Navigating AI conformity: A design framework to assess fairness, explainability, and performance
Author
Abstract
Suggested Citation
DOI: 10.1007/s12525-025-00770-2
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Anja Lambrecht & Catherine Tucker, 2019. "Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads," Management Science, INFORMS, vol. 65(7), pages 2966-2981, July.
- Carolina Costabile & Jon Iden & Bendik Bygstad, 2022. "Building digital platform ecosystems through standardization: an institutional work approach," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 1877-1889, December.
- Ole Hanseth & Bendik Bygstad, 2015. "Flexible generification: ICT standardization strategies and service innovation in health care," European Journal of Information Systems, Taylor & Francis Journals, vol. 24(6), pages 645-663, November.
- Abraham, Rene & Schneider, Johannes & vom Brocke, Jan, 2019. "Data governance: A conceptual framework, structured review, and research agenda," International Journal of Information Management, Elsevier, vol. 49(C), pages 424-438.
- K. Valerie Carl & Cristina Mihale-Wilson & Jan Zibuschka & Oliver Hinz, 2024. "A consumer perspective on Corporate Digital Responsibility: an empirical evaluation of consumer preferences," Journal of Business Economics, Springer, vol. 94(7), pages 979-1024, October.
- Magnani, Giovanna & Gioia, Denny, 2023. "Using the Gioia Methodology in international business and entrepreneurship research," International Business Review, Elsevier, vol. 32(2).
- repec:osf:socarx:pm3wy_v1 is not listed on IDEAS
- Christian Meske & Babak Abedin & Mathias Klier & Fethi Rabhi, 2022. "Explainable and responsible artificial intelligence," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2103-2106, December.
- Kira J.M. Matus & Michael Veale, 2022. "Certification systems for machine learning: Lessons from sustainability," Regulation & Governance, John Wiley & Sons, vol. 16(1), pages 177-196, January.
- Bill Kuechler & Vijay Vaishnavi, 2008. "On theory development in design science research: anatomy of a research project," European Journal of Information Systems, Taylor & Francis Journals, vol. 17(5), pages 489-504, October.
- Lei Wang & Ram Gopal & Ramesh Shankar & Joseph Pancras, 2022. "Forecasting venue popularity on location‐based services using interpretable machine learning," Production and Operations Management, Production and Operations Management Society, vol. 31(7), pages 2773-2788, July.
- Kraus, Mathias & Feuerriegel, Stefan & Oztekin, Asil, 2020. "Deep learning in business analytics and operations research: Models, applications and managerial implications," European Journal of Operational Research, Elsevier, vol. 281(3), pages 628-641.
- Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
- Fahmida E. Moula & Chi Guotai & Mohammad Zoynul Abedin, 2017. "Credit default prediction modeling: an application of support vector machine," Risk Management, Palgrave Macmillan, vol. 19(2), pages 158-187, May.
- Moritz Zahn & Stefan Feuerriegel & Niklas Kuehl, 2022. "The Cost of Fairness in AI: Evidence from E-Commerce," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(3), pages 335-348, June.
- Ron Iphofen & Mihalis Kritikos, 2021. "Regulating artificial intelligence and robotics: ethics by design in a digital society," Contemporary Social Science, Taylor & Francis Journals, vol. 16(2), pages 170-184, March.
- Robin Hirt & Niklas Kühl & Gerhard Satzger, 2019. "Cognitive computing for customer profiling: meta classification for gender prediction," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(1), pages 93-106, March.
- Smith, James F, 1977. "The Equal Credit Opportunity Act of 1974: A Cost/Benefit Analysis," Journal of Finance, American Finance Association, vol. 32(2), pages 609-622, May.
- Nic Fleming, 2018. "How artificial intelligence is changing drug discovery," Nature, Nature, vol. 557(7707), pages 55-57, May.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Keding, Christoph & Meissner, Philip, 2021. "Managerial overreliance on AI-augmented decision-making processes: How the use of AI-based advisory systems shapes choice behavior in R&D investment decisions," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
- Maisa Cardoso Aniceto & Flavio Barboza & Herbert Kimura, 2020. "Machine learning predictivity applied to consumer creditworthiness," Future Business Journal, Springer, vol. 6(1), pages 1-14, December.
