Copacetic Smartening of Small Data for HLC

The need for more human-like computing, which involves endowing machines with human-like perceptual reasoning and learning abilities, has becoming increasingly evident in the last year. The inexplicable 'black box', highly complex and context dependent models of deep learning techniques and conventional probability approaches, are not always successful in environments like Improvised Explosive Device Disposal (IEDD), which can have severe consequences for incorrect judgements. Moving towards a more transparent, explainable and human-like approach will transform the human-machine relationship and provide a more efficient and effective environment for humans and machines to collaborate in, leading to improved prospects for UK growth and employment.

This feasibility study focuses on those high risk situations where human cognition is superior to any machine, when humans are called to make judgements where information is sparse, time is poor and their previous knowledge, experience and 'gut feel' often play a critical part in their decision making. Unlike machines, humans rely on small scale data and small scale models (e.g. schema or frames) to make their judgements, reflecting on the possibilities or likelihoods of surprise events to improve their sense making in a given situation. A key challenge is to identify those few critical learning and inference kernels (CLIKs) that are at the heart of these schema humans use to make their judgements in a satisficing manner that feels right, i.e. things appear to be in copacetic or perfect order. Using the IEDD context as its setting, this research moves away from the conventional Bayesian and probability-based approaches, instead moving towards a novel approach inspired by the cognitive sciences to develop human-like inference techniques and learning schema. The schema will then be encoded into explainable artificial intelligence (XAI) agents so they can work alongside humans to enhance performance during high cognitive load tasks and for the learning and training of future experts.

The impact from this project will benefit the inter-disciplinary research community developing novel ways for enhancing human-machine collaboration. One key impact is expected to be growth in the scale and diversity of the research into human-like computing approaches and algorithms that are not restricted by more conventional probabilistic approaches. This will lead to greater understanding of human-like computing and the ways in which explainable artificial intelligence (XAI) can support humans in high risk, uncertain, critical decision-making environments; also how the lessons drawn from critical learning and inference Kernels (CLIK) development could lead to improved business processes, risk assessment and economic growth from new human-like computing tools or services. The project will also impact government departments and organisations across industry that would gain value from implementing human-like computing for enhanced judgements in a wide variety of applications. The development of the CLIKs and XAI will benefit people working increasingly with cyber-forms of business, collaborating with computers and AI; resulting from increased awareness of these challenges of leaders of UK organisations with influence across business and government (primarily in the UK, but also internationally). The longer-term impact, achieved by exploitation of the agent prototypes and approaches developed during the project, will enable co-creation of new XAI services, and creation of new, smarter, approaches to government and new businesses. It is anticipated that these will impact the public, as users of the smarter systems and services created by the project. The case study draws from experienced senior leaders in UK business, industry and government who will in turn benefit from the reflective practice nature of the elicitation interviews. This appreciative, explainable and reflective form of judgement-support working will, in the short-term, help people to make more robust judgements leading to a more stable basis for policy-making in financial sectors and government. This will benefit UK public as they rely on safer systems (e.g. banking) and the more contractual nature of government projects post-Brexit. Improved, explainable judgements in policy-making and more reflective decision-making will generally provide a more stable and sound foundation for leadership, leading to improved prospects for UK growth and employment.