Transparency. For a while now, it has been the case that within the traditional AI models, which in particular are the deep learning networks, we have “black box” systems that even their creators can not always trace in terms of how they come to a certain decision. This has brought in the growth of Explainable AI (XAI), a very large step forward that also marks the future of what innovation will look like in many fields.
Understanding Explainable AI
Explainable AI is a term used for those techniques and models that make the AI machine processes open for human understanding. Instead of just presenting an output, XAI systems also present the reasoning that led to a certain result. This transparency in the process builds trust, enables better issue diagnosis, and also sees to it that AI decisions are in compliance with ethical, legal, and professional rules.
From which we see that in images which parts of the picture caused a model to determine it was a dog or a cat to text that goes along with approval of a loan application, XAI does the job of making machine intelligence accessible to human understanding.
Driving Trust and Adoption
Trust is of the essence when it comes to the use of AI in health care, legal and financial fields. For example, in health care which is a medical AI puts forth a certain treatment plan, we see that doctors and patients’ buy-in to that is very much a factor. Also if the AI is not explainable, then adoption and use of these solutions will be low which in turn limits what we as a whole can get out of it.
Explainable AI is what builds that trust by providing clarity. Which organizations adopt XAI do better at meeting regulatory requirements, we also see them addressing ethical issues head-on and in turn see greater adoption of their AI-based solutions.
Fueling Better Innovation
When developers and researchers are able to see the what and why of AI model decision making they are able to identify errors, also biases and areas for improvement more easily. Explainable AI speeds up the innovation process by making the AI development more of a iterative process as opposed to a guess at what is going on.
Also in what XAI does is open up new fields for collaboration between humans and AI. We see that with explainable insight into model performance which in turn domain experts, be they doctors, lawyers, or engineers, may put forth very relevant input to improve AI systems, which in turn produce smarter and more effective technologies.
Enhancing Ethical and Fair AI
Bias issues in AI have become a large-scale problem. What we see is that models that are trained on unrepresentative data sets put out harmful stereotypes and also produce unfair results. In the area of Explainable AI we see a very important solution. By making the inner work of AI systems transparent XAI promotes accountability and fairness which are very much at the core of responsible AI innovation.
Future in play are rules which may see also to it that we have certain elements of explainability, in which case XAI will not be a choice for the best practices’ community but a requirement for companies which wish to be at the front of the pack.
