Abstract
As artificial intelligence becomes ubiquitous in our lives, so do the opportunities to combine machine and human intelligence to obtain more accurate and more resilient prediction models across a wide range of domains. Hybrid intelligence can be designed in many ways, depending on the role of the human and the algorithm in the hybrid system. This paper offers a brief taxonomy of hybrid intelligence, which describes possible relationships between human and machine intelligence for robust forecasting. In this taxonomy, biological intelligence represents one axis of variation, going from individual intelligence (one individual in isolation) to collective intelligence (several connected individuals). The second axis of variation represents increasingly sophisticated algorithms that can take into account more aspects of the forecasting system, from information to task to human problem-solvers. The novelty of the paper lies in the interpretation of recent studies in hybrid intelligence as precursors of a set of algorithms that are expected to be more prominent in the future. These algorithms promise to increase hybrid system’s resilience across a wide range of human errors and biases thanks to greater human-machine understanding. This work ends with a short overview for future research in this field.
Highlights
Taxonomy of Hybrid Intelligence.The last decade has seen major advances in computation power and its increased availability at a low-cost due to cloud computing
Advances in machine learning and statistical learning methods, the availability of big data, mobile devices, and social networks has led to an explosion of artificial intelligence and its applications to a wide range of commercial, governmental, and social products and services
While artificial intelligence is building momentum, it is becoming apparent that artificial intelligence and biological intelligence are characterized by different mechanisms, oftentimes not entirely overlapping
Summary
Understanding how other disciplines are integrating human and machine intelligence in their design can help forecasting researchers and practitioners by providing a blueprint that can be adapted to the specific necessities of their field. Compared to previous excellent reviews that have been written on the subject of hybrid intelligence [11,12], the present perspective focuses more on papers, results and insights from behavioral disciplines, cognitive science and psychology. This novel lens can better support practitioners interested in designing better human-aware algorithms. The papers in this article were selected by searching various academic repositories, using the terms “hybrid” and “human–machine”
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