You are currently viewing Building Trustworthy AI

Building Trustworthy AI

Trustworthy AI, what is it?

Trustworthy AI is made up of several key components: accountability, reliability, transparency and fairness. These aspects must be present in every step of the AI development lifecycle to ensure that the end result is one that assists it’s users in a way that is positive, fair and transparent.

How can it be achieved?

1. Adopting Ethical Frameworks

Get into the habit of learning and keeping up to date with ethical frameworks designed for AI development, such as the Ethical Application of AI Index (EAAI), the Fairness Flow, Model Cards and so on. These frameworks were developed to assist AI developers in creating AI systems according to a structured approach that takes ethics into consideration.

2. Focusing on Data Governance

As we are all aware, data is the foundation of AI, therefor AI developers must ensure the data being used is accurate, complete and of high quality.  This can be achieved through meticulous data collection methods, using a wide range of training datasets and keeping strict record of each dataset used, its origin, its purpose and so on. Regular audits can also be carried out to monitor the accuracy of the data and to pick up on any potential biases present in the datasets.

3. Emphasise Transparency

When it comes to AI, transparency is a crucial factor needed to build trust. Many users want more than just an answer or a decision, they want to understand how the AI system arrived at that particular answer / decision. Transparency can be achieved through the use of explainable AI (XAI) and a visualization of the path that the AI followed to reach its answer / decisions.

Building trustworthy AI is a collective effort of prioritizing accountability, reliability, transparency and fairness. As we advance into the future of AI, prioritizing trustworthiness will be essential in the continued adoption of AI into society.