The Ethical Threat of Artificial Intelligence in Practice
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How do clinicians set rules that allow professionals to make good use of technology to find patterns in complex data but also stop companies from extracting unethical value from those data? Everyone — companies, researchers, and governments — are "starved" for the data that algorithms depend on. The problem is that datasets from academic institutions, where data are often collected, are generally very homogeneous.
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@piya Diverse data are needed to train the algorithm to have relevant results. What's more, for machine learning to be truly valuable, a whole lot of data, usually from several sources, is needed, and that means that data need to be released out into the wild. And there's no data scientist willing to guarantee that it's safe once it's out there. Bias in the data can have damaging consequences. What algorithms today are really good at doing is predicting trends, he explained. But if you input data that are biased, your output will also inherently be biased.