Salary automl fairness #1836
Salary automl fairness #1836
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:17Z I think we should clearly call out that we are trying to see if there is any bias towards a specific gender either in this section or in the next before we proceed further. This will set the context and problem statement upfront |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:17Z We can remove unwanted/unused imports here moonlanderr commented on 2024-07-17T04:47:02Z removed unwanted |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:18Z Incomplete sentence : Using this dataset we will attempt to train a model to predict
Also, any reason why we chose to keep only 104 records?
moonlanderr commented on 2024-07-17T04:46:48Z corrected. KarthikDutt commented on 2024-08-21T04:29:58Z The dataset consists of 32,561 records, with 21,790 males and 10,771 females, suggesting a bias favoring males. THis statement is incorrect. The dataset is biased because males are more likely to be classified as earning more than 50k compared to women and not because there is less representation of women in the dataset. moonlanderr commented on 2024-09-06T05:42:17Z corrected |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:19Z Before building the model, we might also consider showing if the data is inherently biased. This can be done by applying some formulas to calculate DPR and EOR. By doing so the story will be:
moonlanderr commented on 2024-07-17T04:49:48Z Now I have shown this by just breaking up the male vs female counts , with showing male bias KarthikDutt commented on 2024-08-21T04:30:20Z Please refer to previous comment. moonlanderr commented on 2024-09-06T05:45:20Z added a basic analysis of male vs female higher salary stats that indicate bias |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:20Z Brief description on what is happening during prepare_data, fit, score, report would be useful if there any users who are first time users of AutoML api. moonlanderr commented on 2024-07-17T05:39:52Z added descriptions |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:20Z vanilla-trained may not be the correct term. moonlanderr commented on 2024-07-17T05:39:33Z replaced vanilla |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:21Z Brief explanation of the curves would be good to have moonlanderr commented on 2024-07-17T06:26:00Z added briefly, along with the link to refer for more detail |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:22Z Since we are bringing in the terms eqr and dpr, we must explain here what these terms mean. moonlanderr commented on 2024-07-17T06:34:15Z added the how automl and fairness works, where users can check the detail |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:23Z Here, we should also explain specific to this example/dataset, what EOR and DPR mean. For ex -
moonlanderr commented on 2024-07-17T07:18:37Z added brief explanation |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:23Z Here we will need to talk about sensitive_variable and other new fairness related parameters that we have added and explain what those parameters are and what values they can take. moonlanderr commented on 2024-07-17T08:16:52Z added brief explanation on sensitive variable |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:24Z We will have to explain how we mitigated the bias. What strategy we are using to mitigate the bias. moonlanderr commented on 2024-07-17T08:35:20Z so here I thought to mention the grid searching by automl, do you mean something else by strategy? I am not sure what is internally happening. Could you add this section here, referring to the startegy it is using, KarthikDutt commented on 2024-08-21T04:31:53Z Internally we are using an approach called Reweighing. Reweighing is a preprocessing technique that Weights the examples in each (group, label) combination differently to ensure fairness before classification moonlanderr commented on 2024-09-06T06:51:39Z added |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:25Z When we say 'Hence we can consider the model is now mitigated' , we will have to tell why we consider it mitigated moonlanderr commented on 2024-07-17T08:38:34Z I explained that here "The model report shows that |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:25Z Formating is bad. If needed we might use image here so tht it is more readable. moonlanderr commented on 2024-07-17T08:40:40Z It is showing the table properly in firefox, is it due to browser? I think it will be in the table format when it gets published, otherwise will add as image
KarthikDutt commented on 2024-08-21T04:33:15Z I am using chrome. I feel it is better to use images. In case you are sure that it will be fine after publishing, then you can ignore this. moonlanderr commented on 2024-09-06T07:01:23Z yes, it will be formatted further by the publishing team, will give it the proper display |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:26Z Bad formating moonlanderr commented on 2024-07-17T08:41:22Z same as above, showing ok in the notebook my machine, |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:27Z We will need to explain what selection rate means (Definition) moonlanderr commented on 2024-07-17T08:48:05Z added |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:28Z This indicates that females were more likely to be incorrectly classified as negative cases.
The above statement does not convey the problem that females might face due to this. Clearly stating that the model is likely to classify a female as earning less than 50k might give more description of the problem. moonlanderr commented on 2024-07-17T08:51:14Z added |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:28Z males are being incorrectly classified as negative cases after mitigation.
