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But the girl, 14-year-old Lamya Robinson, said she has never been to the arena before.
Robinson and her parents told a local television station she was mistaken for another girl, who was banned from the rink for allegedly fighting. That is not me. Who is that? she recalled her confused reaction to Fox 2 Detroit. She says she was kicked out, standing outside alone at night.
Her mother Juliea Robinson called it racial profiling.
Critics of facial recognition technology have said the algorithm is inaccurate when it comes to processing darker skin. Research has found the poorest accuracy consistently found in subjects who are female, Black, and 18-30 years old. The study Gender Shades from
A Hidden Trap for CIOs: Data-set Bias in Machine Learning
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Facebook, predicciones de tu futuro
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Clinical research requires that data be mined for insights. Machine learning, which develops algorithms to find patterns, has difficulty doing this with data related to health records because this type of information is neither static nor regularly collected. A new study developed a transparent and reproducible machine learning tool to facilitate analysis of health information. The tool can be used in clinical forecasting, which can predict trends as well as outcomes in individual patients.
The study, by a researcher at Carnegie Mellon University (CMU), appears in
Proceedings of Machine Learning Research. Temporal Learning Lite, or TL-Lite, is a visualization and forecasting tool to bridge the gap between clinical visualization and machine learning analysis, explains Jeremy Weiss, assistant professor of health informatics at CMU s Heinz College, who authored the study. While the individual elements of this tool are well known, their integration into an interactive cl