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IBM defines explainable artificial intelligence (XAI) as a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms. XAI describes an AI model, its expected impact, and potential biases.
AI algorithms often operate as “black boxes” that take input and provide output with no way to understand their inner workings. In simple terms, black box is an impenetrable system. The goal of XAI is to make the rationale behind the output of an algorithm understandable to humans.
How Does Explainable AI Work?
XAI can improve the user experience of a product or service by helping the end user trust that the AI is making good decisions. As AI becomes more advanced, humans must understand and control machine learning (ML) processes to ensure accurate AI model results.
One can divide XAI into three categories:
- Explainable data- what data went into training a model? Why was that data chosen? How was fairness assessed? Was any effort made to reduce bias?
- Explainable predictions- What features of a model were activated or used to reach a particular output?
- Explainable algorithms- What are the individual layers of the model, and how do they lead to the output or prediction?
Explainable models are sometimes referred to as “white box” models. White box AI is transparent about how it comes to conclusions. Humans can look at an algorithm and understand its behavior and the factors influencing its decision-making.
Therefore, white box algorithms give a result and clearly readable rules.
White box AI tends to be more practical for businesses since a company can understand how these programs came to their predictions, making it easier to act on them. Companies can use them to find tangible ways to improve their workflows and know what happens if something goes wrong.
Two key elements make a model white box: features must be understandable, and the ML process must be transparent.
Why is Explainable AI Important in Business?
XAI helps characterize model accuracy, fairness, transparency, and outcomes in AI-powered decision-making. With XAI, humans can gain insights into the rationale behind AI recommendations and decisions, making them more trustworthy and understandable.
XAI also helps an organization adopt a responsible approach to AI development by quickly helping analysts understand system outputs, overcoming false positives, giving confidence in the AI diagnosis, and reducing the need to hire highly skilled data scientists.
Recent research shows that companies seeing the biggest bottom-line returns from AI, those that attribute at least 20% of earnings before interest and taxes (EBIT) to their use of AI, are more likely than others to follow best practices that enable explainability.
Further, organizations that establish digital trust among consumers by making AI explainable are likelier to see their annual revenue and EBIT grow by 10% or more.
Explainable AI is still early in adoption. However, Garner found that about 30% of government and large enterprise contracts require explainable and ethical AI to purchase AI products and services by 2025.
“Black box AI models are not necessarily bad – a black box AI designed to recommend a product to buy or a book to read is relatively safe to use. But when it comes to AI that can materially impact our lives or livelihoods, like in talent acquisition, it’s important that organizations prioritize the explainability and transparency of their AI tools for both ethical and legal reasons,” William Rose, CTO at Talent Select AI, said in a recent interview.
“If an AI model helps make a hiring decision, it’s critical that we understand and articulate why that decision or recommendation was made.”
Talent Select AI’s patent-pending technology was developed alongside world-renowned industrial organizational (IO) psychology researchers, using the latest breakthroughs and research in IO, data science, and machine learning to provide hiring decision-makers with meaningful and predictive candidate analytics so that you can identify and hire your ideal candidates faster.
Building on decades of established research in language psychology, our innovative AI technology leverages cutting-edge IO psychology science, using Natural Language Processing (NLP) to analyze the specific words candidates use to provide an accurate, objective, and unbiased assessment of their unique skills, personality traits, and professional competencies.
At Talent Select AI, we're committed to providing explainable AI across all our products. If any questions arise, we'll show you how the decisions were made.
Contact us today to see how we do it.
