Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry

Gangadhari, Rajan Kumar; Khanzode, Vivek; Murthy, Shankar; Dennehy, Denis

doi:10.1108/bij-03-2022-0161

Cited by 3 publications

(2 citation statements)

References 109 publications

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“…SWARA, COPRAS, FANP, FAHP, FTOPSIS, SAW, and EDAS [31] The study investigates barriers to ML implementation for accident analysis in the Indian oil industry, aiming to identify and analyze these barriers to improve safety measures.…”

Section: Author Application Of Problemmentioning

confidence: 99%

A Comprehensive Guide to the COPRAS method for Multi-Criteria Decision Making

Taherdoost,

Mohebi

2024

j. of manag. sci. & eng. res.

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MCDM has been utilized as a proficient decision-making technique for numerous decades. Complex Proportional Assessment (COPRAS) method, a prominent technique in multi-criteria decision-making (MCDM) which offers a systematic and effective framework for evaluating alternatives and making informed choices. The versatility of COPRAS is demonstrated via case studies across various domains, such as engineering, business, and environmental management, showcasing its adaptability and robustness in providing solutions to diverse decision-making scenarios. There is a lack of a comprehensive guide and a reviewing of application, strengths, and limitation for this method in the literature. Therefore, this study aims to offer an in-depth understanding of the COPRAS approach, including its applications, advantages, and disadvantages. Additionally, it provides detailed guidance on how to utilize the COPRAS methodology for decision-making and real-life problems.

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Section: Author Application Of Problemmentioning

confidence: 99%

A Comprehensive Guide to the COPRAS method for Multi-Criteria Decision Making

Taherdoost,

Mohebi

2024

j. of manag. sci. & eng. res.

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“…AI provides many transformational benefits to organisations across all industries and sectors (Alshahrani et al, 2021;Dennehy et al, 2022;Dwivedi et al, 2021;Elbanna et al, 2020;Vassilakopoulou et al, 2022). Recent studies have reported that AI can lead to new forms of business value (Enholm et al, 2022;Mikalef and Gupta, 2021), dynamic business-to-business relationships (Dwivedi and Wang, 2022;Keegan et al, 2022), enriched customer experiences (Jain et al, 2022;Griva et al, 2021;Kautish and Khare, 2022), enhanced human capabilities (Dwivedi et al, 2021), resilient supply chains (Zamani et al, 2022) and improved safety in the workplace (Gangadhari et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence (AI) and Information Systems: Perspectives to Responsible AI

et al. 2022

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example the responsible design (Dennehy et al., 2021) and governance (Mäntymäki et al., 2022b) of AI systems. While organisations are increasingly investing in ethical AI and Responsible AI (RAI) (Zimmer et al., 2022), recent reports suggest that this comes at a cost and may lead to burnout in responsible-AI teams (Heikkilä, 2022). Thus, it is critical to consider how we educate about RAI (Grøder et al., 2022) and rethink our traditional learning designs (Pappas & Giannakos, 2021), as this can influence end-users' perceptions towards AI applications (Schmager et al., 2023) as well as how future employees approach the design and implementation of AI applications (Rakova et al., 2021;Vassilakopoulou et al., 2022).The use of algorithmic decision-making and decisionsupport processes, particularly AI is becoming increasingly pervasive in the public sector, also in high-risk application areas such as healthcare, traffic, and finance (European Commission, 2020). Against this backdrop, there is growing concern over the ethical use and safety of AI, fuelled by reports of ungoverned military applications (Butcher and Beridze, 2019; Dignum, 2020), privacy violations attributed to facial recognition technologies used by the police (Rezende, 2022), unwanted biases exhibited by AI applications used by courts (Imai et al., 2020), and racial biases in clinical algorithms (Vyas et al. 2020). The opacity and lack of explainability frequently attributed to AI systems makes evaluating the trustworthiness of algorithmic decisions challenging even for technical experts, let alone the public. Together with the algorithm-propelled proliferation of misinformation, hate speech and polarising content on social media platforms, there is a high risk for erosion of trust in algorithmic systems used by the public sector (Janssen et al., 2020). Ensuring that people can trust in the algorithmic processes is essential not only for reaping the potential benefits from AI (Dignum, 2020) but also for fostering trust and resilience at a societal level.AI researchers and practitioners have expressed their fears about AI systems being developed that are Denis Dennehy

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A conceptual framework of barriers to data science implementation: a practitioners' guideline

Reddy,

Mishra,

Goyal

et al. 2023

BIJ

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PurposeThe study explores the potential barriers to data science (DS) implementation in organizations and identifies the key barriers. The identified barriers were explored for their interconnectedness and characteristics. This study aims to help organizations formulate apt DS strategies by providing a close-to-reality DS implementation framework of barriers, in conjunction with extant literature and practitioners' viewpoints.Design/methodology/approachThe authors synthesized 100 distinct barriers through systematic literature review (SLR) under the individual, organizational and governmental taxonomies. In discussions with 48 industry experts through semi-structured interviews, 14 key barriers were identified. The selected barriers were explored for their pair-wise relationships using interpretive structural modeling (ISM) and fuzzy Matriced’ Impacts Croise's Multiplication Appliquée a UN Classement (MICMAC) analyses in formulating the hierarchical framework.FindingsThe lack of awareness and data-related challenges are identified as the most prominent barriers, followed by non-alignment with organizational strategy, lack of competency with vendors and premature governmental arrangements, and classified as independent variables. The non-commitment of top-management team (TMT), significant investment costs, lack of swiftness in change management and a low tolerance for complexity and initial failures are recognized as the linkage variables. Employee reluctance, mid-level managerial resistance, a dearth of adequate skills and knowledge and working in silos depend on the rest of the identified barriers. The perceived threat to society is classified as the autonomous variable.Originality/valueThe study augments theoretical understanding from the literature with the practical viewpoints of industry experts in enhancing the knowledge of the DS ecosystem. The research offers organizations a generic framework to combat hindrances to DS initiatives strategically.

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Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry

Cited by 3 publications

References 109 publications

A Comprehensive Guide to the COPRAS method for Multi-Criteria Decision Making

A Comprehensive Guide to the COPRAS method for Multi-Criteria Decision Making

Artificial Intelligence (AI) and Information Systems: Perspectives to Responsible AI

A conceptual framework of barriers to data science implementation: a practitioners' guideline

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