Sep 11, 2024
Artificial intelligence has revolutionized many aspects of our lives, including the way we work. More and more of us use technologies such as AI in our daily routines, and it has become almost unimaginable to work without it. As a result, the need to understand and trust the decisions made by this technology is crucial. This is where Explainable Artificial Intelligence, or XAI, comes into play. This methodology promises to transform AI into a tool that is not only powerful, but also understandable and trustworthy for users. In this blog, we will explore XAI, its purpose, reliability, and the areas in which it is used.
What is explainable AI?
Explainable Artificial Intelligence (XAI) is a methodology that enables AI systems to explicitly share their processes and algorithms, making them understandable and reliable for human users.
The operation of a payment terminal that rejects a credit card and explains the reason for the rejection is a simple example of a system that operates as a “black box.” Just like in this case, where the payment terminal uses an algorithm to make decisions, explainable AI also relies on algorithms, although much more complex, that aim to provide clarity about their decision-making processes. In AI, this delves into machine learning, and more specifically, Deep Learning.
What is the goal of Explainable AI/XAI?
The main objective of XAI is to provide explanations or answers for the decisions made by artificial intelligence. This need is increasingly critical across the many sectors that use these technologies. A user must be able to reason about why a decision was made; similarly, any tool that uses AI should be capable of explaining its reasoning.
For example, someone who is about to make a financial decision needs to understand the factors that the algorithm has identified as critical in reaching that conclusion before proceeding.
How reliable is XAI?
Trust in AI can vary among users. Some may fully trust the statements or information provided by AI simply because they come from a computer or “intelligent system,” while others may require more solid justifications. This trust can quickly deteriorate when errors begin to appear, when the system fails, and so on. And once a system enters this cycle of distrust, regaining user confidence becomes a challenge.
To trust the decisions of a machine that uses AI, it is essential for the user to apply their own judgment to distinguish between what is logical and illogical. Although the ability of these systems to explain their reasoning is a significant advancement, the user must verify that this explanation is correct.
The importance of the human factor
As mentioned above, the human factor is essential for achieving optimal results when working alongside AI. Human beings perceive and process information differently, considering multiple factors that can influence decision-making. This human judgment is crucial for evaluating context, interpreting emotional nuances, and applying ethical values for regulatory compliance, areas that machines alone cannot fully understand.
Using these AI systems with their built-in decision intelligence can provide a major competitive advantage to drive our businesses forward.
Where and when is Explainable AI used?
More and more sectors are adopting explainable artificial intelligence. This technology is applied in areas such as medicine, where understanding algorithmic decisions in diagnoses and treatments is crucial. It is also used in banking and finance to ensure transparency in credit models, as well as in the legal field to guarantee fair and understandable decisions in predictive justice systems.
Explainable AI also finds applications in sectors such as manufacturing and industry, supporting data-driven decision-making. If you want to know how IMMERSIA applies this technology through its digital twins platform TOKII, you can check out our ‘Primetals Success Case.’
Benefits of XAI
After this long explanation of the different concepts, and now that we clearly understand their meanings and implications, here is a summary of some of the benefits of explainable AI:
1- Improved trust: Increases user confidence in AI systems by providing clear and understandable explanations of how decisions are made.
2- Better decision making: Offers actionable information and explanations that support informed decisions.
3-Regulatory compliance and bias mitigation: Facilitates regulatory compliance by making AI decisions transparent and justifiable. It also helps identify and correct biases by offering clarity about the decision-making process.
4- Enhanced user experience and accessibility: Makes complex AI systems more accessible and easier to use by presenting explanations in natural language and visual formats.
5- Improved problem-solving: XAI enables users to observe the AI’s calculations and reasoning, helping detect errors or discrepancies in its decisions. This not only improves system accuracy but also allows experts to guide and adjust the AI to better align with their goals, preventing project failures caused by misunderstandings between AI and experts.
6- Empowerment of non-technical users: Allows users without technical backgrounds to understand and effectively use AI systems.
As we have seen, XAI offers numerous benefits while also facing certain challenges. For AI to be truly trustworthy, it must be transparent, accountable, and ethical. In this regard, explainable artificial intelligence plays a crucial role in meeting these requirements. The concept of XAI reflects a commitment to developing AI that is centered on human needs. By breaking down the “why” behind AI decisions, it enables people to better understand how these technologies work and to participate meaningfully in the digital environment.
