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Using Language Models to Simulate Human Samples: Acknowledgments and References
This story was originally published on HackerNoon at: https://hackernoon.com/using-language-models-to-simulate-human-samples-acknowledgments-and-references.
Explore the impact and challenges surrounding the adoption of datasheets for datasets.
Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories.
You can also check exclusive content about #ai-training-data, #data-provenance, #mitigating-bias-in-ai, #ai-transparency, #ai-ethics, #machine-learning-models, #datasheets-for-datasets, #ai-data-documentation, and more.
This story was written by: @textmodels. Learn more about this writer by checking @textmodels's about page,
and for more stories, please visit hackernoon.com.
Datasheets for datasets have gained traction across academic and industry settings, fostering transparency and accountability. While implementation challenges exist, the benefits of improved communication and accountability outweigh the costs, driving adoption and evolution in dataset creation practices.
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By HackerNoonThis story was originally published on HackerNoon at: https://hackernoon.com/using-language-models-to-simulate-human-samples-acknowledgments-and-references.
Explore the impact and challenges surrounding the adoption of datasheets for datasets.
Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories.
You can also check exclusive content about #ai-training-data, #data-provenance, #mitigating-bias-in-ai, #ai-transparency, #ai-ethics, #machine-learning-models, #datasheets-for-datasets, #ai-data-documentation, and more.
This story was written by: @textmodels. Learn more about this writer by checking @textmodels's about page,
and for more stories, please visit hackernoon.com.
Datasheets for datasets have gained traction across academic and industry settings, fostering transparency and accountability. While implementation challenges exist, the benefits of improved communication and accountability outweigh the costs, driving adoption and evolution in dataset creation practices.