Sep 11, 2024
Explainable artificial intelligence (XAI) ensures transparent and reliable decisions, enhancing trust and informed decision-making across multiple sectors.
Artificial intelligence has revolutionized various aspects of our lives, including work. More and more people are using technologies such as Artificial Intelligence in our daily lives, and it seems almost unimaginable to work without the use of AI. Thus, the need to understand and trust the decisions made by this technology is crucial. This is where Explainable Artificial Intelligence (XAI) comes into play. This methodology promises to transform AI into a tool that is not only powerful but also understandable and reliable for users. In this blog, we will explore XAI, its purpose, reliability, and the areas where it is used.
What is Explainable AI?
Explainable artificial intelligence (XAI) is a methodology that allows AI systems to explicitly share their processes and algorithms, making them understandable and trustworthy for human users.
The operation of a card reader that rejects a credit card and explains the reason for that rejection is a simple example of a system that operates as a 'black box'. Just like in this case, where the card reader uses an algorithm to make decisions, explainable artificial intelligence also relies on algorithms, albeit more complex ones, that seek to provide clarity about their decision-making processes. In the case of AI, we delve into machine learning, and more specifically, deep learning.
What is the goal of Explainable AI/XAI?
The main goal of XAI is to provide explanations or answers for the decisions made by artificial intelligence. This need becomes increasingly critical in various sectors that use these technologies. A user must be able to reason the why behind a decision; similarly, any tool that uses artificial intelligence should be able to explain its reasoning. For example, a person about to make a financial decision needs to know the factors that the algorithm has considered critical to reach that conclusion before proceeding.
How reliable is XAI?
Trust in AI can vary among users. Some may fully trust the claims or information provided by AI simply because it comes from a computer or an 'intelligent system', while others may require stronger justifications. This trust can easily crumble when errors start to emerge, the system fails, etc. And once a system falls into this cycle of distrust, regaining user trust becomes a challenge.
To trust the decisions of a machine that uses AI, it is crucial that the user applies their own judgment to distinguish between the logical and the illogical. Although the ability of these systems to explain their reasoning is a significant advancement, the user must verify that the explanation is correct.
The importance of the human factor
As we have mentioned, the human factor is essential for achieving optimal results in conjunction with AI. Humans perceive and process information differently, considering multiple factors that can influence decision-making. This human judgment ability is crucial for evaluating contexts, interpreting emotional nuances, and applying ethical values for regulatory compliance that machines, by themselves, cannot fully comprehend. The use of these AI systems with their Decision Intelligence can provide us with a significant competitive advantage to drive our business.
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 it is crucial to understand the decisions of algorithms in diagnoses and treatments. It is also used in banking and finance to ensure transparency in credit models, as well as in the legal field to ensure fair and understandable decisions in predictive justice systems. Explainable AI also finds applications in sectors such as manufacturing and industry, facilitating 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 terms, and now that we fully understand the meanings and implications of each, here we summarize some of the benefits of explainable AI.
1- Improvement of Trust: Increases user trust in AI systems by providing clear and understandable explanations of how decisions are made.
2- Improvement of Decision Making: Provides actionable information and explanations that facilitate informed decision-making.
3- Compliance with Regulations and Mitigation of Biases: Facilitates compliance with regulations by making AI decisions transparent and justifiable. It also helps identify and correct biases by offering clarity on the decision-making process.
4- Improvement of User Experience and Accessibility: Makes complex AI systems more accessible and easier to use by presenting explanations in natural language and visualizations.
5- Facilitates Problem Solving: XAI allows for observing the calculations and reasoning of AI, which makes it easier to detect errors or discrepancies in its decision-making. This not only improves the accuracy of the system but also allows experts to guide and adjust the AI to better align it with their goals. Thus, it prevents project failures due to misunderstandings between AI and experts.
6- Empowerment of Non-Technical Users: Allows users without technical experience to understand and effectively use AI systems.
As we have seen, XAI offers numerous benefits and also faces certain challenges. For AI to be truly reliable, it is essential that it be transparent, accountable, and ethical. In this sense, explainable artificial intelligence plays a crucial role in meeting these requirements. The concept of XAI reflects the commitment to develop AI that focuses on human beings. By breaking down the 'why' behind AI decisions, it allows people to better understand how these technologies work and engage meaningfully in the digital environment.
