AI Explained: The BonsXAI Blog
The Pros and Cons of Explainable AI (XAI): Why Accessibility is a Challenge?
Author: Nizar Massouh
Artificial intelligence (AI) has been around for decades, but it's only in recent years that it's been applied in real-world settings. With the introduction of deep learning, the performance of AI has grown exponentially, but it has also made it increasingly opaque. This is where Explainable AI (XAI) comes in. XAI is a new research line that focuses on making AI models more transparent, enabling humans to understand the generated outputs. While XAI has many advantages, accessibility remains a challenge, making it difficult for non-technical people to use.
One of the key advantages of XAI is that it provides an understanding of the "why" behind an AI model's output. As more AI models are integrated into various use cases and driving decisions, a level of transparency and accountability is required. XAI can help achieve this by providing interpretable and transparent models that can be used to understand and explain AI outputs. This is particularly important in the regulation of algorithms, particularly artificial intelligence and its subfield of machine learning, where the right to explanation is essential for decisions that significantly affect individuals, particularly legally or financially.
XAI has various use cases where it will play a big role in AI models' adoption. In healthcare, XAI can accelerate diagnostics, image analysis, resource optimization, and medical diagnosis. It can also improve transparency and traceability in decision-making for patient care and streamline the pharmaceutical approval process. In financial services, XAI can improve customer experiences with a transparent loan and credit approval process, speed up credit risk, wealth management, and financial crime risk assessments, accelerate resolution of potential complaints and issues, and increase confidence in pricing, product recommendations, and investment services. In criminal justice, XAI can optimize processes for prediction and risk assessment, accelerate resolutions using explainable AI on DNA analysis, prison population analysis, and crime forecasting, and detect potential biases in training data and algorithms.
However, there are also some cons to XAI, the primary one being accessibility. XAI tools are currently designed for technical people, which means that non-technical people may find it challenging to use. This is because XAI models are complex, and they require significant expertise in data science and machine learning to be understood and utilized. This makes it difficult for people who are not trained in these fields to take advantage of XAI models.
Another potential disadvantage of XAI is that it may not always be accurate. XAI models rely on the data that is fed to them, and if the data is biased or incomplete, the output may also be biased or incomplete. This can result in decisions that are not fair or accurate, which can be detrimental in certain use cases.
XAI is revolutionizing machine learning by making AI models more transparent and explainable. With the increasing adoption of AI models in various use cases, XAI will play a crucial role in improving transparency, accountability, and trust. However, accessibility remains a challenge, making it difficult for non-technical people to use XAI models. As XAI research progresses, efforts should be made to create more user-friendly interfaces and tools that can be used by people with different levels of technical expertise.
At Bonsxai we are specializing in accessible and non-technical Explainable AI (XAI) solutions, we are dedicated to empowering organizations and individuals to gain deep insights into their data through the power of storytelling. Check our innovative XAI platform that leverages Natural Language Generation (NLG) techniques to transform complex data sets into compelling narratives.
