On August 27, 1960, at the Olympics in Rome, one of the most controversial gold medals was awarded. At the 100-meter freestyle men's swimming event, Australian swimmer John Devitt and American Lance Larson both recorded the same finish time of 55.2 seconds. Only Devitt walked away with the gold medal. The way swimming was timed was by using three timers per lane, all with stopwatches, from which an average was taken. In the rare occurrence there was a tie, a head judge, in this case Hans Runströmer from Sweden, was on hand to adjudicate.

The rise of human-information systems, cybernetic systems, and increasingly autonomous systems requires the application of epistemic frameworks to machines and human-machine teams. This chapter discusses higher-order design principles to guide the design, evaluation, deployment, and iteration of Lethal Autonomous Weapons Systems (LAWS) based on epistemic models. Epistemology is the study of knowledge. Epistemic models consider the role of accuracy, likelihoods, beliefs, competencies, capabilities, context, and luck in the justification of actions and the attribution of knowledge. The aim is not to provide ethical justification for or against LAWS, but to illustrate how epistemological frameworks can be used in conjunction with moral apparatus to guide the design and deployment of future systems. The models discussed in this chapter aim to make Article 36 reviews of LAWS systematic, expedient, and evaluable. A Bayesian virtue epistemology is proposed to enable justified actions under uncertainty that meet the requirements of the Laws of Armed Conflict and International Humanitarian Law. Epistemic concepts can provide some of the apparatus to meet explainability and transparency requirements in the development, evaluation, deployment, and review of ethical AI.

In this chapter we argue that discourses on AI must transcend the language of 'ethics' and engage with power and political economy in order to constitute 'Good Data'. In particular, we must move beyond the depoliticised language of 'ethics' currently deployed (Wagner 2018) in determining whether AI is 'good' given the limitations of ethics as a frame through which AI issues can be viewed. In order to circumvent these limits, we use instead the language and conceptualisation of 'Good Data', as a more expansive term to elucidate the values, rights and interests at stake when it comes to AI's development and deployment, as well as that of other digital technologies. Good Data considerations move beyond recurring themes of data protection/privacy and the FAT (fairness, transparency and accountability) movement to include explicit political economy critiques of power. Instead of yet more ethics principles (that tend to say the same or similar things anyway), we offer four 'pillars' on which Good Data AI can be built: community, rights, usability and politics. Overall we view AI's 'goodness' as an explicly political (economy) question of power and one which is always related to the degree which AI is created and used to increase the wellbeing of society and especially to increase the power of the most marginalized and disenfranchised. We offer recommendations and remedies towards implementing 'better' approaches towards AI. Our strategies enable a different (but complementary) kind of evaluation of AI as part of the broader socio-technical systems in which AI is built and deployed.

A YORKSHIRE university aims to use data and digital connectivity to support revolutionary changes in the region's manufacturing sector. The University of Huddersfield has established a Digital Enablers' Network (DEN) which is working with local manufacturing firms to make them more competitive in global markets. The university is forging strong ties with businesses, including enterprises run by former students, through its 3M Buckley Innovation Centre. James Devitt, the university to industry programme manager and 3M Buckley innovation centre business development manager, said the large scale automation of processes using artificial intelligence are leading to major improvements in productivity. "The University of Huddersfield is proud of its close association with industry and manufacturing,'' Mr Devitt said. "It has built strong capabilities in a range of new industrial digital technologies, centred around its Centre for Industrial Analytics (CIndA).

SOMEWHERE in California, Google is building a device that will usher in a new era for computing. It's a quantum computer, the largest ever made, designed to prove once and for all that machines exploiting exotic physics can outperform the world's top supercomputers. And New Scientist has learned it could be ready sooner than anyone expected – perhaps even by the end of next year. The quantum computing revolution has been a long time coming. In the 1980s, theorists realised that a computer based on quantum mechanics had the potential to vastly outperform ordinary, or classical, computers at certain tasks.