Human-Machine Collaboration with Deep Learning Agents

Recent advances in artificial intelligence (AI) and machine learning (ML), especially ML based on deep neural networks (so-called deep learning), has led to a range of successful applications in tasks such as image recognition, classification, and anomaly detection. However, it is widely acknowledged that in many applications, effective task performance involves a combination of machine intelligence and human judgement; i.e., collaboration between human and machine agents. The first generation of deep learning systems suffered from being `black boxes', with minimal ability to explain their decisions, making them of limited use in human-machine collaboration applications. This situation is steadily improving, with increasingly-sophisticated explainable AI (XAI) techniques. However, there is still a lack of knowledge about how best to equip a deep learning based machine agent with XAI capabilities geared to help improve task performance in a human-machine team. Specifically, we are interested in applications that involve some combination of the following factors that complicate the problem: (1) decision-making where the ML agent needs to process multimodal data (for example, including imagery, audio, and text classification) so an explanation must itself be multimodal and also will likely involve the 'fusion' of data from multiple modalities; (2) decision-making where the machine agent needs to learn from the human agent (for example, the human 'tells' the machine some important piece of information to modify its model of the world); (3) decision-making where the human agent needs to learn from the machine agent (for example, the machine needs to communicate and hence transfer some 'insight' to the human, allowing them to have the same insight themselves in future). This PhD is suitable for someone keen to gain in-depth knowledge of state-of-the-art deep learning and XAI, and an interest in human-computer collaboration in general. The project will be carried out in collaboration with IBM UK, Dstl, Cardiff University Crime and Security Institute and with international cooperation (University partners in the US via the Distributed Analytics and Information Sciences Distributed Technology Alliance).