ACM Transactions on Interactive Intelligent Systems最新文献

Appropriate trust is an important component of the interaction between people and AI systems, in that ‘inappropriate’ trust can cause disuse, misuse or abuse of AI. To foster appropriate trust in AI, we need to understand how AI systems can elicit appropriate levels of trust from their users. Out of the aspects that influence trust, this paper focuses on the effect of showing integrity. In particular, this paper presents a study of how different integrity-based explanations made by an AI agent affect the appropriateness of trust of a human in that agent. To explore this, (1) we provide a formal definition to measure appropriate trust, (2) present a between-subject user study with 160 participants who collaborated with an AI agent in such a task. In the study, the AI agent assisted its human partner in estimating calories on a food plate by expressing its integrity through explanations focusing on either honesty, transparency or fairness. Our results show that (a) an agent who displays its integrity by being explicit about potential biases in data or algorithms achieved appropriate trust more often compared to being honest about capability or transparent about the decision-making process, and (b) subjective trust builds up and recovers better with honesty-like integrity explanations. Our results contribute to the design of agent-based AI systems that guide humans to appropriately trust them, a formal method to measure appropriate trust, and how to support humans in calibrating their trust in AI.

In Explainable Artificial Intelligence (XAI) research, various local model-agnostic methods have been proposed to explain individual predictions to users in order to increase the transparency of the underlying Artificial Intelligence (AI) systems. However, the user perspective has received less attention in XAI research, leading to a (1) lack of involvement of users in the design process of local model-agnostic explanations representations and (2) a limited understanding of how users visually attend them. Against this backdrop, we refined representations of local explanations from four well-established model-agnostic XAI methods in an iterative design process with users. Moreover, we evaluated the refined explanation representations in a laboratory experiment using eye-tracking technology as well as self-reports and interviews. Our results show that users do not necessarily prefer simple explanations and that their individual characteristics, such as gender and previous experience with AI systems, strongly influence their preferences. In addition, users find that some explanations are only useful in certain scenarios making the selection of an appropriate explanation highly dependent on context. With our work, we contribute to ongoing research to improve transparency in AI.

Word vector embeddings have been shown to contain and amplify biases in the data they are extracted from. Consequently, many techniques have been proposed to identify, mitigate, and attenuate these biases in word representations. In this paper, we utilize interactive visualization to increase the interpretability and accessibility of a collection of state-of-the-art debiasing techniques. To aid this, we present the Visualization of Embedding Representations for deBiasing (“VERB”) system, an open-source web-based visualization tool that helps users gain a technical understanding and visual intuition of the inner workings of debiasing techniques, with a focus on their geometric properties. In particular, VERB offers easy-to-follow examples that explore the effects of these debiasing techniques on the geometry of high-dimensional word vectors. To help understand how various debiasing techniques change the underlying geometry, VERB decomposes each technique into interpretable sequences of primitive transformations and highlights their effect on the word vectors using dimensionality reduction and interactive visual exploration. VERB is designed to target natural language processing (NLP) practitioners who are designing decision-making systems on top of word embeddings, and also researchers working with the fairness and ethics of machine learning systems in NLP. It can also serve as a visual medium for education, which helps an NLP novice understand and mitigate biases in word embeddings.