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LuPe: A System for Personalized and Transparent Data-driven Decisions

Authors:

Sarah Oppold,

Melanie HerschelAuthors Info & Claims

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Pages 2905 - 2908

https://doi.org/10.1145/3357384.3357857

Published: 03 November 2019 Publication History

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Abstract

Machine learning models are commonly used for decision support even though they 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 will likely continue to rely on imperfect models in many applications. In these settings, how could we at least use the "best" model for an individual or a group of users and transparently communicate the risks and weaknesses that apply?

We demonstrate LuPe, a system that addresses these questions. LuPe allows to optimize the choice of the applied model for subgroups of the population or individuals, thereby personalizing the model choice to best fit users' profiles, which improves fairness. LuPe further captures data to explain the choices made and the results of the model. We showcase how such data enable users to understand the system performance they can expect. This transparency helps users in making informed decisions or providing informed consent when such systems are used. Our demonstration will focus on several real-world applications showcasing the behavior of LuPe, including credit scoring and income prediction.

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Cited By

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Weber PCarl KHinz O(2023)Applications of Explainable Artificial Intelligence in Finance—a systematic review of Finance, Information Systems, and Computer Science literatureManagement Review Quarterly10.1007/s11301-023-00320-074:2(867-907)Online publication date: 28-Feb-2023
https://doi.org/10.1007/s11301-023-00320-0
Majumdar PChhabra SSingh RVatsa M(2021)Subgroup Invariant Perturbation for Unbiased Pre-Trained Model PredictionFrontiers in Big Data10.3389/fdata.2020.5902963Online publication date: 18-Feb-2021
https://doi.org/10.3389/fdata.2020.590296
Oppold SHerschel M(2020)A System Framework for Personalized and Transparent Data-Driven DecisionsAdvanced Information Systems Engineering10.1007/978-3-030-49435-3_10(153-168)Online publication date: 3-Jun-2020
https://doi.org/10.1007/978-3-030-49435-3_10

Index Terms

LuPe: A System for Personalized and Transparent Data-driven Decisions
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Data model extensions
        Data provenance
    2. Information integration
  2. Information systems applications
    1. Decision support systems
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Software architectures
        Data flow architectures

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Published In

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

November 2019

3373 pages

ISBN:9781450369763

DOI:10.1145/3357384

General Chairs:
Wenwu Zhu
Tsinghua University, China
,
Dacheng Tao
University of Massachusetts, USA
,
Xueqi Cheng
Institute of Computing Technology, CAS, China
,
Program Chairs:
Peng Cui
Tsinghua University, China
,
Elke Rundensteiner
Worcester Polytechnic Institute, USA
,
David Carmel
Amazon Research, USA
,
Qi He
LinkedIn, USA
,
Jeffrey Xu Yu
Chinese University of Hong Kong, China

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

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Published: 03 November 2019

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CIKM '19: The 28th ACM International Conference on Information and Knowledge Management

November 3 - 7, 2019

Beijing, China

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CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Weber PCarl KHinz O(2023)Applications of Explainable Artificial Intelligence in Finance—a systematic review of Finance, Information Systems, and Computer Science literatureManagement Review Quarterly10.1007/s11301-023-00320-074:2(867-907)Online publication date: 28-Feb-2023
https://doi.org/10.1007/s11301-023-00320-0
Majumdar PChhabra SSingh RVatsa M(2021)Subgroup Invariant Perturbation for Unbiased Pre-Trained Model PredictionFrontiers in Big Data10.3389/fdata.2020.5902963Online publication date: 18-Feb-2021
https://doi.org/10.3389/fdata.2020.590296
Oppold SHerschel M(2020)A System Framework for Personalized and Transparent Data-Driven DecisionsAdvanced Information Systems Engineering10.1007/978-3-030-49435-3_10(153-168)Online publication date: 3-Jun-2020
https://doi.org/10.1007/978-3-030-49435-3_10

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Towards Real-Time Fault-Tolerant CORBA Middleware

Empirical evaluation of a process to increase consensus in group architectural decision making

Decision making process: typology, intelligence, and optimization

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