- Vítor Ribeiro & João Barata & Paulo Rupino Cunha, 2024. "Modeling inter-organizational business process governance in the age of collaborative networks," Electronic Markets, Springer;IIM University of St. Gallen, vol. 34(1), pages 1-27, December.
- Moritz Zahn & Stefan Feuerriegel & Niklas Kuehl, 2022. "The Cost of Fairness in AI: Evidence from E-Commerce," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(3), pages 335-348, June.
- Philippe Jardin, 2025. "Designing Ensemble-Based Models Using Neural Networks and Temporal Financial Profiles to Forecast Firms’ Financial Failure," Computational Economics, Springer;Society for Computational Economics, vol. 65(1), pages 149-209, January.
- Wosnitza, Jan Henrik, 2022. "Calibration alternatives to logistic regression and their potential for transferring the dispersion of discriminatory power into uncertainties of probabilities of default," Discussion Papers 04/2022, Deutsche Bundesbank.
- Lucian Belascu & Alexandra Horobet & Georgiana Vrinceanu & Consuela Popescu, 2021. "Performance Dissimilarities in European Union Manufacturing: The Effect of Ownership and Technological Intensity," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
- Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
- Martinovici, A., 2019. "Revealing attention - how eye movements predict brand choice and moment of choice," Other publications TiSEM 7dca38a5-9f78-4aee-bd81-c, Tilburg University, School of Economics and Management.
- Stefan Feuerriegel & Mateusz Dolata & Gerhard Schwabe, 2020. "Fair AI," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 62(4), pages 379-384, August.
- Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2024. "The mind in the machine: Estimating mind perception's effect on user satisfaction with voice-based conversational agents," Journal of Business Research, Elsevier, vol. 175(C).
- Abedin, Mohammad Zoynul & Hajek, Petr & Sharif, Taimur & Satu, Md. Shahriare & Khan, Md. Imran, 2023. "Modelling bank customer behaviour using feature engineering and classification techniques," Research in International Business and Finance, Elsevier, vol. 65(C).
- Gahm, Christian & Uzunoglu, Aykut & Wahl, Stefan & Ganschinietz, Chantal & Tuma, Axel, 2022. "Applying machine learning for the anticipation of complex nesting solutions in hierarchical production planning," European Journal of Operational Research, Elsevier, vol. 296(3), pages 819-836.
- Siliang Tong & Nan Jia & Xueming Luo & Zheng Fang, 2021. "The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance," Strategic Management Journal, Wiley Blackwell, vol. 42(9), pages 1600-1631, September.
- Tobias Götze & Marc Gürtler & Eileen Witowski, 2020. "Improving CAT bond pricing models via machine learning," Journal of Asset Management, Palgrave Macmillan, vol. 21(5), pages 428-446, September.
- Eileen Doctor & Torsten Eymann & Daniel Fürstenau & Martin Gersch & Kristina Hall & Anna Lina Kauffmann & Matthias Schulte-Althoff & Hannes Schlieter & Jeannette Stark & Katrin Wyrtki, 2023. "A Maturity Model for Assessing the Digitalization of Public Health Agencies," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 65(5), pages 539-554, October.
- Sun, Weixin & Zhang, Xuantao & Li, Minghao & Wang, Yong, 2023. "Interpretable high-stakes decision support system for credit default forecasting," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
- Patrick Büchel & Michael Kratochwil & Maximilian Nagl & Daniel Rösch, 2022. "Deep calibration of financial models: turning theory into practice," Review of Derivatives Research, Springer, vol. 25(2), pages 109-136, July.
- Suyuan Luo & Tsan-Ming Choi, 2024. "Great partners: how deep learning and blockchain help improve business operations together," Annals of Operations Research, Springer, vol. 339(1), pages 53-78, August.
- Kaffash, Sepideh & Nguyen, An Truong & Zhu, Joe, 2021. "Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 231(C).
More about this item
Keywords
Machine learning; Algorithmic fairness; Explainable AI; Certification; AI auditing; Impact assessment;All these keywords.
JEL classification:
- M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
- L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
- O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:elmark:v:35:y:2025:i:1:d:10.1007_s12525-025-00770-2. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.