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Continuous monitoring and evaluation of AI systems are necessary to identify and address biases from "summary" of Human-Centered AI by Ben Shneiderman
Continuous monitoring and evaluation of AI systems play a crucial role in ensuring their ethical use. By regularly examining the performance of AI systems, developers can detect and address biases that may arise during the system's operation. Bias in AI systems can have far-reaching consequences, affecting individuals' lives and perpetuating social inequalities. Identifying biases in AI systems requires a proactive approach that involves monitoring the system's outputs and evaluating its decision-making processes. By analyzing the data that the AI system processes and the results it produces, developers can gain insights into any biases that may be present in the system. This ongoing monitoring allows for the timely identification of biases and the implementation of corrective measures to mitigate their impact. Addressing biases in AI systems is essential to ensure fair and equitable outcomes for all users. Biases can manifest in various forms, such as racial, gender, or socioeconomic biases, and can result in discriminatory practices or unfair treatment. By continuously monitoring and evaluating AI systems, developers can take proactive steps to address biases and promote fairness in decision-making processes. Furthermore, continuous monitoring and evaluation of AI systems help to enhance transparency and accountability in their use. By regularly assessing the performance of AI systems and identifying biases, developers can demonstrate their commitment to ethical practices and responsible AI development. This transparency fosters trust among users and stakeholders and promotes the adoption of AI systems in various domains.- Continuous monitoring and evaluation of AI systems are necessary to identify and address biases that may arise during their operation. By proactively monitoring the system's outputs and evaluating its decision-making processes, developers can detect biases and implement corrective measures to ensure fair and equitable outcomes for all users. This ongoing monitoring also helps enhance transparency and accountability in the use of AI systems, fostering trust among users and stakeholders.
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