Fair, Accountable, and Transparent Decision Support

Machine learning models are commonly used for decision support. Ideally, the decisions should be impartial, unbiased, and fair. However, machine learning models are far from perfect, e.g., due to bias introduced by imperfect training data or wrong feature selection. While efforts are made and should continue to be put into developing better models, we also acknowledge that we will continue to rely on imperfect models in many applications. But what if we can provably rely on the “best” model for an individual or a group of individuals and transparently communicate the risks and weaknesses that apply?

In light of this question, we propose a system framework that optimizes the choice of model for specific subgroups of the population or even individual persons, relying on metadata sheets for data and models. At the same time, to achieve transparency, the framework captures data to explain the choices made and results of the model at different scales to different stakeholders.

Publications

  1. Oppold, S., & Herschel, M. (2020). A System Framework for Personalized and Transparent Data-Driven Decisions. Proceedings of the  International Conference on Advanced Information Systems Engineering (CAiSE).
  2. Oppold, S., & Herschel, M. (2019). LuPe: A System for Personalized and Transparent Data-driven Decisions. In W. Zhu, D. Tao, X. Cheng, P. Cui, E. A. Rundensteiner, D. Carmel, Q. He, & J. X. Yu (Eds.), CIKM (pp. 2905–2908). ACM. http://dblp.uni-trier.de/db/conf/cikm/cikm2019.html#OppoldH19

Resources

Title File
LiQuID supplemental material - Model overview Information_Overview.pdf
LiQuID supplemental material - XML Schema LiQuID.xsd
LiQuID supplemental material - Accountability workload Workload.pdf
MDS for Adult Dataset - Training data adultData_d1_MDS.json
MDS for Adult Dataset - Test data adultData_d2_MDS.json
MDS for gradient boosted tree model m10 adult_m10_gradBoostMDS.json
MDS for SVM model m1 adult_m1_linearSVMMDS.json
MDS for logistic regression model m2 adult_m2_logRegMDS.json
MDS for decision tree model m3 adult_m3_decTreeMDS.json
MDS for random forest tree model m4 adult_m4_randForestMDS.json
MDS for gradient boosted tree model m5 adult_m5_gradBoostMDS.json
MDS for SVM model m6 adult_m6_linearSVMMDS.json
MDS for logistic regression model m7 adult_m7_logRegMDS.json
MDS for decision tree model m8 adult_m8_decTreeMDS.json
MDS for random forest tree model m9 adult_m9_randForestMDS.json
MDS model ensemble m1 adult_me1MDS.json
MDS model ensemble m2 adult_me2MDS.json
MDS model ensemble m3 adult_me3MDS.json
MDS for German Credit Dataset germanCreditDataMDS.json
MDS for German Credit Dataset in Spark readable format germanCreditDataReadableMDS.json
MDS for logistic regression model m2 germanCredit_m2_logRegMDS.json
MDS for decision tree model m3 germanCredit_m3_decTreeMDS.json
MDS for random forest tree model m4 germanCredit_m4_randForestMDS.json
MDS for gradient boosted tree model m5 germanCredit_m5_gradBoostMDS.json
MDS for SVM model m6 germanCredit_m6_linearSVMMDS.json
MDS for logistic regression model m7 germanCredit_m7_logRegMDS.json
MDS for German Credit Dataset germanCredit_me1MDS.json
MDS model ensemble m2 germanCredit_me2MDS.json
MDS model ensemble m3 germanCredit_me3MDS.json
MDS model ensemble m4 germanCredit_me4MDS.json
MDS model ensemble m5 germanCredit_me5MDS.json
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