We investigate human perception of heat map explanations and find that intuitive understanding is biased. 1/5 <LINK> Saliency attribution methods score how important parts of a model’s input are to the model decision and are often visualised using heat maps. Previous work focused on developing and verifying attribution methods. Less is known about how humans interpret these explanations. 2/5 We conduct various user studies to investigate whether superficial and unrelated factors (e.g., word length) influence human self-reported importance ratings. We collect user feedback and statistically analyse importance ratings using a GAMM model. 3/5 <LINK> We find that numerous factors such as word length, sentence length and learning effects affect human importance ratings. These factors shouldn't affect importance, because the explanation already objectively has importance, but they do. 4/5 <LINK> We present two bias correction methods and demonstrate their ability to compensate the distorting influence of word length and repeated exposure. Details are in paper. Thanks! 5/5 <LINK>",http://arxiv.org/abs/2201.11569,"While a lot of research in explainable AI focuses on producing effective explanations, less work is devoted to the question of how people understand and interpret the explanation. In this work, we focus on this question through a study of saliency-based explanations over textual data. Feature-attribution explanations of text models aim to communicate which parts of the input text were more influential than others towards the model decision. Many current explanation methods, such as gradient-based or Shapley value-based methods, provide measures of importance which are well-understood mathematically. But how does a person receiving the explanation (the explainee) comprehend it? And does their understanding match what the explanation attempted to communicate? We empirically investigate the effect of various factors of the input, the feature-attribution explanation, and visualization procedure, on laypeople's interpretation of the explanation. We query crowdworkers for their interpretation on tasks in English and German, and fit a GAMM model to their responses considering the factors of interest. We find that people often mis-interpret the explanations: superficial and unrelated factors, such as word length, influence the explainees' importance assignment despite the explanation communicating importance directly. We then show that some of this distortion can be attenuated: we propose a method to adjust saliencies based on model estimates of over- and under-perception, and explore bar charts as an alternative to heatmap saliency visualization. We find that both approaches can attenuate the distorting effect of specific factors, leading to better-calibrated understanding of the explanation. ",Human Interpretation of Saliency-based Explanation Over Text,5,"['Check out our new paper! (w/ @alon_jacovi, Heike Adel, @yoavgo and Thang Vu)\n\nHuman Interpretation of Saliency-based Explanation Over Text\n<LINK>\n\nWe investigate human perception of heat map explanations and find that intuitive understanding is biased.\n\n1/5 <LINK>', 'Saliency attribution methods score how important parts of a model’s input are to the model decision and are often visualised using heat maps.\nPrevious work focused on developing and verifying attribution methods. Less is known about how humans interpret these explanations.\n\n2/5', 'We conduct various user studies to investigate whether superficial and unrelated factors (e.g., word length) influence human self-reported importance ratings.\nWe collect user feedback and statistically analyse importance ratings using a GAMM model.\n\n3/5 https://t.co/sTaHj1CdOi', ""We find that numerous factors such as word length, sentence length and learning effects affect human importance ratings. These factors shouldn't affect importance, because the explanation already objectively has importance, but they do.\n\n4/5 https://t.co/qnz5PlqZh8"", 'We present two bias correction methods and demonstrate their ability to compensate the distorting influence of word length and repeated exposure.\nDetails are in paper. Thanks!\n\n5/5 https://t.co/MHk36SNAPt']",22,01,1234