Editing Talk:2494: Flawed Data

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For the [[1258: First|first]] time in a very long time I was the [[269: TCMP|first]] to make an attempt at the main explanation. I guess this comic came out very late then? Or just late up on explain xkcd? Seems like the Monday comic first came up on Tuesday in many countries including those in Europe. But guess it was still Monday in the US, at least in the western parts? I hope this is not as bad an attempt as Cueball's research strategies in the last panel :-) --[[User:Kynde|Kynde]] ([[User talk:Kynde|talk]]) 07:05, 27 July 2021 (UTC)
: Isn't it related to a recently published article[https://www.laboratoryequipment.com/577819-AI-Models-to-Analyze-Cancer-Images-Take-Shortcuts-that-Introduce-Bias/ [1]][https://www.nature.com/articles/s41467-021-24698-1 [2]] about bias introduced into AI by humanly-biased data?
::Reports of bias in AI have been in the news for several years. Most notably, facial-recognition systems that are bad at distinguishing faces of black and brown people. [[User:Barmar|Barmar]] ([[User talk:Barmar|talk]]) 13:58, 27 July 2021 (UTC)
This explanation has some good ideas for what these things mean, but it goes into them in excessive detail, which doesn't leave a lot of room for other ideas to be included side-by-side.  I think that might be common.  I was just thinking that there are a lot of ways extra math is used to produce worse conclusions: as soon as you have to work more to find what is good, occam's razor says you are less likely to be relevant.  Similarly there are a lot of ways that AI is used to work with data, but its power greatly surpasses its ability to reflect the underlying meaning of things.  For example, the existing data can be extended without being scrapped, and look completely real in every known respect, but that doesn't mean that any new information is included in what is generated, since the only data to work with is what was already there.  [[Special:Contributions/108.162.219.98|108.162.219.98]] 16:19, 27 July 2021 (UTC)

There are various techniques in Machine Learning to augment the training data, which can include generating fake data that looks like the real data; one such technique is using {{w|Generative adversarial network}} (GAN).

On first reading, I was thinking the Good approach would be to go out and run new experiments and measurements, incorporating the lessons from their flawed data, to avoid making the same mistakes again.  This can be quite expensive, but it is really the only way to increase the validity of the data.  Just saying "We can't trust our conclusions," throws away the opportunity to learn from earlier mistakes and come up with better measurements next time.  [[User:Nutster|Nutster]] ([[User talk:Nutster|talk]]) 14:38, 28 July 2021 (UTC)


Is this a reference to Biogen? Doing some motivated post hoc subgroup analysis to get Aduhelm approved.

From what I know, the "very bad" approach is becoming common in data science, see the Wikipedia page for {{w|Synthetic_data#Synthetic_data_in_machine_learning}} or, when done on a single feature at a time, {{w|Imputation_(statistics)}}. The reason imputation can be problematic because the data is missing due to some confounding variable, so trying to fill in based on existing values will bias the results. A slightly related example is for class imbalance, where some groups are underrepresented and therefore won't be predicted as accurately as overrepresented groups. Instead of gathering more data, especially more representative data, data scientists will often use something like SMOTE to generate more data.  An example of a widely used but frankly bad synthetic dataset is kddcup99.  [[Special:Contributions/172.68.142.147|172.68.142.147]] 05:25, 11 August 2021 (UTC)

Super Bad: So we used a RNG to make completely random data

Ultra Bad: So I just picked my favorite numbers to use as data







The joke here, is, that adding the artificial intelligence variable is going to get things really screwed up!