Professor Nigel Crook

We find ourselves at a unique point of time in history. Following over two millennia of debate amongst some of the greatest minds that ever existed about the nature of morality, the philosophy of ethics and the attributes of moral agency, and after all that time still not having reached consensus, we are coming to a point where artificial intelligence (AI) technology is enabling the creation of machines that will possess a convincing degree of moral competence. The existence of these machines will undoubtedly have an impact on this age old debate, but we believe that they will have a greater impact on society at large, as AI technology deepens its integration into the social fabric of our world. The purpose of this special issue on Computing Morality is to bring together different perspectives on this technology and its impact on society. The special issue contains four very different and inspiring contributions.

VirtuosA (‘virtuous algorithm’) is introduced, a model in which artificial intelligence (AI) systems learn ethical behaviour based on a framework adapted from Christian philosopher Dallas Willard and brought together with associated neurobiological structures and broader systems thinking. To make the inquiry concrete, the authors present a simple example scenario that illustrates how a robot might acquire behaviour akin to the virtue of kindness that can be attributed to humans. References to philosophical work by Peter Sloterdijk help contextualise Willard’s virtue ethics framework. The VirtuosA architecture can be implemented using state-of-the-art computing practices and plausibly redescribes several concrete scenarios implemented from the computing literature and exhibits broad coverage relative to other work in ethical AI. Strategies are described for using the model for systems evaluation —particularly the role of ‘embedded evaluation’ within the system—and its broader application as a meta-ethical device is discussed.

Undergraduate computing courses inevitably include a high degree of regeneration in order to keep abreast of this rapidly changing field. Introductory programming modules in particular need to adapt to changing trends and languages. Until recently, the focus of debate within the Oxford Brookes University curriculum has therefore been on the course content, but since 2012 there has been a major change in the method of delivery through the introduction of a new apprenticeship model. This paper seeks to reflect on this, and other recent changes which have led to improved student engagement and results. The data is limited however, and so the results presented here are not conclusive.

Humans describe images in terms of nouns and adjectives while algorithms

operate on images represented as sets of pixels. Bridging this gap between

how humans would like to access images versus their typical representation

is the goal of image parsing, which involves assigning object and attribute

labels to pixel. In this paper we propose treating nouns as object labels and

adjectives as visual attribute labels. This allows us to formulate the image

parsing problem as one of jointly estimating per-pixel object and attribute

labels from a set of training images. We propose an efficient (interactive

time) solution. Using the extracted labels as handles, our system empowers

a user to verbally refine the results. This enables hands-free parsing of an

image into pixel-wise object/attribute labels that correspond to human semantics.

Verbally selecting objects of interests enables a novel and natural

interaction modality that can possibly be used to interact with new generation

devices (e.g. smart phones, Google Glass, living room devices). We

demonstrate our system on a large number of real-world images with varying

complexity. To help understand the tradeoffs compared to traditional

mouse based interactions, results are reported for both a large scale quantitative

evaluation and a user study.

We present an Embodied Conversational Agent(ECA) that incorporates a context-sensitive mechanism for handling user barge-in. The affective ECA engages the user in social conversation, and is fully implemented. We will use actual examples of system behaviour to illustrate. The ECA is designed to recognise and be empathetic to the emotional state of the user. It is able to detect, react quickly to, and then follow up with considered responses to different kinds of user interruptions. The design of the rules which enable the ECA to respond intelligently to different types of interruptions was informed by manually analysed real data from human-human dialogue. The rules represent recoveries from interruptions as two-part structures: an address followed by a resumption. The system is robust enough to man- age long, multi-utterance turns by both user and system, which creates good opportunities for the user to interrupt while the ECA is speaking.