Can we use a better term instead of negative cases. Users might wonder what negative cases would mean in this context. moonlanderr commented on 2024-07-17T08:53:18Z added |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:29Z We might have to add a couple more sentence to describe why we are reducing threshold from 0.8 to 0.7. We can probably start by acknowledging that with EOR and threshold of 0.8 , the model was not able to find a fair model. But it AuotoML was able to mitigate to an extent that EOR improved to 0.7 which is still a substantial improvement over 0.17.
Then we can explain that , we can formalize this by reducing the threshold to 0.7 in the API as well. moonlanderr commented on 2024-07-17T08:56:25Z added |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:30Z Formating is not readable. moonlanderr commented on 2024-07-17T08:57:11Z same, as I think it would get fixed once published |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:30Z Formatting needs to be fixed. moonlanderr commented on 2024-07-17T08:57:55Z will get fixed in publishing as with earlier notebooks |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:31Z Abbreviations FNR , FPR and SR needs to be expanded.
We might have to reword the drawbacks so that it does not appear as if , all that we did to mitigate the bias has resulted in nothing at the end as new bias got introduced. moonlanderr commented on 2024-07-17T09:02:28Z removed part of drawback, the rest is ok, slightly hinting that new biases have crept in which can be worked on further. |
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View / edit / reply to this conversation on ReviewNB KarthikDutt commented on 2024-06-05T09:58:32Z Conclusion must talk a little bit about how users can mitigate biases in their own datasets. What fairness parameters to choose and when. moonlanderr commented on 2024-07-17T09:05:32Z I think that this will depend on the user data, and will be a bit open ended to comment, besides the automl fairness guide is there to help for that. Since this is now getting a bit heavy with technical detail and concepts. |
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corrected. View entire conversation on ReviewNB |
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View / edit / reply to this conversation on ReviewNB BP-Ent commented on 2024-09-05T21:53:08Z In this study, we explored the application of fairness metrics in machine learning, particularly focusing on the limitations and benefits of Demographic Parity Ratio (DPR) and Equalized Odds Ratio (EOR) for fairness assessment. First, we performed an initial fairness assessment of the model predicting salary by utilizing the demographic variable dataset and a vanilla automl workflow. The initial model showed discrepancies in fairness metrics, particularly with higher false positive rates for certain groups revealed by the Demographic Parity Ratio (DPR) and the Equalized Odds Ratio (EOR). Subsequently, fairness mitigation was done first with DPR and then with EOR. While DPR addressed some aspects of fairness, it fell short in balancing false positive and false negative rates across groups, leading to suboptimal performance in fairness. Then mitigation using the Equalized Odds Ratio metric provided a more comprehensive fairness assessment by ensuring equal false positive and true positive rates across all groups, thereby addressing the limitations observed with DPR. Finally, adjusting the threshold allowed automl to construct a fair model, which is useful for getting an Ensemble model. Otherwise, if the model is not able to construct a fair model, a model ensemble is not created. Although there might be bias still present in the model, the mitigation workflow was able to reduce it significantly. Thus continuous evaluation and refinement of the fairness workflow would be crucial for achieving more equitable machine learning models and unbiased decision-making processes. moonlanderr commented on 2024-09-06T08:51:52Z added |
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added a basic analysis of male vs female higher salary stats that indicate bias View entire conversation on ReviewNB |
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yes, it will be formatted further by the publishing team, will give it the proper display View entire conversation on ReviewNB |
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@BP-Ent , all suggestions added pls check |
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@KarthikDutt , I have corrected the bias indication paragraph, pls check, |
…rr/arcgis-python-api into salary_automl_fairness
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Checklist
Please go through each entry in the below checklist and mark an 'X' if that condition has been met. Every entry should be marked with an 'X' to be get the Pull Request approved.
imports are in the first cell?arcgisimports? Note that in some cases, for samples, it is a good idea to keep the imports next to where they are used, particularly for uncommonly used features that we want to highlight.GISobject instantiations are one of the following?gis = GIS()gis = GIS('home')orgis = GIS('pro')gis = GIS(profile="your_online_portal")gis = GIS(profile="your_enterprise_portal")./misc/setup.pyand/or./misc/teardown.py?api_data_owneruser?api_data_owneraccount and change the notebook to first download and unpack the files.<img src="base64str_here">instead of<img src="https://some.url">? All map widgets contain a static image preview? (Callmapview_inst.take_screenshot()to do so)os.path.join()? (Instead ofr"\foo\bar",os.path.join(os.path.sep, "foo", "bar"), etc.)Export Training Data Using Deep Learningtool published on geosaurus org (api data owner account) and added in the notebook usinggis.content.getfunction?gis.content.getfunction? Note: This includes providing test raster and trained model.