In the ever-evolving landscape of artificial intelligence (AI), the quest for transparency and interpretability has become paramount. Enter explainable AI—a critical field that aims to demystify the inner workings of AI models, making them more comprehensible and accountable to human users. 

At its core, explainable AI refers to a set of techniques, principles and processes that allow humans to understand how AI models arrive at specific decisions. Imagine a scenario where an AI system recommends a loan application rejection. Instead of blindly accepting this outcome, XAI empowers us to dissect the decision-making process, uncover biases and ensure fairness.

Explainable AI isn’t a luxury; it’s a necessity. As AI permeates our lives, understanding its decisions becomes non-negotiable. Whether it’s a medical diagnosis, credit approval or autonomous driving, explainable AI ensures that the AI’s inner workings are transparent, fair and justifiable. So, let’s embrace explainable AI—a beacon guiding us toward responsible and trustworthy AI adoption.

The biggest challenge is that most organizations have little knowledge on how AI systems make decisions and how to interpret AI and machine learning results. 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 it potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.

AI and human skills are not competitors; they are collaborators. Embracing this partnership is crucial for harnessing the full potential of both AI and human capabilities. As technology continues to advance, it is essential to strike a balance that maximizes efficiency and innovation while preserving the unique qualities that make us human. By doing so, we can create a future where AI and human skills coexist harmoniously, driving progress and improving the quality of life for all.

Everyone is looking at AI. Everyone is getting mixed results. The main issue is that data science has not changed, and scientific content is very complex and needs more attention to get the most out of the new AI engines. This is not new for Access Innovations.

Access Innovations knows information science and scholarly publishing. We also know AI and are uniquely positioned to get the most out of the new AI engines. We use various techniques to enhance your data and train focused language models so that you get better results.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.