The artificial intelligence (AI) and machine learning market has been steadily growing over the past decade. If you look back at their track record, AI and machine learning helps decision-makers make educated strategic decisions. However, skeptics still ask if the AI hype is justified?
I guess it depends on who you ask. Understanding AI is hard because it is not one thing, and the technology has many use cases. Responsible AI is the practice of designing, developing and deploying AI with good intentions to empower employees and businesses, and fairly impact customers and society. Responsible AI could be the next big thing, allowing companies to engender trust and scale AI with confidence.
However, they do need some agreed upon principles. AI and the machine learning models that support it should be comprehensive, explainable, ethical and efficient.
No one is denying that building a responsible AI governance framework can be a lot of work. Ongoing scrutiny is crucial to ensure an organization is committed to providing an unbiased, trustworthy AI.
There is also no doubt that AI has become part of the business world and is here to stay. The potential of AI in terms of economic benefits is unrivalled. This emerging technology is even considered by many to be more important and impactful than the internet was. It is also no surprise that AI is increasingly involved in decision-making, either as a tool, advisor or even manager.
This means that today intelligent technology is increasingly acquiring power to influence a wide variety of outcomes important to society. As we all know, with greater power also comes greater responsibility. For this reason, we need to start addressing the question of whether AI is intrinsically equipped to be a responsible actor and as such act in ways that we humans—as the important end-user—consider ethical.
Most organizations have little visibility and knowledge on how AI systems make the decisions they do, and as a result, how the results are being applied in the various fields that AI and machine learning are being applied. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact and potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.
We are the intelligence and the technology behind world-class explainable AI solutions.
Melody K. Smith
Sponsored by Access Innovations, changing search to found.
