Description: Unconventional methods are innovative approaches that may not follow traditional guidelines in explainable AI. These methods aim to provide a deeper and more accessible understanding of artificial intelligence models, especially those that are complex and difficult to interpret. They often focus on transparency and interpretability, allowing users to understand how and why certain decisions are made by algorithms. Unlike conventional methods, which may rely on standard techniques and predefined models, unconventional methods can include creative and adaptive approaches, such as data visualization, storytelling, and human interaction. These methods are particularly relevant in contexts where trust and ethics are paramount, such as healthcare, criminal justice, and finance. By providing more intuitive and accessible explanations, unconventional methods can help mitigate bias and increase the acceptance of AI in various applications. In summary, these innovative approaches represent an evolution in how explainability in artificial intelligence is addressed, promoting greater understanding and trust in automated decisions.
