Trustworthy AI: A Computational Perspective

AI-empowered consumer behavior analysis for trustworthy track recommendation over musical dance electronic products

-In the digital music era, accurate and trustworthy track recommendations for musical dance electronic products are becoming increasingly important to improve user experiences and attract more consumers. Consumer behavior modeling is critical in user interest learning and has been extensively used in recommender systems to improve recommendation accuracy. This paper proposes a novel AI-empowered consumer behavior analysis method for trustworthy track recommendations over musical dance electronic products. Specifically, we first model consumer behavior by integrating collaborative filtering and a hidden Markov model to capture the key interactive patterns between consumers and musical dance electronic products. Then, we develop a trustworthy track recommendation method based on multi-layer attention representation learning, which leverages scattering transform for audio preprocessing and attention-based independent recurrent neural networks for encoding user preferences and product features. Extensive experiments on real-world datasets demonstrate the superiority of our proposed method in terms of recommendation accuracy and trustworthiness.

An explainable artificial intelligence‐based approach for reliable damage detection in polymer composite structures using deep learning

Artificial intelligence (AI) techniques are increasingly used for structural health monitoring (SHM) of polymer composite structures. However, to be confident in the trustworthiness of AI models, the models must be reliable, interpretable, and explainable. The use of explainable artificial intelligence (XAI) is critical to ensure that the AI model is transparent in the decision‐making process and that the predictions it provides can be trusted and understood by users. However, existing SHM methods for polymer composite structures lack explainability and transparency, and therefore reliable damage detection. Therefore, an interpretable deep learning model based on an explainable vision transformer (X‐ViT) is proposed for the SHM of composites, leading to improved repair planning, maintenance, and performance. The proposed approach has been validated on carbon fiber reinforced polymers (CFRP) composites with multiple health states. The X‐ViT model exhibited better damage detection performance compared to existing popular methods. Moreover, the X‐ViT approach effectively highlighted the area of interest related to the prediction of each health condition in composites through the patch attention aggregation process, emphasizing their influence on the decision‐making process. Consequently, integrating the ViT‐based explainable deep‐learning model into the SHM of polymer composites provided improved diagnostics along with increased transparency and reliability.

Highlights

From Pixels to Principles: A Decade of Progress and Landscape in Trustworthy Computer Vision

The rapid development of computer vision technologies and applications has brought forth a range of social and ethical challenges. Due to the unique characteristics of visual technology in terms of data modalities and application scenarios, computer vision poses specific ethical issues. However, the majority of existing literature either addresses artificial intelligence as a whole or pays particular attention to natural language processing, leaving a gap in specialized research on ethical issues and systematic solutions in the field of computer vision. This paper utilizes bibliometrics and text-mining techniques to quantitatively analyze papers from prominent academic conferences in computer vision over the past decade. It first reveals the developing trends and specific distribution of attention regarding trustworthy aspects in the computer vision field, as well as the inherent connections between ethical dimensions and different stages of visual model development. A life-cycle framework regarding trustworthy computer vision is then presented by making the relevant trustworthy issues, the operation pipeline of AI models, and viable technical solutions interconnected, providing researchers and policymakers with references and guidance for achieving trustworthy CV. Finally, it discusses particular motivations for conducting trustworthy practices and underscores the consistency and ambivalence among various trustworthy principles and technical attributes.

Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components. Over the years, signal processing and machine learning algorithms have been combined to achieve this. Many publications and extensive research works are performed on energy disaggregation or NILM for the state-of-the-art methods to reach the desired performance. The initial interest of the scientific community to formulate and describe mathematically the NILM problem using machine learning tools has now shifted into a more practical NILM. Currently, we are in the mature NILM period where there is an attempt for NILM to be applied in real-life application scenarios. Thus, the complexity of the algorithms, transferability, reliability, practicality, and, in general, trustworthiness are the main issues of interest. This review narrows the gap between the early immature NILM era and the mature one. In particular, the paper provides a comprehensive literature review of the NILM methods for residential appliances only. The paper analyzes, summarizes, and presents the outcomes of a large number of recently published scholarly articles. Furthermore, the paper discusses the highlights of these methods and introduces the research dilemmas that should be taken into consideration by researchers to apply NILM methods. Finally, we show the need for transferring the traditional disaggregation models into a practical and trustworthy framework.