5 Principles to Design an AI Platform

1) Design for People: First and foremost, we need to remember that we are designing for people and that all the normal considerations apply. We need to do research and observation and get a really good understanding of the people we are designing for, their context and what the need is that we are trying to create a solution for.

2) Design for Transparent Value: the user interacting with the AI has to have a clear understanding of why they are getting a better outcome than another experience.

AI generated art work by BeepMind

3) Design for Failure: As AI technologies rely on statistical probability, there will be situations where the system acts in the ‘wrong’ way or comes up with an output or decision which isn’t in line with human expectations. As a result, it is important to ensure that the implications of a failure are considered and designed for.

4) Design for Learning: A key part of any machine learning system is its ongoing improvement and adaptation based on usage and increasing data sets in the live environment. This whole process needs consideration to ensure that it continues to improve and not degrade the capability of the system.

5) Design for Ethics: The whole field of ethics around AI and Machine learning is still very new and is the subject of much research and debate. Putting aside some of the more existential questions and focusing on some nearer-term practical considerations for narrow AI, there are a few points worth considering.

Source: http://tom-castle.com/incorporating-ai-into-design/ by https://tom-castle.com/