Matt Roach

Tommaso Turchi, Ben Wilson, Matt Roach et al.

AI is now embedded in healthcare, finance, policy, and many other domains, yet genuine human-AI synergy - combined performance that exceeds what either party achieves alone - is uncommon. Meta-analyses show that AI assistance tends to improve human performance compared to working alone, but studies finding true synergy are scarce. We call this persistent shortfall the synergy gap. Most current work treats human-AI combination as an engineering problem and concentrates on interpretability, trust calibration, or interface design. These matter, but they cover only part of what determines whether combination works. Closing the synergy gap, we argue, requires explicit engagement with a wider design space. We map that space through six interconnected elements: sociotechnical context, decision-making frameworks, human decision participants, AI capabilities, interaction, and holistic evaluation. For each element, we describe what it covers, how it shapes the others in practice, and what it implies for design. The result is a shared vocabulary for practitioners building hybrid systems, an analytical lens for researchers studying combination patterns, and a starting point for evaluators interested in the full quality of human-AI decision-making rather than accuracy alone.

Ben Wilson, Matimba Swana, Peter Winter et al.

The phrase 'human in the loop' is increasingly used to imply a sense of safety in relation to AI decision systems. It shouldn't. There are contexts where it can be applied appropriately, but these are not in the deployed decision systems we see dominating today. Human oversight of AI decision processes is one of the most popular proposals for addressing concerns, especially about bias, discrimination, misinformation, manipulation, accountability, and transparency. But there is insufficient examination of what human oversight actually means. The question raised in this paper is whether using the metaphor of a loop does anything to assist understanding of what is required and what is achieved in a particular decision context. Indiscriminate use of the loop metaphor obscures both processes and outcomes. It enables 'humanwashing', an activity analogous to 'greenwashing', where writers and commentators use language primarily aimed at putting systems in the best possible light.

Alan Dix, Tommaso Turchi, Ben Wilson et al.

While XAI focuses on providing AI explanations to humans, can the reverse - humans explaining their judgments to AI - foster richer, synergistic human-AI systems? This paper explores various forms of human inputs to AI and examines how human explanations can guide machine learning models toward automated judgments and explanations that align more closely with human concepts.