TY - JOUR

T1 - Decision support for efficient XAI services

T2 - A morphological analysis, business model archetypes, and a decision tree

AU - Gerlach, Jana

AU - Hoppe, Paul

AU - Jagels, Sarah

AU - Licker, Luisa

AU - Breitner, Michael H.

N1 - Funding Information: Open Access funding enabled and organized by Projekt DEAL. The research project 'SiNED – Systemdienstleistungen für sichere Stromnetze in Zeiten fortschreitender Energiewende und digitaler Transformation' acknowledges the support of the Lower Saxony Ministry of Science and Culture through the 'Niedersächsisches Vorab' grant programme (grant ZN3563) and of the Energy Research Centre of Lower Saxony.

PY - 2022/12

Y1 - 2022/12

N2 - The black-box nature of Artificial Intelligence (AI) models and their associated explainability limitations create a major adoption barrier. Explainable Artificial Intelligence (XAI) aims to make AI models more transparent to address this challenge. Researchers and practitioners apply XAI services to explore relationships in data, improve AI methods, justify AI decisions, and control AI technologies with the goals to improve knowledge about AI and address user needs. The market volume of XAI services has grown significantly. As a result, trustworthiness, reliability, transferability, fairness, and accessibility are required capabilities of XAI for a range of relevant stakeholders, including managers, regulators, users of XAI models, developers, and consumers. We contribute to theory and practice by deducing XAI archetypes and developing a user-centric decision support framework to identify the XAI services most suitable for the requirements of relevant stakeholders. Our decision tree is founded on a literature-based morphological box and a classification of real-world XAI services. Finally, we discussed archetypical business models of XAI services and exemplary use cases.

AB - The black-box nature of Artificial Intelligence (AI) models and their associated explainability limitations create a major adoption barrier. Explainable Artificial Intelligence (XAI) aims to make AI models more transparent to address this challenge. Researchers and practitioners apply XAI services to explore relationships in data, improve AI methods, justify AI decisions, and control AI technologies with the goals to improve knowledge about AI and address user needs. The market volume of XAI services has grown significantly. As a result, trustworthiness, reliability, transferability, fairness, and accessibility are required capabilities of XAI for a range of relevant stakeholders, including managers, regulators, users of XAI models, developers, and consumers. We contribute to theory and practice by deducing XAI archetypes and developing a user-centric decision support framework to identify the XAI services most suitable for the requirements of relevant stakeholders. Our decision tree is founded on a literature-based morphological box and a classification of real-world XAI services. Finally, we discussed archetypical business models of XAI services and exemplary use cases.

KW - Archetypes

KW - Artificial intelligence

KW - Business models

KW - Decision tree

KW - Explainability

KW - Morphological analysis

UR - http://www.scopus.com/inward/record.url?scp=85142454660&partnerID=8YFLogxK

U2 - 10.1007/s12525-022-00603-6

DO - 10.1007/s12525-022-00603-6

M3 - Article

AN - SCOPUS:85142454660

VL - 32

SP - 2139

EP - 2158

JO - Electronic markets

JF - Electronic markets

SN - 1019-6781

IS - 4

ER -