Human-Centered Evaluation of Explanations (2022)

Abstract: The NLP community are increasingly interested in providing explanations for NLP models to help people make sense of model behavior and potentially improve human interaction with models. In addition to computational challenges in generating these explanations, evaluations of the generated explanations require human-centered perspectives and approaches. This tutorial will provide an overview of human-centered evaluations of explanations. First, we will give a brief introduction to the psychological foundation of explanations as well as types of NLP model explanations and their corresponding presentation, to provide the necessary background. We will then present a taxonomy of human-centered evaluation of explanations and dive into depth in the two categories: 1) evaluation based on human-annotated explanations; 2) evaluation with human-subjects studies. We will conclude by discussing future directions. We will also adopt a flipped format to maximize the in- teractive components for the live audience.

## tutorial description thanks to recent advances in deep learning and large-scale pretrained language models, nlp systems have demonstrated impressive performance in a wide variety of tasks, ranging from classification to generation. however, in order to effectively use these nlp systems in support of human endeavour, it is critical that we can explain model predictions in ways that humans can easily comprehend. such explanations are particularly important for highstakes decisions such as hiring and loan approval. indeed, the nlp community have developed a battery of algorithms and models for explaining model predictions and there have been past tutorials dedicated to such algorithms @xcite . however, there is less consensus on how to evaluate explanations. and, since these explanations eventually serve the needs of humans, it is important to take a human-centered approach to their evaluation, meaning evaluating with respect to human criteria, measuring human perceptions of explanations and whether explanations serve human needs. therefore, interdiscplinary perspectives are necessary for the success of such evaluations, especially ones from psychology and hci, which is unfamiliar to the nlp community. this tutorial aims to fill in this gap and introduce the nascent area of human-centered evaluation of explanations. this tutorial will first present the psychological and philosophical foundations of explanations. we will highlight that explanations are heterogeneous and selective. we will discuss diverse goals people seek explanations for, highlighting that effective explanations identify a difference maker, which is often causal. these discussions will lay the foundation for the rest of the tutorial. we will then introduce the basic elements of explanations and their presentation, including explanation types and taxonomies, so that participants are familiar with the subject of evaluation. we will proceed with a taxonomy of human-centered evaluations, to include two primary types: applicationgrounded human-subjects evaluations and evaluations based on human-provided explanations. we start with how to conduct human-subjects studies to evaluate explanations. we would like to encourage nlp researchers to move beyond using simplified evaluation tasks, to considering different usage scenarios of explantions and articulating evaluation goals-for whom and what purposes a given explanation method is meant to serve, then define the evaluation task, evaluation criteria, and recruiting requirement accordingly. we will also describe common methods to measure different evaluation criteria, such as survey scales and behavioral measurement, while raising limitations of existing methods. then, we cover evaluations with evaluation based on human-annotated explanations, as this area is more familiar with the nlp audience. this family of evaluations involves collecting explanations alongside ground-truth labels, and using these human-annotated explanations as a gold standard for model-generated explanations. while intuitive, this practice has validity issues associated with misalignment between human reasoning and model behavior, which we will discuss at length. we will conclude the tutorial with a discussion of future directions for human-centered evaluation of explanations. flipped format. our tutorial will be in a flipped format: participants view the videos asynchronously and participate in q&a and work through hands-on activities. the flipped classroom has shown better retention than traditional instruction in a stand-alone instruction session @xcite . we believe the flipped format is also condusive for acl tutorials: (1) it will have more longevity, as the recorded (and edited) videos will be of higher quality than videos recorded at a typical conference session; (2) it will be easier for hybrid participation; (3) it will be a more engaging experience for in-person participants. all of the videos will be in segments of twenty minutes or less for easy asynchronous viewing. to ensure accessibility, we will have manual (not asr) captions and distribute the slide source along with the videos for easier incorporation of the tutorial into classroom instruction. ## outline of the tutorial content and reading list the tutorial will consiste of two parts: (1) (offline) two hours of content to be viewed asynchronously and (2) (online or in-person) three hours of q&a and hands-on activities. we include the cited references in the outline description. ## expected outcome we plan to make tutorial presentation materials public. we will make sure the videos are accessible to a wide population, e.g., via transcripts. estimated audience size. we estimate that ∼200 people will attend the tutorial. the algorithmic counterpart, @xcite , was one of the most popular tutorials at emnlp that year. ## diversity considerations our speakers are diverse in discipline (nlp, hci, and psychology), gender (4 male, 3 female), seniority (from professor to postdocs), academia and industry (5 from acadmia, 2 from industry). our flipped format will accomodate a diverse group of audience because of its asychronous nature. for example, non-native speakers have more time to digest the content. we also require a low barrier of entry. to further attract a diverse group of participants, we will advertise this through underrepresented groups such as women in nlp, black in ai, and queer in ai. ## presenter biographies jordan boyd-graber is an associate professor at the university of maryland, with joint appointments between computer science, the ischool, lan-guage science, and the institute for advanced computer studies. he has been teaching using a flipped classroom approach since 2013. he and his collaborators helped end the use of perplexity for topic models @xcite , first developed interactive topic models @xcite , and improved word-level analysis of topic model explanations @xcite . additional information at: http://boydgraber.org . samuel carton is a postdoctoral researcher at the university of colorado, boulder. his interests lie in model interpretability and human-ai interaction. additional information at: https: //shcarton.github.io . shi feng is a postdoctoral researcher at the uhiversity of chicago. his research interests include interpretable nlp, adversarial robustness, and alignment. additional information at: http: //www.shifeng.umiacs.io/ . q. vera liao is a principal researcher at microsoft research montreal, where she is part of the fate (fairness, accountability, transparency, and ethics) group. she is an hci researcher by training, with current interest in human-ai interaction and explainable ai. more information can be found at: http://www.qveraliao.com/ tania lombrozo is the arthur w. marks '19 professor of psychology at princeton university. she is a leading expert in understanding explanations. additional information is available at: http://cognition.princeton.edu/ . alison smith-renner is a senior research scientist in human-ai interaction at dataminr. her research interests include explainable and interactive natural language processing from a human-centric perspective. additional information is available at: https://alisonmsmith.github.io chenhao tan is an assistant professor of computer science at the university of chicago, and is also affiliated with the harris school of public policy. his research interest includes natural language processing, human-centered ai, and computational social science. additional information is available at: https://chenhaot.com 6 technical requirements for the in-person tutorial, we request roundtables so that participants can discuss together during the q&a and the workshop activities; it would be good to have power outlets around the tables.