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simulated sensitive attribute inference model results  (COMPAS Inc)

 
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    Structured Review

    COMPAS Inc simulated sensitive attribute inference model results
    Overall design measuring the impact of uncertain <t>sensitive</t> <t>attribute</t> <t>inference</t> on bias mitigation algorithms.
    Simulated Sensitive Attribute Inference Model Results, supplied by COMPAS Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/simulated sensitive attribute inference model results/product/COMPAS Inc
    Average 90 stars, based on 1 article reviews
    simulated sensitive attribute inference model results - by Bioz Stars, 2026-04
    90/100 stars

    Images

    1) Product Images from "Impact on bias mitigation algorithms to variations in inferred sensitive attribute uncertainty"

    Article Title: Impact on bias mitigation algorithms to variations in inferred sensitive attribute uncertainty

    Journal: Frontiers in Artificial Intelligence

    doi: 10.3389/frai.2025.1520330

    Overall design measuring the impact of uncertain sensitive attribute inference on bias mitigation algorithms.
    Figure Legend Snippet: Overall design measuring the impact of uncertain sensitive attribute inference on bias mitigation algorithms.

    Techniques Used:

    Gender inference accuracy using existing demographic inference models.
    Figure Legend Snippet: Gender inference accuracy using existing demographic inference models.

    Techniques Used:

    Balanced accuracy and fairness score of the outcome prediction model (using inferred sensitive attribute) on Wikidata.
    Figure Legend Snippet: Balanced accuracy and fairness score of the outcome prediction model (using inferred sensitive attribute) on Wikidata.

    Techniques Used:

    Prediction model fairness difference using the ground truth sensitive attribute S and the inferred sensitive attribute S ′ with 0.75 balanced accuracy.
    Figure Legend Snippet: Prediction model fairness difference using the ground truth sensitive attribute S and the inferred sensitive attribute S ′ with 0.75 balanced accuracy.

    Techniques Used:

    Prediction model fairness difference between baseline model and bias mitigation methods using inferred sensitive attribute with 0.75 balanced accuracy.
    Figure Legend Snippet: Prediction model fairness difference between baseline model and bias mitigation methods using inferred sensitive attribute with 0.75 balanced accuracy.

    Techniques Used:



    Similar Products

    simulated sensitive attribute inference model results  (COMPAS Inc)
    90
    COMPAS Inc simulated sensitive attribute inference model results
    Overall design measuring the impact of uncertain <t>sensitive</t> <t>attribute</t> <t>inference</t> on bias mitigation algorithms.
    Simulated Sensitive Attribute Inference Model Results, supplied by COMPAS Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/simulated sensitive attribute inference model results/product/COMPAS Inc
    Average 90 stars, based on 1 article reviews
    simulated sensitive attribute inference model results - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Overall design measuring the impact of uncertain sensitive attribute inference on bias mitigation algorithms.

    Journal: Frontiers in Artificial Intelligence

    Article Title: Impact on bias mitigation algorithms to variations in inferred sensitive attribute uncertainty

    doi: 10.3389/frai.2025.1520330

    Figure Lengend Snippet: Overall design measuring the impact of uncertain sensitive attribute inference on bias mitigation algorithms.

    Article Snippet: For the COMPAS and credit card client data sets, we use the simulated sensitive attribute inference model results with random misclassification at 0.75 balanced accuracy.

    Techniques:

    Gender inference accuracy using existing demographic inference models.

    Journal: Frontiers in Artificial Intelligence

    Article Title: Impact on bias mitigation algorithms to variations in inferred sensitive attribute uncertainty

    doi: 10.3389/frai.2025.1520330

    Figure Lengend Snippet: Gender inference accuracy using existing demographic inference models.

    Article Snippet: For the COMPAS and credit card client data sets, we use the simulated sensitive attribute inference model results with random misclassification at 0.75 balanced accuracy.

    Techniques:

    Balanced accuracy and fairness score of the outcome prediction model (using inferred sensitive attribute) on Wikidata.

    Journal: Frontiers in Artificial Intelligence

    Article Title: Impact on bias mitigation algorithms to variations in inferred sensitive attribute uncertainty

    doi: 10.3389/frai.2025.1520330

    Figure Lengend Snippet: Balanced accuracy and fairness score of the outcome prediction model (using inferred sensitive attribute) on Wikidata.

    Article Snippet: For the COMPAS and credit card client data sets, we use the simulated sensitive attribute inference model results with random misclassification at 0.75 balanced accuracy.

    Techniques:

    Prediction model fairness difference using the ground truth sensitive attribute S and the inferred sensitive attribute S ′ with 0.75 balanced accuracy.

    Journal: Frontiers in Artificial Intelligence

    Article Title: Impact on bias mitigation algorithms to variations in inferred sensitive attribute uncertainty

    doi: 10.3389/frai.2025.1520330

    Figure Lengend Snippet: Prediction model fairness difference using the ground truth sensitive attribute S and the inferred sensitive attribute S ′ with 0.75 balanced accuracy.

    Article Snippet: For the COMPAS and credit card client data sets, we use the simulated sensitive attribute inference model results with random misclassification at 0.75 balanced accuracy.

    Techniques:

    Prediction model fairness difference between baseline model and bias mitigation methods using inferred sensitive attribute with 0.75 balanced accuracy.

    Journal: Frontiers in Artificial Intelligence

    Article Title: Impact on bias mitigation algorithms to variations in inferred sensitive attribute uncertainty

    doi: 10.3389/frai.2025.1520330

    Figure Lengend Snippet: Prediction model fairness difference between baseline model and bias mitigation methods using inferred sensitive attribute with 0.75 balanced accuracy.

    Article Snippet: For the COMPAS and credit card client data sets, we use the simulated sensitive attribute inference model results with random misclassification at 0.75 balanced accuracy.

    Techniques: