In the quest to make AI more human-centric, we must address the 'Real Experience Blind Spot' that overlooks the meanings, complexity and qualitative richness of actual human experience.
The computational, analytical model of mind, which has been predominant in the West for the last century, primarily focuses on observation, analysis, measurement, and symbolic abstract representation of experience. It treats human experience as a series of discrete, divisible elements, breaking it down into quantifiable parts that can be processed and analysed by the experiencer, by human observers and by machines.
However, this model and its representations fail to capture and express the whole nature of human experience. By reducing complex emotions, thoughts, and conditions to mere symptoms, symbols and data points, it overlooks the qualitative aspects and meanings of human life. This reductionist approach leads to a superficial understanding of actual lived experience, which is particularly problematic when dealing with chronic health and other persistent or recurring conditions, especially multi-faceted and -morbid illnesses that are now the norm not the exception.
The 'Real Experience Blind Spot' refers to the gap in the computational, analytical model's ability to see, understand and address the full spectrum of human experience over time. Generative and other AI systems are not equipped to handle the meanings and nuances of real-life experiences, especially those limited by chronic conditions, complex emotional states, and external social and environmental factors.
This blind spot becomes bigger when we consider the growing incidence of chronic physical and mental health issues, as well as non-health-related conditions like social isolation, burnout and climate crisis impacts. The current models fail to comprehend and integrate these complex experiences, leading to incomplete and often ineffective recommendations and solutions.
Neglecting real lived experience in our models has significant consequences. For individuals with chronic health conditions, this means that their symptoms are often treated in isolation, without addressing their originating causes or the interconnected nature of their experiences. This can lead to inadequate treatment and prolonged suffering.
Beyond health, the failure to see, know and incorporate real experiences into the design and creation of policies, programmes, services and technologies has broader societal implications. Issues such as social isolation, burnout, and climate-change-related suffering are exacerbated when the real human aspect is ignored. The lack of a relevant experiential approach in AI models contributes to a growing sense of disconnection and inadequacy in addressing these complex challenges.
Experiential Cognition® proposes a shift from the computational analytical, data-driven model to one that sees, incorporates and prioritises human experiences in their entirety. This new paradigm seeks to understand and integrate the meanings, contexts and qualities of lived experiences, rather than just their observable and/or measurable symptoms.
By focusing on real experiential aspects, AI can find paths to become more human-enabling, -connecting, and -fulfilling. This approach aims to bridge the gap between mathematical and abstract representations and real life, providing more meaningful, relevant and effective pathways, recommendations and solutions for individuals, communities and society to learn and progress as a whole.
The real future of AI lies in its ability to bridge the experience gap by incorporating the principles of Experiential Cognition®. This involves developing approaches and technologies that can understand and process the complexities of human experiences, beyond mere data points.
To achieve this, interdisciplinary collaboration is essential. Insights from psychology, sociology, geography and other fields of knowing experience can inform the development of AI systems that truly reflect the richness of human life. By addressing the Real Lived Experience Blind Spot, we can create AI that not only serves but also enhances human existence.