Explainable and Responsible Artificial Intelligence

About This Course

The field of Artificial Intelligence (AI) has been gaining increasing attention in the recent years. There has been widespread adoption of AI models for commercial purposes and also more usage and presence of AI systems in public places. The rise in the adoption and usage of AI models are due to reasons such as availability of faster and cheaper computing power, larger computing storage capacity, and advanced research in AI algorithms leading to better performance.

The use of AI models can be found in almost all industries and for various purposes. For example, it has been reported that AI models has outperformed human doctors in speed and accuracy in diagnosing brain tumours, and in the legal domain AI models have been able to sieve through documents for evidence for legal litigation. AI models have also been able to perform speech recognition that is on par with human transcribers.

In addition, the use of AI models has also become part and parcel of our daily lives, with common applications such as computer vision & facial recognition technologies for temperature sensing, speech recognition for authentication & transcription purposes, travel route finding & optimisation using map services, smart home devices and personal assistance, etc.

With the pervasive and widespread use of AI models, it has become inevitable that the spotlight has been slowly shifting from how AI models can be applied, to building explainability and responsibility into AI systems.

Cases of AI hiccups or unhappiness due to AI bias have been reported in various applications, and with that, there are much debate and call for the need of Explainable & Responsible AI implementation.

In the human resource domain, due to the large volume of applicants received for a job role, there has been an increase in use of AI algorithms to sieve through resumes and to rate candidates suitability for the job role.

The emphasis on responsible AI implementation & explainable AI models are pertinent, in order to promote AI innovation and adoption. It is also required in order to gain trust and confidence in the users of AI models. Therefore, there is a need for further research, development and discussion in the AI community concerning the various aspects. In the recent years, one can see the rise of new innovation and further discussions by the technology giants (such as Google, Meta, Microsoft, IBM etc.) and regulators of various countries, in coming up with software/tools and best practice guidelines to provide guidance on this aspect.