Using technology to enhance CI will require breakthroughs in the theory and practice of human-computer interaction. Computer systems designed to facilitate decision-making have a long history, but have often foundered due to a failure to understand how humans use and perceive such technology. To avoid the mistakes of the past, it is crucial to base the design of interactive systems on fundamental theories of human behaviour.³⁶

Future Horizons:

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5-yearhorizon

Improved interaction boosts effectiveness

Renewed focus on improving how humans interact with CI tools significantly improves the tools' effectiveness. The increasing use of technology to boost human collaboration prompts efforts to begin monitoring its impact on human behaviour and relations.

10-yearhorizon

AI breakthroughs bring more effective collaboration

Breakthroughs in key AI capabilities like context-awareness and continual learning make it possible for CI tools to adapt to users and more effectively guide human collaboration. Evidence that some efforts to harness CI are actually causing humans' skills to atrophy prompts a refocusing of efforts on tools that enhance human capabilities rather than replacing them.

25-yearhorizon

Artificial agents join collaborative teams

Advances in AI make it possible to imbue AI with a true theory of mind, allowing artificial agents to become equal members in AI-human teams. CI-literate AI is used to represent the interests of nature — forests or ocean ecosystems, say — or to represent the interests of future generations. Organisations such as the IPCC and UN use human-AI teams to aid cross-cultural negotiations.

There is considerable excitement around LLMs, which have opened up an intuitive new way for humans to interface with computers through natural language. But AI struggles with certain challenging aspects of human interaction. Human decision-making is highly context-dependent, for instance, and that context is not static. Group interactions are also governed by subtle cues and complex social conventions.

AI researchers have made some progress in understanding context³⁷ and gesture recognition,³⁸ and there is tentative evidence that LLMs have a rudimentary ability to model human mental states (known as theory of mind).³⁹ But imbuing machines with true social intelligence remains a distant goal, and will require a multidisciplinary effort spanning the social, behavioural and natural sciences.⁴⁰ A concerted effort is also needed to improve our understanding of how humans perceive and interact with conversational AI.⁴¹

In addition, AI chatbots raise significant privacy and security concerns.⁴² And their propensity to confidently assert incorrect information, or “hallucinate”, makes it essential to ensure people understand the technology’s limits so they don’t become overly trusting of it.⁴³ As technology increasingly mediates group interactions it will also be important to measure whether it is enhancing human capabilities or causing them to atrophy.

Human-computer interaction - Anticipation Scores

The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:

The uncertainty related to future science breakthroughs in the field
The transformative effect anticipated breakthroughs may have on research and society
The scope for action in the present in relation to anticipated breakthroughs.

This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.