http://confocal-microscopy-list.275.s1.nabble.com/Digital-imaging-ethics-as-pertaining-to-the-enhancement-of-microscopy-images-with-artificial-intellie-tp7588915p7588916.html
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> Earlier today a few people (including myself) brought up Doug Cromey's excellent treatise on digital imaging ethics in a related thread that dealt with training new microscope users within a research setting. Lately I've been hearing a lot about applications of machine learning and artificial intelligence to "improve", "de-noise", or "fix" images (microscopy or otherwise), extracting new information from low-resolution images, and even creating new 3D views of samples with very little information. Here is just one such example from Nvidia and MIT:
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https://news.developer.nvidia.com/ai-can-now-fix-your-grainy-photos-by-only-looking-at-grainy-photos/>
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https://www.youtube.com/watch?time_continue=84&v=pp7HdI0-MIo>
> It's clear that the microscopy world will eventually come to a head with this technology. I think I've seen a few research articles on this topic now, and this month's issue of Nature Methods has a paper on this topic too:
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https://www.nature.com/articles/s41592-018-0194-9>
> I've been wondering if and how Cromey's guide for digital imaging ethics should be altered when it comes to AI-assisted microscope imaging. Should it be allowed/accepted? Other readings of mine on AI show that machine learning algorithms can produce biased results if the training datasets are incomplete in some way, and the very nature of machine learning makes it difficult to understand why it produced a certain result, since the deep learning neural networks that are used to generate the results are essentially black boxes that can't easily be probed. But on the other hand, I'm constantly blown away by what I've seen so far online for other various applications of AI (facial recognition, translation, etc.).
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> I also just finished a good read about AI from the perspective of economics:
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https://www.predictionmachines.ai/>
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https://youtu.be/5G0PbwtiMJk>
> The basic message of this book is that AI makes prediction cheap. When something is cheap, we use more of it. Other processes that complement prediction, like judgement (by a human or otherwise) becomes more valuable. It's easy to see how the lessons of this book could be re-framed for imaging science.
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> Curious to know the community's opinion on this matter. I used to laugh at the following video, but now I'm not laughing:
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https://www.youtube.com/watch?v=LhF_56SxrGk>
> John Oreopoulos