Augmented Intelligence (AuI) refers to the use of artificial intelligence (AI) to amplify certain cognitive tasks performed by human decision-makers. However, there are concerns that AI’s increasing capability and alignment with human values may undermine user agency, autonomy, and responsible decision-making. To address these concerns, we conducted a user study in the field of orthopedic radiology diagnosis, introducing a reflective XAI (explainable AI) support that aimed to stimulate human reflection, and we evaluated its impact of in terms of decision performance, decision confidence and perceived utility. Specifically, the reflective XAI support system prompted users to reflect on the dependability of AI-generated advice by presenting evidence both in favor of and against its recommendation. This evidence was presented via two cases that closely resembled a given base case, along with pixel attribution maps. These cases were associated with the same AI advice for the base case, but one case was accurate while the other was erroneous with respect to the ground truth. While the introduction of this support system did not significantly enhance diagnostic accuracy, it was highly valued by more experienced users. Based on the findings of this study, we advocate for further research to validate the potential of reflective XAI in fostering more informed and responsible decision-making, ultimately preserving human agency.
The release of ChatGPT to the general public has sparked discussions about the dangers of artificial intelligence (AI) among the public. The European Commission’s draft of the AI Act has further fueled these discussions, particularly in relation to the definition of AI and the assignment of risk levels to different technologies. Security concerns in AI systems arise from the need to protect against potential adversaries and to safeguard individuals from AI decisions that may harm their well-being. However, ensuring secure and trustworthy AI systems is challenging, especially with deep learning models that lack explainability. This paper proposes the concept of Controllable AI as an alternative to Trustworthy AI and explores the major differences between the two. The aim is to initiate discussions on securing complex AI systems without sacrificing practical capabilities or transparency. The paper provides an overview of techniques that can be employed to achieve Controllable AI. It discusses the background definitions of explainability, Trustworthy AI, and the AI Act. The principles and techniques of Controllable AI are detailed, including detecting and managing control loss, implementing transparent AI decisions, and addressing intentional bias or backdoors. The paper concludes by discussing the potential applications of Controllable AI and its implications for real-world scenarios.
As machine learning (ML) has emerged as the predominant technological paradigm for artificial intelligence (AI), complex black box models such as GPT-4 have gained widespread adoption. Concurrently, explainable AI (XAI) has risen in significance as a counterbalancing force. But the rapid expansion of this research domain has led to a proliferation of terminology and an array of diverse definitions, making it increasingly challenging to maintain coherence. This confusion of languages also stems from the plethora of different perspectives on XAI, e.g. ethics, law, standardization and computer science. This situation threatens to create a “tower of Babel” effect, whereby a multitude of languages impedes the establishment of a common (scientific) ground. In response, this paper first maps different vocabularies, used in ethics, law and standardization. It shows that despite a quest for standardized, uniform XAI definitions, there is still a confusion of languages. Drawing lessons from these viewpoints, it subsequently proposes a methodology for identifying a unified lexicon from a scientific standpoint. This could aid the scientific community in presenting a more unified front to better influence ongoing definition efforts in law and standardization, often without enough scientific representation, which will shape the nature of AI and XAI in the future.
Asymmetric Shapley values (ASVs) are an extension of Shapley values that allow a user to incorporate partial causal knowledge into the explanation process. Unfortunately, computing ASVs requires sampling permutations, which quickly becomes computationally expensive. We propose A-PDD-SHAP, an algorithm that employs a functional decomposition approach to approximate ASVs at a speed orders of magnitude faster compared to permutation sampling, which significantly reduces the amortized complexity of computing ASVs when many explanations are needed. Apart from this, once the A-PDD-SHAP model is trained, it can be used to compute both symmetric and asymmetric Shapley values without having to re-train or re-sample, allowing for very efficient comparisons between different types of explanations.
Fetal death, infant morbidity and mortality are generally caused by the presence of congenital anomalies. By performing a fetal morphology scan, the sonographer can detect their presence and have a thorough conversation with the soon-to-be parents. Diagnosing congenital anomalies is a difficult task even for an experienced sonographer. A more accurate diagnosis can be set through a merger between the doctor’s knowledge and Artificial Intelligence. The aim of paper is to present an intelligent framework that is able to differentiate accurately between the view planes of the fetal abdomen in an ultrasound movie. Deep learning methods, such as ResNet50, DenseNet121, and InceptionV3, have been trained to classify each movie frame. A thorough statistical analysis is used to benchmark the neural networks, and to build a hierarchy. The best performing algorithm is used to classify each frame of the movie, followed by a synergetic weighted voting system that sets the label of the entire ultrasound video. We have tested our proposed framework on several fetal morphology videos. The experimental results showed that the framework differentiates well between the fetal abdomen view planes, even if the deep learning neural networks are able to differentiate between the static images with accuracies that range between 46.01% and 77.80%.
