Human-guided collaborative multi-objective design of explainable, fair and privacy-preserving AI for digital health

Artificial Intelligence (AI) is one of the most significant pillars for the digital transformation of modern healthcare systems which will leverage the growing volume of real-world data collected through wearables and sensors, and consider multitude of complex interactions between diseases and individual/population. While AI-enabled digital health services and products are rapidly expanding in volume and variety, most of the AI innovations remain in the form of proof-of-concept. There is a continuous debate regarding whether AI is worthy of trust. The EU AI HLEG has defined that trustworthy AI systems should be lawful, ethical and robust. To translate it into actionable practices, provision of explainability, fairness and privacy is crucial. A considerable volume of research has been conducted in the areas of explainable AI, fair AI and privacy-preserving AI. However, the current research efforts to tackle the three challenges are fragmented and have culminated in a variety of solutions with heterogeneous, non-interoperable, or even conflicting capabilities. The ambitious vision of HarmonicAI is to build a human-machine collaborative multi-objective design framework to foster coherently explainable, fair and privacy-preserving AI for digital health. HarmonicAI draws together proven experts in AI, health care, IoT, data science, privacy, cyber security, software engineering, HCI and industrial design with an underlying common aim to develop concrete technical and operational guidelines for AI practitioners to design human-centered, domain-specific, requirement-oriented trustworthy AI solutions, accelerating the scalable deployment of AI-powered digital health services and offering assurance to the public that AI in digital health is being developed and used in an ethical and trustworthy manner.