A Human-Centric Evaluation Platform for Explainable Knowledge Graph Completion (2024)

Abstract: Explanations for AI are expected to help human users understand AI-driven predictions. Evaluating plausibility, the helpfulness of the explanations, is therefore essential for developing eXplainable AI (XAI) that can really aid human users. Here we propose a human-centric evaluation platform to measure plausibility of explanations in the context of eXplainable Knowledge Graph Completion (XKGC). The target audience of the platform are researchers and practitioners who want to 1) investigate real needs and interests of their target users in XKGC, 2) evaluate the plausibility of the XKGC methods. We showcase these two use cases in an experimental setting to illustrate what results can be achieved with our system.

## introduction a knowledge graph (kg) is a structured representation of knowledge that captures the relationships between entities. it is composed of triples in the format @xcite , denoted as @xmath0, r, o), where two entities are connected by a specified relation. for example, in the triple (london, iscapitalof, uk), london and uk are the entities, and iscapitalof is the relation. these entities can be depicted as nodes in a knowledge graph, while the relation denotes a labeled link connecting the subject to the object. knowledge graphs are beneficial for many nlp tasks, e.g., fact checking @xcite ), question answering @xcite and information extraction @xcite . the applicability of kgs in downstream tasks, however, is often limited by their incompleteness @xcite : they do not contain exhaustive information about all relationships between figure 1 : an example explaining a predicted triple (in red) with important training triples (in blue), learned according to gradients by @xcite . they are faithful, yet not helpful for users to understand the prediction. the defined entities @xcite . to address this issue, researchers and practitioners have worked on knowledge graph completion (kgc): the task of predicting new relationships between the entities in the knowledge graph. for this, two parts of a triple (i.e., slots) are given to a kgc system @xcite ; @xcite , inter alia) and the third is inferred; e.g., answering the query @xmath1, r, ?). such methods learn low-dimensional representations of entities and relations for predictive inference. the embedding based kgc models, however, are black boxes that do not (and cannot) provide explanations of why the model makes a certain prediction. the lack of transparency significantly hampers users' trust and engagement with kgc systems, especially in the high-risk domains, such as medicine @xcite . to provide explanations for such embedding-based kgc systems, researchers have proposed explainable kgc (xkgc) methods @xcite @xcite . however, it remains unexplored how helpful users find the explanations provided by these methods. for instance, figure 1 shows an example explanation that would not be helpful for the end user. we thus target to evaluate what kind of explanations are helpful for the users because ultimately, the explanations should directly aid them. therefore, it is important to measure the plausibility of the explanations: the extent to which an explanation generated by xai is comprehensible and beneficial to human users @xcite . thus, to evaluate the plausibility of the explanations, we present a human-centric platform illustrated in figure 2 . our evaluation platform offers the following novel contributions. first, it introduces a new evaluation paradigm that assesses how well explanations can assist users in judging the correctness of kgc predictions. in contrast to the prevalent human evaluation paradigm in the literature that requests annotators to simulate ai's behavior @xcite @xcite , the new paradigm aligns better with real human-ai interaction systems, where ais facilitate humans rather than the other way around. furthermore, given the growing complexity of ai, it becomes increasingly challenging for annotators to imitate ai's behavior without comprehensive training, especially when utilizing crowdsourcing platforms like amazon mechanical turk (amt) @xcite . another notable advantage of our system is its capability to quantify the helpfulness of explanations in an objective manner. our system suggests metrics, such as the accuracy rate of annotators' judgments, which stems itself from well-defined ground truth to quantitatively measure human feedback. with these novel contributions, our evaluation platform can effectively measure plausibility of xkgc methods. considering the diversity of humans, our system also provides various statistical tools to rigorously and comprehensively analyze the collected feedback for reliable conclusions. additionally, our evaluation platform aids in identifying genuine requirements from users regarding explanations, thereby it can assist in developing and refining xkgc methods to generate explanations that are centered around human needs. finally, we formulate our study on human-centric evaluation as practical guidelines, which can be replicated to design evaluations for other use cases in the future. ## human centric evaluation for xkgc we build an online system to evaluate xkgcs in a human centric manner. our system considers the real needs and interests of human users in collaboration with ai, allowing us to investigate: can humans assess correctness of a kgc prediction based on its explanations? which explanations are helpful for human users? the answers to these questions provide hints for evaluating the ultimate goal of an xai method: the generated explanations are expected to assist human users in understanding ai-driven predictions. to this end, our platform has two user views: one for researchers to set up a test and the other for testers to give feedback. ## architecture of the evaluation system the system is as a web application