A Framework for Trustworthy AI in Credit Risk Management: Perspectives and Practices

被引：0
|
作者：
Mazumder, Sourav [1 ]
Dhar, Subhankar [2 ]
Asthana, Avinash [3 ]
机构：
[1] IBM Corp, Open Grp, Armonk, NY 10504 USA
[2] San Jose State Univ, Informat Syst & Technol, San Jose, CA 95192 USA
[3] IBM Corp, Financial Serv, New York, NY 10003 USA
来源：
COMPUTER | 2023年 / 56卷 / 05期
关键词：
Analytical models; Computational modeling; Computer architecture; Risk management; Reliability; Artificial intelligence;
D O I：
10.1109/MC.2023.3236564
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
We present a reference architecture for trustworthy artificial intelligence based on three key tenets (usable, reliable, and transparent) and analyze its applicability in managing the challenges involved in the credit risk model from a practitioner's perspective. We propose a holistic approach covering relevant concerns in practical implementations.
引用
收藏
页码：28 / 40
页数：13
相关论文
共 50 条
  • [1] Financial Risk Management and Explainable, Trustworthy, Responsible AI
    Fritz-Morgenthal, Sebastian
    Hein, Bernhard
    Papenbrock, Jochen
    [J]. FRONTIERS IN ARTIFICIAL INTELLIGENCE, 2022, 5
  • [2] An AI Harms and Governance Framework for Trustworthy AI
    Peckham, Jeremy B.
    [J]. COMPUTER, 2024, 57 (03) : 59 - 68
  • [3] Trustworthy AI: From Principles to Practices
    Li, Bo
    Qi, Peng
    Liu, Bo
    Di, Shuai
    Liu, Jingen
    Pei, Jiquan
    Yi, Jinfeng
    Zhou, Bowen
    [J]. ACM COMPUTING SURVEYS, 2023, 55 (09)
  • [4] Credit risk management: a survey of practices
    Fatemi, Ali
    Fooladi, Iraj
    [J]. MANAGERIAL FINANCE, 2006, 32 (03) : 227 - 233
  • [5] A Trustworthy Healthcare Management Framework Using Amalgamation of AI and Blockchain Network
    Jadav, Dhairya
    Jadav, Nilesh Kumar
    Gupta, Rajesh
    Tanwar, Sudeep
    Alfarraj, Osama
    Tolba, Amr
    Raboaca, Maria Simona
    Marina, Verdes
    [J]. MATHEMATICS, 2023, 11 (03)
  • [6] Assessing trustworthy AI: Technical and legal perspectives of fairness in AI
    Kattnig, Markus
    Angerschmid, Alessa
    Reichel, Thomas
    Kern, Roman
    [J]. COMPUTER LAW & SECURITY REVIEW, 2024, 55
  • [7] Orthos: A Trustworthy AI Framework for Data Acquisition
    Moti, Moin Hussain
    Chatzopoulos, Dimitris
    Hui, Pan
    Faltings, Boi
    Gujar, Sujit
    [J]. ENGINEERING MULTI-AGENT SYSTEMS (EMAS 2020), 2020, 12589 : 100 - 118
  • [8] Towards an AI-centric Requirements Engineering Framework for Trustworthy AI
    Ronanki, Krishna
    [J]. 2023 IEEE/ACM 45TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: COMPANION PROCEEDINGS, ICSE-COMPANION, 2023, : 278 - 280
  • [9] POLARIS: A framework to guide the development of Trustworthy AI systems
    Baldassarre, Maria Teresa
    Gigante, Domenico
    Kalinowski, Marcos
    Ragone, Azzurra
    [J]. PROCEEDINGS 2024 IEEE/ACM 3RD INTERNATIONAL CONFERENCE ON AI ENGINEERING-SOFTWARE ENGINEERING FOR AI, CAIN 2024, 2024, : 200 - 210
  • [10] Trustworthy Artificial Intelligence: Design of AI Governance Framework
    Sharma, Sanur
    [J]. STRATEGIC ANALYSIS, 2023, 47 (05) : 443 - 464
12345