With AI becoming more ingrained in our lives, some ethical questions have been raised about its use. Is the AI fair and reliable? Is it free from bias? Is it understandable? And finally, who is accountable for it?
As Hans De Graaf talks about in Copyright, Ethics, Biases, and AI’s Climate Impact, we don’t want to deal with a ‘black box’. This is a system where you only see what goes in and what comes out, but not how it made that decision. AI should be able to tell you how it made that decision and explain its reasons.
You should also inform people when AI is involved in your decision-making process or is being used in your products. That gives people the opportunity to ask questions or object. Be clear about the data used, the training method and the process so your users can understand how it works, evaluate its functionality, and understand its strengths and limitations.
AI should be built with equitable data, so any decision it makes is as free from bias as possible. This will ensure any answers delivered by the AI are ethical and fair. It should also be assessed for its environmental impact. For example, the computing power needed to train large data sets can emit more CO2 than the lifetime emission of a car.
While AI regulation by governments is growing more common, it’s important to ensure that you build AI with an ethical framework and accountability in mind. There should be oversight, impact assessments, continuous audits, and checks and balances in place to avoid issues. There are many tools to help you – such as UNESCO’s Ethical impact assessment.
Remember, AI is a tool. And it we need to make sure the work it does is done fairly, responsibly and without bias.
Listen to Hans De Graaf as he delves further into ethical AI as well as copyright law and the climate impact of AI in his podcast Copyright, Ethics, Biases, and AI’s Climate Impact.
