We consider an aggregated human-AI collaboration aimed at generating a joint interpretable model. The model takes the form of Boolean decision rules, where human input is provided in the form of logical conditions or as partial templates. This focus on the combined construction of a model offers a different perspective on joint decision making. Previous efforts have typically focused on aggregating outcomes rather than decisions logic. We demonstrate the proposed approach through two examples and highlight the usefulness and challenges of the approach.

Advances in machine learning, computer vision, or autonomous processes will open a vast array of opportunities to organizations and employees. This will force us to rethink many aspects of our life and work. For example, virtual personal assistants can understand text, context and tone of voice, converse in natural language, make human-like gestures and even support decision making. The algorithms provide supervised and unsupervised learning capabilities, can be programmed in virtually any language and can be deployed at scale in any location. Using historical data, they create unique AI models that are perfectly fitting specific business and life environments.

Artificial Intelligence is one of the most extended biomimicry projects in the history of scientific research. Just like how the human brain is a piece of hardware/wetware running our mind -- the software, machines have GPU/CPU processing units as the hardware and neural networks as the software. Psychologists first instigated this research to help further understand how the brain works and our mind learns. One of the first neural networks that came out in 1957, The Perceptron, was made by an American psychologist -- Frank Rosenblatt, who modeled it based on the learnings from probing a frog's brain. Even though the pioneers of Artificial Intelligence were visionaries who believed that AI could change the world, most were doubtful whether machines would think like us or be "creative".