consisting of frontend (html5/javascript) and backend (python). we will describe the respective components and the data flow (shown in figure 5 ) in detail. ## statistical analysis of human feedback due to the complexity and costliness of human evaluation, as well as the diversity among human testers, the collected feedback tends to be both limited in quantity and diverse in quality. consequently, statistical analysis assumes a critical role to draw reliable conclusions from human feedback. we include the following statistical analysis tools in our platform. power analysis. in human evaluation, there is often an important question: how many testers are necessary to draw a solid conclusion? there is no a universally applicable minimum sample size for obtaining statistically significant results @xcite . power analysis is commonly used in e.g., social-science and clinical research literature @xcite , which determines an adequate sample size for a human evaluation based on a stated effect size that defines the difference level of the compared methods. as the effect size used in power analysis is prospectively anticipated before the evaluation by the researchers, it is good to analyze the post hoc power of an observed effect size derived from the collected human feedback, especially if the findings are non-significant @xcite . ## guidelines for human-centric evaluation human evaluation can be subject to various biases that may affect the reliability of the conclusions @xcite @xcite . the following concerns need to be addressed. engagement. testers often exhibit varying levels of engagement and various thinking modes. to mitigate the impact of tester bias, we propose that each tester @xmath2, analyzing the feedback with paired tests, especially when the number of available testers is limited. additionally, testers' engagement tends to decrease over time. therefore, it is crucial to impose a constraint on the total evaluation time (e.g. one hour per session). furthermore, to ensure the testers' proper engagement during the evaluation process, we can randomly assign some straightforward predictions as checkpoints for validation. equivalency. all testers should evaluate similar set of predictions in a similar order. this is to reduce deviations caused by individual predictions. diversity. testers may have the tendency to retain information from previous predictions, which can result in the earlier assessments influencing the later ones. consequently, we recommend selecting predictions that are as distinct from each other as possible to mitigate this concern. balance. predictions should be balanced. specifically, numbers of correct and erroneous predictions should be similar, and the order of predictions should be random, such that testers cannot simply guess prediction results. human-understandable benchmark data. the data used in a human evaluation needs to be human understandable, otherwise testers have no clue how to assess predictions and explanations. while a seemingly obvious statement, in practice we found it difficult to find kgc data that satisfies this constraint. in addition, testers recruited for a human evaluation are often lay people, not professionals of an area, thus plain datasets without domain-specific knowledge (such as biology and healthcare) would be better. if the evaluated xkgcs are domain specific, e.g., disease diagnosis, then specialists should accordingly be employed. ## experimental study to demonstrate what results and findings can be acquired with the proposed system, we conducted two evaluations. ## related work human evaluation has attracted increasing attention in xai research due to its ultimate goal of aiding human to understand ai predictions. many evaluation benchmarks are based on simulatability @xcite : how well human can simulate ai with help of explanations. for instance, @xcite employed forward simulation to evaluate attribute-based xai methods for text classification. @xcite extended the simulation test with counterfactual simulation to compare different types of explanations for text and tabular data. @xcite executed in-depth analysis of simulation tests for explanations of review classification. in addition, there are other human evaluations for xai outside of nlp. for example, @xcite proposed a decision-making based evaluation to measure human performance on decisions given predictions and explanations. more human evaluation tests can be found in the surveys e.g. @xcite . however the literature lacks a human evaluation tool to facilitate researchers on human-centric evaluation of kgc explanations. existing kgc evaluation platforms focus on measurement of prediction performance. for instance, @xcite proposed a reconsideration of the used metrics by creating a "complete" judgement set inspired by evaluation of information retrieval. @xcite proposed the use of unit tests in order to evaluate models in a fine-grained manner by considering different capabilities. @xcite provided refined performance evaluation by bucketizing the test set into user-specified chunks. to bridge the gap, our platform provides a tool to measure plausibility of kgc explanations with human evaluation. ## conclusion ai explanations only achieve their goal if the explanation is helpful to the human user. to measure this, we present a human-centric evaluation platform in the context of explainable knowledge graph completion. distinguishing from the simulatabilitybased evaluation, our system assesses how well explanations assist users in judging the correctness of kgc predictions, and thus aligns better with human-ai interaction systems, where ai facilities humans rather than the other way around. to alleviate possible biases, we provide a set of guidelines in experiment design, and diverse analysis tools for reliable conclusions. the experiments demonstrate the findings and results that can be acquired with the proposed system.