Artificial Intelligence (AI) has revolutionized various aspects of our lives, including our work. More and more people are incorporating technologies like AI into their daily lives, and it’s becoming almost unimaginable to work without AI. With this comes the crucial need to understand and trust the decisions made by this technology. 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 objectives, reliability, and the areas where it is used.
What is Explainable Artificial Intelligence (XAI)?
Explainable Artificial Intelligence (XAI) is a methodology that allows AI systems to explicitly share their processes and algorithms, making them understandable and reliable for human users.
The functioning of a point-of-sale terminal that rejects a credit card and explains the reason for that rejection is a simple example of a system operating as a ‘black box’. Similarly, while the terminal uses an algorithm to make decisions, Explainable AI also relies on algorithms, albeit more complex, that aim 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 primary goal of XAI is to provide explanations or answers for the decisions made by artificial intelligence. This need becomes increasingly critical in various sectors using these technologies. A user should be able to reason the why behind a decision; similarly, any tool using artificial intelligence should be able to explain its reasoning. For example, a person about to make a financial decision needs to understand the factors that the algorithm has considered critical in reaching 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 more robust justifications. This trust can easily erode when errors are detected, or the system presents faults. Once a system falls into this cycle of distrust, regaining user confidence becomes a challenge.
To trust the decisions of an AI system, it is crucial that the user applies their own judgment to distinguish between logical and 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 mentioned, the human factor is essential for achieving optimal results alongside AI. Humans perceive and process information differently, considering multiple factors that can influence decision-making. This human judgment capability is crucial for evaluating contexts, interpreting emotional nuances, and applying ethical values for regulatory compliance—factors that machines alone cannot fully grasp. The use of these AI systems with Decision Intelligence can provide us with a significant competitive advantage to drive our business forward.
Where and When is Explainable AI Used?
More and more sectors are adopting Explainable AI. This technology is applied in areas such as medicine, where understanding algorithmic decisions in diagnostics and treatments is crucial. It is also used in banking and finance to ensure transparency in credit models, and in the legal field to ensure fair and understandable decisions in predictive justice systems. Explainable AI also finds applications in manufacturing and industry, facilitating data-driven decision-making. To see how IMMERSIA applies this technology, you can check out our ‘Primetals Success Case’.
Benefits of Explainable Artificial Intelligence (XAI)
After this extensive explanation of the different terms, and now that we understand the meanings and implications of each, here are some of the benefits of Explainable AI:
1- Improved Trust: Increases user trust in AI systems by providing clear and understandable explanations of how decisions are made.
2- Enhanced Decision-Making: Provides actionable information and explanations that facilitate informed decision-making.
3- Regulatory Compliance and Bias Mitigation: 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- Improved User Experience and Accessibility: Makes complex AI systems more accessible and easier to use by presenting explanations in natural language and visualizations.
5- Problem Solving Facilitation: XAI allows for observing the calculations and reasoning of AI, which helps detect errors or discrepancies in decision-making. This not only improves system accuracy but also enables experts to guide and adjust AI to better align with their objectives, preventing project failures due to misunderstandings between AI and experts.
6- Empowering Non-Technical Users: Allows users without technical expertise to understand and effectively use AI systems.
As we’ve seen, XAI offers numerous benefits and also faces certain challenges. For AI to be truly trustworthy, it must be transparent, responsible, and ethical. In this regard, Explainable AI plays a crucial role in meeting these requirements. The concept of XAI reflects the commitment to developing AI that is human-centered. By breaking down the ‘why’ behind AI decisions, it allows people to better understand how these technologies work and to meaningfully engage in the digital environment.
