More transparency and trust in AI
AI can present itself as a strange, foreign concept. Can we trust AI to do what it is designed to do? How should organizations address such concerns? As trust is a critical element for every consumer experience, organizations need to build trust in AI right from the start.
Education and the understanding of AI’s current limitations will go a long way to imparting trust in the technology. More real-time monitoring of AI applications to check if they are performing within safe boundaries is necessary too. The risk profile (including ethics, social responsibility, accountability and reliability) of the AI application and its use case will help determine ways to tap its potential. To address the risks associated with discrimination and bias, technology teams will have to work around insufficient data and faulty algorithms. To lessen any perceived lack of humanity, design teams must acknowledge human psychology and behaviour.
Boards must acknowledge that AI is – in many ways – like human intelligence, whereby mistakes are often part of the learning process. Incorporating this expectation and consequent learnings into strategy and processes that will ultimately improve the AI journey will, in turn, lead to heightened trust.