Building explainable and trustworthy intelligent systems

Existing machine learning-based intelligent systems are autonomous and opaque (often considered “black-box” systems), which has led to the lack of trust in AI adoption and, consequently, the gap between machine and human being.

In 2018, the European Parliament adopted the General Data Protection Regulation (GDPR), which introduces a right of explanation for all human individuals to obtain “meaningful explanations of the logic involved” when a decision is made by automated systems. To this end, it is a compliance that an intelligent system needs to be transparent and is expected to provide human-understandable explanations.

In November 2019, the Australian Government established the Australia’s Artificial Intelligence (AI) Ethics Framework to guide businesses and governments to responsibly design, develop and implement AI systems. According to the AI ethics principles set out in the Framework, an intelligent system is expected to address human-centred values and support fairness, reliability, transparency and explainability.

• New algorithms and techniques to design robust, reliable, and trustworthy systems underpinned by machine intelligence.
• New theories and methods to support transparency and explainability.
• New approach to incorporate human behaviour and in put in the design and development of intelligence systems.

• Knowledge in data mining and machine/deep learning.
• Knowledge in human-computer interaction (optional).
• Problem-solving and logical thinking capabilities.
• Programming skills in Python.
• Academic writing skills.