Pouria Akbarighatar

• Abedin, Ehsan; Mendoza, Antonette; Akbarighatar, Pouria & Karunasekera, Shanika (2024). Predicting Credibility of Online Reviews: An Integrated Approach. IEEE Access. ISSN 2169-3536. 12, s. 49050–49061. doi: 10.1109/ACCESS.2024.3383846.
• Akbarighatar, Pouria; Pappas, Ilias & Vasilakopoulou, Polyxeni (2023). JUSTICE AS FAIRNESS: A HIERARCHICAL FRAMEWORK OF RESPONSIBLE AI PRINCIPLES, Proceedings of the European Conference on Information Systems ECIS 2023. Association for Information Systems (AIS). ISSN 978-1-958200-08-7.
• Akbarighatar, Pouria; Pappas, Ilias & Vassilakopoulou, Polyxeni (2023). Practices for Responsible AI: Findings from Interviews with Experts. I Midha, Vishal & Animesh, Animesh (Red.), The 29th Americas Conference on Information Systems (AMCIS). Association for Information Systems (AIS). ISSN 978-1-958200-05-6.
• Akbarighatar, Pouria; Pappas, Ilias & Vasilakopoulou, Polyxeni (2023). A sociotechnical perspective for responsible AI maturity models: Findings from a mixed-method literature review. International Journal of Information Management Data Insights. 3(2). doi: 10.1016/j.jjimei.2023.100193. Fulltekst i vitenarkiv Vis sammendrag

  As artificial intelligence (AI) is increasingly used in various industries, it becomes crucial for organizations to enhance their capabilities and maturity in adopting AI responsibly. This paper employs a mixed-method approach that combines topic modeling with manual content analysis to provide a comprehensive review of the literature on AI maturity and readiness. The review encompasses an extensive corpus of 1451 papers, identifying the main themes and topics within this body of literature. Based on these findings, a subset of papers was selected and further analyzed to identify AI capabilities utilizing a sociotechnical lens. This further analysis led to the identification of foundational and responsible AI (RAI) capabilities. These capabilities have been integrated in a sociotechnical framework of capabilities for AI maturity models providing valuable insights for organizations and AI service providers and a basis for further research.

• Akbarighatar, Pouria (2022). MATURITY AND READINESS MODELS FOR RESPONSIBLE ARTIFICIAL INTELLIGENCE (RAI): A SYSTEMATIC LITERATURE REVIEW, The 14th Mediterranean Conference on Information Systems (MCIS 2022). Acting in the digital society: moving towards a sustainable future. Association for Information Systems (AIS). ISSN 978-88-87173-05-5. s. 1–17. Vis sammendrag

  The development and deployment of Artificial intelligence (AI) in organizations is of growing interest to the Information systems (IS) discipline. This can be approached from a sociotechnical perspective contributing to managing the unintended outcomes of AI while extending AI use boundaries. This paper presents the findings from a systematic literature review on organizational maturity and readiness for AI development and development. A key result is that extant research has lost sight of AI systems' humanistic and ethical aspects, and principles related to responsible AI are not sufficiently defined. This is a hurdle because principles for responsible AI are fundamental for AI development and deployment ensuring long-term benefits. Drawing from the literature review findings, we provide a conceptual maturity model with two main dimensions (responsible and instrumental), twelve conditions and thirty factors. The maturity and readiness factors for responsible AI are deduced from synthesizing 35 articles in related literature. Specifically, the paper identified six capabilities for responsible AI: AI model, Cooperative AI, ethical awareness, laws & regulations, data governance and continuous improvement; six instrumental capabilities were also identified: strategic alignment, technology, culture, data management, financial and human resource management.

• Jafarzadeh, Hamed; Heidary-Dahooie, Jalil & Akbarighatar, Pouria (2022). A project prioritization approach considering uncertainty, reliability, criteria prioritization, and robustness. Decision Support Systems. ISSN 0167-9236. 156. doi: 10.1016/j.dss.2022.113731. Vis sammendrag

  Given the limitation of resources in organizations (time, money, staff, material, etc.), deciding which projects should be given priority – among many competing projects – is a constant and continuous challenge for decision makers. As a result, a proliferation of methods and solutions for project prioritization has been developed, each with its own strengths and limitations. In this paper, we propose a hybrid approach for prioritizing projects taking into consideration four pervasive challenges in decision making: 1) uncertainty (imprecision and vagueness) of human decisions, 2) reliability (extent of sureness) of decision makers, 3) systematic identification of selection criteria, and 4) robustness of decision making (reasonable tolerance against insignificant changes to decision makers’ evaluations during the process). In doing so, we propose integrating Z-numbers theory with Quality Function Development (ZQFD), four Multi-Criteria Decision-Making (Z-MCDM) methods, ensemble ranking aggregation, and sensitivity analysis. The proposed method is then applied in a real-world organization with twenty IT projects to illustrate how our approach might be used in practice. Furthermore, we elaborate on the trustworthiness of the proposed method in light of 16 criteria available in the literature of structured decision making.

• Akbarighatar, Pouria (2020). Prioritising IT Projects: Combination of Fuzzy QFD and ARAS to Address Criteria Multiplicity Challenge, Proceedings of the 23rd Pacific Asia Conference on Information Systems (PACIS). Association of Information Systems. ISSN 978-1-7336325-3-9.
• Akbarighatar, Pouria (2020). Prioritising IT projects: A multi-method approach, Proceedings of the 23rd Pacific Asia Conference on Information Systems (PACIS). Association of Information Systems. ISSN 978-1-7336325-3-9.
• Akbarighatar, Pouria & Jafarzadeh, Hamed (2018). A methodology for project portfolio selection under criteria prioritisation, uncertainty and projects interdependency–combination of fuzzy QFD and DEA. Expert Systems With Applications. ISSN 0957-4174. doi: 10.1016/j.eswa.2018.05.028.

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• Akbarighatar, Pouria; Pappas, Ilias & Vassilakopoulou, Polyxeni (2023). Justice as Fairness: a hierarchical framework of Responsible AI principles.

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