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N. Vithya, N. Subha, S. Pathur Nisha, A. Balthilak
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Unresolved referenced work
2019
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2019
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The global landscape of AI ethics guidelines
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On the ethics of algorithmic decision-making in healthcare
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Datasheets for datasets
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Bias in computer systems
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