Image by DCStudio on Freepik

We have tested the Hyperwrite to generate a short post about AI explainability. And, here is three results generated by Hyperwrite: 

1. “AI Explainability is the practice of understanding how Artificial Intelligence arrives at important decisions. It’s about making sure that the decisions made by AI are fair and accurate, and that people can understand how and why the AI made them.”

2. “AI Explainability is a way to make sure that Artificial Intelligence programs follow rules that humans can understand. It’s a way of making sure the machines don’t make decisions that could be harmful, or are not what people want them to do. Explainability helps us to make sure that AI is doing the right thing and being responsible.”

3. “AI explainability is a way of understanding how a computer program makes decisions. It’s like when a person makes a decision, they have to explain why they decided to do something. AI explainability helps us figure out why AI programs made a certain decision.”

This topic is no longer new amongst us. However, the solutions have not been determined and proved yet. A very concise evidence of the Oxford Internet Institute, submitted to the UK Parliament Science & Technology Committee, Governance of AI Enquiry, introduced the measures to improve not only the explainability but also transparency of AI, including “model cards” – developed from the documentation method, “algorithmic registers, binding regulations with appropriate measures for enforcement, counterfactual explanations, amongst others”. 

To read the evidence: https://committees.parliament.uk/writtenevidence/113811/pdf/

To test Hyperwrite: https://hyperwriteai.com/