Description: Tactical approaches in explainable artificial intelligence (XAI) refer to the strategies employed to enhance the understandability and transparency of AI systems. These approaches aim to break down the decision-making processes of algorithms, allowing users to comprehend how and why certain outcomes are generated. XAI is crucial in various contexts where automated decisions can significantly impact people’s lives, such as in healthcare, criminal justice, and finance. By implementing tactical approaches, the goal is not only to increase user trust in AI systems but also to facilitate the identification of biases and errors in models. The main characteristics of these approaches include data visualization, the generation of explanations in natural language, and the implementation of more interpretable models. In a world where AI is increasingly present, the need for tactical approaches becomes essential to ensure that these technologies are used ethically and responsibly, promoting more effective interaction between humans and machines.
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