Except if it's used in security camera, it's going to be a disaster. Those things have super low resolution, and software will be cheaper than upgrading. And models have huge bias.
Tons of fun.
Personally, I think one reason we still do this has a lot to do with detective shows being so ridiculously popular that people think that it's some sort of scientific process, when it's not. As a thought experiment, we'd probably have flat-earther-ism be the dominant belief if was like 15/20 broadcast TV shows are dedicated to glorifying flat-earther-ism. This means the most dangerous thing about "Super Resolution" is the public has already been "primed" with cop shows having the "enhance" feature.
The bigger problem is informal settings. Propaganda, for one.
Forensic evidence has been and still is systematically abused:
> * a 2002 FBI re-examination of microscopic hair comparisons the agency’s scientists had performed in criminal cases, in which DNA testing revealed that 11 percent of hair samples found to match microscopically actually came from different individuals;
> * a 2004 National Research Council report, commissioned by the FBI, on bullet-lead evidence, which found that there was insufficient research and data to support drawing a definitive connection between two bullets based on compositional similarity of the lead they contain;
> * a 2005 report of an international committee established by the FBI to review the use of latent fingerprint evidence in the case of a terrorist bombing in Spain, in which the committee found that “confirmation bias”—the inclination to confirm a suspicion based on other grounds—contributed to a misidentification and improper detention; and
> * studies reported in 2009 and 2010 on bitemark evidence, which found that current procedures for comparing bitemarks are unable to reliably exclude or include a suspect as a potential biter.
> Beyond these kinds of shortfalls with respect to “reliable methods” in forensic feature-comparison disciplines, reviews have found that expert witnesses have often overstated the probative value of their evidence, going far beyond what the relevant science can justify.
(https://web.archive.org/web/20170120002449/https://www.white... page 16)
Even more:
* Tire and shoe prints: https://www.apmreports.org/story/2016/09/27/questionable-sci...
* Lie detector tests: https://en.wikipedia.org/wiki/Polygraph#Effectiveness
* Burn patterns: https://www.pbs.org/wgbh/frontline/article/forensic-tools-wh...
On the first day of trials of deep-learning based facial recognition here, a random person was arrested because the algorithm confused that person with another one.
Even more stupid, is that the person with "outstanding warrant" was actually ALREADY in prison.
So yes, AI managed to arrest the same person, twice, one time the real person, one time a random look-alike.
So yeah, real people have been harmed by bad matching algorithms.
<Insert other race> all look the same: https://onlinelibrary.wiley.com/doi/abs/10.1002/acp.898
This bus is an ostrich: https://arxiv.org/abs/1312.6199
Racist autofocus: https://sitn.hms.harvard.edu/flash/2020/racial-discriminatio...
Google thinks black people look like gorillas: https://www.wired.com/story/when-it-comes-to-gorillas-google...
And here is an argument to look forward to: should the training dataset be build to represent the general population, or the specific subgroup that the software would be most often encountering? Because that FBI UCR...
I imagine this will tend to reproduce things in the dataset, e.g. up-scaling blurry text may look like fonts that it has memorized more than the original. Or upscaling a feather will provide details like the feather of more common birds. Or upscaling blured out numbers will pick some numbers at random [1].
We need to make sure people don't rely on these details, e.g. in courts, HR reviews, when reddit sleuths try and investigate an incident, when someone looks for cheating partners etc.
[1] https://www.theregister.com/2013/08/06/xerox_copier_flaw_mea...