Coherent and consistent relational transfer learning with auto-encoders

Human defined concepts are inherently transferable, but it is not clear under what conditions they can be modelled effectively by non-symbolic artificial learners. This paper argues that for a transferable concept to be learned, the system of relations that define it must be coherent across domains and properties. That is, they should be consistent with respect to relational constraints, and this consistency must extend beyond the representations encountered in the source domain. Further, where relations are modelled by differentiable functions, their gradients must conform – the functions must at times move together to preserve consistency. We propose a Partial Relation Transfer (PRT) task which exposes how well relation-decoders model these properties, and exemplify this with ordinality prediction transfer task, including a new data set for the transfer domain. We evaluate this on existing relation-decoder models, as well as a novel model designed around the principles of consistency and gradient conformity. Results show that consistency across broad regions of input space indicates good transfer performance, and that good gradient conformity facilitates consistency

Accountability in AI: From principles to industry-specific accreditation

Recent AI-related scandals have shed a spotlight on accountability in AI, with increasing public interest and concern. This paper draws on literature from public policy and governance to make two contributions. First, we propose an AI accountability ecosystem as a useful lens on the system, with different stakeholders requiring and contributing to specific accountability mechanisms. We argue that the present ecosystem is unbalanced, with a need for improved transparency via AI explainability and adequate documentation and process formalisation to support internal audit, leading up eventually to external accreditation processes. Second, we use a case study in the gambling sector to illustrate in a subset of the overall ecosystem the need for industry-specific accountability principles and processes. We define and evaluate critically the implementation of key accountability principles in the gambling industry, namely addressing algorithmic bias and model explainability, before concluding and discussing directions for future work based on our findings

Geometric semi-automatic analysis of radiographs of Colles’ fractures

Fractures of the wrist are common in Emergency Departments, where some patients are treated with a procedure called Manipulation under Anaesthesia. In some cases, this procedure is unsuccessful and patients need to revisit the hospital where they undergo surgery to treat the fracture. This work describes a geometric semi-automatic image analysis algorithm to analyse and compare the x-rays of healthy controls and patients with dorsally displaced wrist fractures (Colles’ fractures) who were treated with Manipulation under Anaesthesia. A series of 161 posterior-anterior radiographs from healthy controls and patients with Colles’ fractures were acquired and analysed. The patients’ group was further subdivided according to the outcome of the procedure (successful/unsuccessful) and pre- or post-intervention creating five groups in total (healthy, pre-successful, pre-unsuccessful, post-successful, post-unsuccessful). The semi-automatic analysis consisted of manual location of three landmarks (finger, lunate and radial styloid) and automatic processing to generate 32 geometric and texture measurements, which may be related to conditions such as osteoporosis and swelling of the wrist. Statistical differences were found between patients and controls, as well as between pre- and post-intervention, but not between the procedures. The most distinct measurements were those of texture. Although the study includes a relatively low number of cases and measurements, the statistical differences are encouraging

Graph-based Neural Modules to Inspect Attention-based Architectures: A Position Paper

Encoder-decoder architectures are prominent building blocks of state-of-the-art solutions for tasks across multiple fields where deep learning (DL) or foundation models play a key role. Although there is a growing community working on the provision of interpretation for DL models as well as considerable work in the neuro-symbolic community seeking to integrate symbolic representations and DL, many open questions remain around the need for better tools for visualization of the inner workings of DL architectures. In particular, encoder-decoder models offer an exciting opportunity for visualization and editing by humans of the knowledge implicitly represented in model weights. In this work, we explore ways to create an abstraction for segments of the network as a two-way graph-based representation. Changes to this graph structure should be reflected directly in the underlying tensor representations. Such two-way graph representation enables new neuro-symbolic systems by leveraging the pattern recognition capabilities of the encoder-decoder along with symbolic reasoning carried out on the graphs. The approach is expected to produce new ways of interacting with DL models but also to improve performance as a result of the combination of learning and reasoning capabilities

Neural Networks for State Evaluation in General Game Playing

Abstract. Unlike traditional game playing, General Game Playing is concerned with agents capable of playing classes of games. Given the rules of an unknown game, the agent is supposed to play well without human intervention. For this purpose, agent systems that use deterministic game tree search need to automatically construct a state value function to guide search. Successful systems of this type use evaluation functions derived solely from the game rules, thus neglecting further improvements by experience. In addition, these functions are fixed in their form and do not necessarily capture the game’s real state value function. In this work we present an approach for obtaining evaluation functions on the basis of neural networks that overcomes the aforementioned problems. A network initialization extracted from the game rules ensures reasonable behavior without the need for prior training. Later training, however, can lead to significant improvements in evaluation quality, as our results indicate.

Contrastive counterfactual visual explanations with overdetermination

A novel explainable AI method called CLEAR Image is introduced in this paper. CLEAR Image is based on the view that a satisfactory explanation should be contrastive, counterfactual and measurable. CLEAR Image seeks to explain an image’s classification probability by contrasting the image with a representative contrast image, such as an auto-generated image obtained via adversarial learning. This produces a salient segmentation and a way of using image perturbations to calculate each segment’s importance. CLEAR Image then uses regression to determine a causal equation describing a classifier’s local input–output behaviour. Counterfactuals are also identified that are supported by the causal equation. Finally, CLEAR Image measures the fidelity of its explanation against the classifier. CLEAR Image was successfully applied to a medical imaging case study where it outperformed methods such as Grad-CAM and LIME by an average of 27% using a novel pointing game metric. CLEAR Image also identifies cases of causal overdetermination, where there are multiple segments in an image that are sufficient individually to cause the classification probability to be close to one

Expressing Belief Flow in Assertion Networks

Abstract. In the line of some earlier work done on belief dynamics, we propose an abstract model of belief propagation on a graph based on the methodology of the revision theory of truth. A modal language is developed for portraying the behavior of this model, and its expressiveness is discussed. We compare the proposal of this model as well as the language developed with some of the existing frameworks for modelling communication situations.