Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead · HackerPapers discussions

Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead — mentioned by koutetsu

Thu, 04 Apr 2024 13:06:34 GMT

Looking at this from a machine learning perspective, the risk of biases is even higher in these cases because of issues with data drift (Members could change sites, they could start dressing differently, etc.) and imbalances in the dataset (A lot fewer Hamams members than civilians in Gaza). Additionally, juging from the amount of data such models would have to go through in order to make predictions (social media, camera footage, etc.) I would assume that they are using neural networks. This type of model performs best without raw unprocessed data e.g. raw camera footage instead of…

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Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead — mentioned by apwheele

Tue, 14 Jun 2022 12:03:55 GMT

Yeah, currently I do things like "Top 5 important factors" for shapely values, but have debated on even doing that much. There is good evidence these reduced form estimates can often be misleading, https://arxiv.org/abs/1811.10154. So it may just be a hopeless task anyway.

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Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead — mentioned by pjmorris

Tue, 12 Oct 2021 20:18:54 GMT

From the linked paper, linked again [0] below, I think this represents Rudins philosophical view, and why it could be practical: Here is the Rashomon set argument: Consider that the data permit a large set of reasonably accurate predictive models to exist. Because this set of accurate models is large, it often contains at least one model that is interpretable. This model is thus both interpretable and accurate. Unpacking this argument slightly, for a given data set, we define the Rashomon set as the set of reasonably accurate predictive models (say within a given accuracy from the best model…

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Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead — mentioned by walnut_eater

Tue, 12 Oct 2021 11:22:44 GMT

Her most cited paper is https://arxiv.org/abs/1811.10154 and it references a lot of her other work. It provides a good representation of what she does and what she is well known for.

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Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead — mentioned by qPM9l3XJrF

Tue, 12 Oct 2021 10:53:05 GMT

Cynthia has a great paper where she distinguishes between "interpretable" and "explainable" https://arxiv.org/pdf/1811.10154.pdf

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Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead — mentioned by eggy

Sat, 11 Apr 2020 18:10:17 GMT

I think someone has translated this to J, but I am trying on my own to practice my J-fu by implementing it in my own way. Then I usually open it up to the J experts on the mailing list, and my learning takes off. There are some awesomely smart people there who are generous with their time. Yes, the takeaway is that with APL or J is that you can see the mechanics in a paragraph of code, and it is not a very trivial example. If the libraries or verbs are created to deal with some of the speed or efficiency issues, it is promising as a way of understanding the concept better. The dataframes of R…

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Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead — HN submission

Wed, 19 Feb 2020 17:57:46 GMT

Stop Explaining Black Box Machine Learning Models, Use Interpretable Models [pdf]

2 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead — mentioned by phab

Fri, 01 Nov 2019 12:31:48 GMT

Actual arXiv link: https://arxiv.org/abs/1811.10154

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Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead — HN submission

Thu, 31 Oct 2019 16:40:48 GMT

Stop Explaining Black Box Models for High Stakes Decisions

150 points; 63 discussion replies on HN. Paper and source history. A submission is not an endorsement.