Collaborative AI systems aim at working together with humans in a shared space to achieve a common goal. This setting imposes potentially hazardous circumstances due to contacts that could harm human beings. Thus, building such systems with strong assurances of compliance with requirements domain specific standards and regulations is of greatest importance. Challenges associated with the achievement of this goal become even more severe when such systems rely on machine learning components rather than such as top-down rule-based AI. In this paper, we introduce a risk modeling approach tailored to Collaborative AI systems. The risk model includes goals, risk events and domain specific indicators that potentially expose humans to hazards. The risk model is then leveraged to drive assurance methods that feed in turn the risk model through insights extracted from run-time evidence. Our envisioned approach is described by means of a running example in the domain of Industry 4.0, where a robotic arm endowed with a visual perception component, implemented with machine learning, collaborates with a human operator for a production-relevant task.

Artificial Intelligence mimics human beings, the human mind and the way the human mind learns through cognition, environmental observation and feedback. AI programs can perform all cognitive functions as thinking, rationalizing, reasoning, intuition, etc. It can include the whole gamut of what the human mind or human intelligence can be, all that it can do and more. In contrast to the typical android or humanoid AI robots, seen in science fiction, AI will find more use cases in machines, systems and software, used in all the industries. We are already witnessing the use of robots in manufacturing and also in predicting when machines will fail, need maintenance, repair, etc. Popular use-cases of AI range from supply chain management to sales, marketing and even automated intelligent customer service.