Accountability and Transparency

AI and NLP systems are increasingly being used to make important decisions that affect people's lives, such as hiring, lending, and criminal justice. However, these systems can sometimes be opaque and difficult to interpret, which raises questions about accountability and transparency. For example, if a machine learning model denies a loan application or recommends a harsher sentence, how can the decision be explained or challenged? To address these issues, researchers and practitioners are exploring ways to improve the interpretability of AI systems, such as explainable AI, counterfactual analysis, and model debugging. Moreover, they are advocating for greater transparency in the development and deployment of these technologies, such as disclosing the training data, algorithms, and performance metrics used to evaluate them.
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