The need for Explainable AI arises from the desire to enhance trust, enable human oversight, and address potential ethical concerns. By providing insights into the decision-making process, XAI empowers users to understand why an AI system reached a particular outcome. It allows stakeholders to iden...
What has become widely acknowledged is that (i) XAI alone is not enough for trustworthiness and that (ii) there is a need to shed light on the connection of XAI with the other requirements of trustworthy AI. Recently, some studies have started to look into these matters. For instance, ...
Explainable artificial intelligence (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact and potential biases. It helps characterize...
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An AI system should be able to explain its outcome. This principle doesn’t imply the correctness of the results; it just means that AI models can justify their recommendations. Explanations can come in different shapes depending on the target audience—end users, developers, model owners, etc....
ensuring AI/CI systems are understandable, monitored, and reliable, we can make decisions that comply with regulations and ethical principles, thereby reducing risks and biases. This empowers users to engage with these technologies confidently, promoting a harmonious integration of AI into daily life....
To promote the integration of AI, XAI strives to improve transparency by devising methods that empower end users to understand, place confidence in and efficiently control AI systems (Adadi and Berrada 2018; Arrieta et al. 2020; Saeed and Omlin 2023). Early work on rule-based expert systems...
. Most XAI is focused on direct users or developers, but it can also be targeted at other stakeholders when relevant, such as policy makers or users' families. What exactly constitutes an AI system is in itself difficult to define, as there is no clear consensus on what AI is. For this...
This motivates the inherent need and expectation from human users that AI systems should be explainable to help confirm decisions. Explainable AI (explainable artificial intelligence (XAI)) is often considered a set of processes and methods that are used to describe deep learning models, by ...
● Manipulate people's decisions: XAI could be used to generate explanations that are deliberately misleading or biased, nudging users towards specific choices that serve the manipulator's interests. ● Conceal harmful biases: Malicious actors could exploit XAI to mask biases within AI systems, making...