SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability

Interpretability of Deep Learning (DL) is a barrier to trustworthy AI. Despite great efforts made by the Explainable AI (XAI) community, explanations lack robustness -- indistinguishable input perturbations may lead to different XAI results. Thus, it is vital to assess how robust DL interpretability is, given an XAI method. In this paper, we identify several challenges that the state-of-the-art is unable to cope with collectively: i) existing metrics are not comprehensive; ii) XAI techniques are highly heterogeneous; iii) misinterpretations are normally rare events. To tackle these challenges, we introduce two black-box evaluation methods, concerning the worst-case interpretation discrepancy and a probabilistic notion of how robust in general, respectively. Genetic Algorithm (GA) with bespoke fitness function is used to solve constrained optimisation for efficient worst-case evaluation. Subset Simulation (SS), dedicated to estimate rare event probabilities, is used for evaluating overall robustness. Experiments show that the accuracy, sensitivity, and efficiency of our methods outperform the state-of-the-arts. Finally, we demonstrate two applications of our methods: ranking robust XAI methods and selecting training schemes to improve both classification and interpretation robustness.Comment: Accepted by the IEEE/CVF International Conference on Computer Vision 2023 (ICCV'23

Linearizability conditions of quasi-cubic systems

In this paper we study the linearizability problem of the two-dimensional complex quasi-cubic system $\dot{z}=z+(zw)^{d}(a_{30}z^{3}+a_{21}z^{2}w+a_{12}zw^2+a_{03}w^{3}),~\dot{w}=-w-(zw)^{d}(b_{30}w^{3}+b_{21}w^{2}z+b_{12}wz^2+b_{03}z^{3})$, where $z, w, a_{ij}, b_{ij}\in \mathbb{C}$ and $d$ is a real number. We find a transformation to change the quasi-cubic system into an equivalent quintic system and then obtain the necessary and sufficient linearizability conditions by the Darboux linearization method or by proving the existence of linearizing transformations

A Tale of Two Villages: Debordering and Rebordering in the Bordered Community Scenic Area

Border is part of the entrenched history and reality of tourist mobility. This study takes the concept of border as the theorical basis to analyze how local borders are produced, developed and transformed in tourism communities. Taking China’s Hongcun Village, a bordered UNESCO World Cultural Heritage Site, and its neighboring community Jicun as the study cases, the authors conducted interviews and observation to explore how local borders are developed. The results show that local borders can be understood from five perspectives in Hongcun Scenic Area: administrative, physical, social-economic, functional and psychological. They are not fixed but interacting with each other and constantly changing. This paper contributes to the literature as it reveals that local borders are always driven by external forces and actors, strongly supported by the market economy. And it conceptualizes borders as processes including bordering, debordering and rebordering, which provides a dynamic perspective to understand tourism impacts

Consequence-based vs. Ethic-based Evaluations? Re-thinking Travel Decision-making amid a Global Pandemic

The global pandemic has put the idea of “travel shaming” under the spotlight—travelers are concerned of being criticized for traveling irresponsibly during the pandemic, hence hesitant to take nonessential travel. However, travel shaming, conceptualized as a type of ethic-based evaluation, has not drawn much attention as consequence-based evaluation (e.g., perceived risks and benefits) in travel-related risk research. This study aims to reveal how different dimensions of risk evaluation influence attitudes and intentions to travel through the lens of the COVID-19 pandemic. Our results show that both consequence-based and ethic-based evaluations play an important role in predicting travelers’ attitudes and intentions to travel during the pandemic. In addition, this study emphasizes that social trust and self-efficacy can exert a significant influence on both consequence-based and ethic-based risk evaluations. Contributions and discussions of this study are provided in closing

Safety Analysis in the Era of Large Language Models: A Case Study of STPA using ChatGPT

Can safety analysis make use of Large Language Models (LLMs)? A case study explores Systems Theoretic Process Analysis (STPA) applied to Automatic Emergency Brake (AEB) and Electricity Demand Side Management (DSM) systems using ChatGPT. We investigate how collaboration schemes, input semantic complexity, and prompt guidelines influence STPA results. Comparative results show that using ChatGPT without human intervention may be inadequate due to reliability related issues, but with careful design, it may outperform human experts. No statistically significant differences are found when varying the input semantic complexity or using common prompt guidelines, which suggests the necessity for developing domain-specific prompt engineering. We also highlight future challenges, including concerns about LLM trustworthiness and the necessity for standardisation and regulation in this domain.Comment: Under Revie

Reachability Verification Based Reliability Assessment for Deep Reinforcement Learning Controlled Robotics and Autonomous Systems

Deep Reinforcement Learning (DRL) has achieved impressive performance in robotics and autonomous systems (RASs). A key impediment to its deployment in real-life operations is the spuriously unsafe DRL policies--unexplored states may lead the agent to make wrong decisions that may cause hazards, especially in applications where end-to-end controllers of the RAS were trained by DRL. In this paper, we propose a novel quantitative reliability assessment framework for DRL-controlled RASs, leveraging verification evidence generated from formal reliability analysis of neural networks. A two-level verification framework is introduced to check the safety property with respect to inaccurate observations that are due to, e.g., environmental noises and state changes. Reachability verification tools are leveraged at the local level to generate safety evidence of trajectories, while at the global level, we quantify the overall reliability as an aggregated metric of local safety evidence, according to an operational profile. The effectiveness of the proposed verification framework is demonstrated and validated via experiments on real RASs.Comment: Submitted, under revie