Artificial intelligence in product lifecycle management

Wang, Lei; Liu, Zhengchao; Liu, Ang; Tao, Fei

doi:10.1007/s00170-021-06882-1

Cited by 93 publications

(41 citation statements)

References 163 publications

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“…De Silva and Alahakoon [51] presents the AI life cycle as consisting of three phases (design, develop, and deploy) and nineteen constituent stages across the three phases from conception to production. Wang et al [52] examines the product lifecycle management in three stages (product design, manufacturing, and service) with four sub-stages for each stage and maps the AI technical requirements to each stage.…”

Section: Literature Reviewmentioning

confidence: 99%

A seven-layer model with checklists for standardising fairness assessment throughout the AI lifecycle

Avinash

Harsh²

2023

AI Ethics

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Problem statement: Standardisation of AI fairness rules and benchmarks is challenging because AI fairness and other ethical requirements depend on multiple factors, such as context, usage case, type of the AI system, and so on. In this paper, we elaborate that the AI system is prone to biases at every stage of its lifecycle, from inception to its use, and that all stages require due attention for mitigating AI bias. We need a standardised approach to handle AI fairness at every stage. Gap analysis: While AI fairness is a hot research topic, a holistic strategy for AI fairness is generally missing. The AI fairness assessment is often developer-centric. Standard processes are required for independent audits for the benefit of other stakeholders. Also, most researchers focus only on a few facets of AI model-building. Peer review shows a focus on biases in the datasets, fairness metrics, and algorithmic bias. In the process, other aspects affecting AI fairness get ignored. The solution proposed: We propose a comprehensive approach in the form of a novel seven-layer model, inspired by the Open System Interconnection (OSI) model, to standardise AI fairness handling. Despite the differences in the various aspects, most AI systems have similar model-building stages. The proposed model splits the AI system lifecycle into seven abstraction layers, each corresponding to a well-defined AI model-building or usage stage. We also provide checklists for each layer and deliberate on potential sources of bias in each layer and their mitigation methodologies. This work will facilitate layer-wise standardisation of AI fairness rules and benchmarking parameters.

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Section: Literature Reviewmentioning

confidence: 99%

A seven-layer model with checklists for standardising fairness assessment throughout the AI lifecycle

Avinash

Harsh²

2023

AI Ethics

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“…In the digital landscape, machine learning and data analytics are powering a wave of such groundbreaking technologies. Companies are aiming to achieve a competitive advantage in this new landscape by implementing accurate predictive analytics and workflow automation solutions, such as Artificial Intelligence (AI), business intelligence (BI) and robot process automation (RPA) (Wang et al, 2021). However, these nascent breakthrough technologies have their own risks.…”

Section: Iterative Minimum Viable Product Approach To Implementing Ai...mentioning

confidence: 99%

Untitled

2021

WIJAR

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Psychotherapists in private practice provide services to an ever-growing client population. The 2020 novel Corona Virus (COVID-19) pandemic was a catalyst for emerging and exasperated mental health concerns among the U.S. population. The result was an increase in demand for services and private practitioners stepping up to meet this growing need. Little is known about the psychotherapists who embark on independent practice and less is known about the nuances of practicing during a global pandemic. The aim of this review was to exhaust the literature on private practice psychotherapy and the practice of psychotherapy during COVID-19, synthesize the findings, report on themes in the literature, and provide recommendations for future lines of inquiry. Themes from this review included the impact of COVID-19 on public mental health, telemental health, private practice shifts, and private practice careers.

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“…This ranges from conceptual, design, construction, operational, through the maintenance stage (Baduge et al 2022 ). For instance, the manufacturing sector has been at the forefront to adopt technologies led by the fourth industrial revolution (also known as Industry 4.0), thereby using several AI techniques in process improvement, cost-efficiency, reduced production times and attaining firms’ sustainability goals (Shiau et al 2022 ; Wang et al 2021 ; Omairi and Ismail 2021 ). The selection of sustainable suppliers was made in the retail industry in Nigeria by using quantitative techniques such as Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and TOPSIS (Okwu and Tartibu 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Identifying issues in adoption of AI practices in construction supply chains: towards managing sustainability

Singh

Dwivedi

Agrawal³

et al. 2023

Oper Manag Res

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The fragmented nature of construction industry coupled with its complex and dynamic nature demands for innovative technologies to record better performance in project execution. In this respect, Artificial Intelligence (AI) based techniques posit a viable means to attain requisite efficiency in performance and alleviate the productivity of construction organizations. The adoption of sustainable practices in Construction Supply Chains (CSCs) lowers the environmental impact, lowers the risk of failure, and boosts competitiveness. The present study attempts to unearth potential issues in the adoption of AI practices in CSCs. Initially, the study identifies potential issues in the implementation of AI-based frameworks in CSCs by performing an extensive literature review and brainstorming sessions with industry experts. The exercise results in identifying 17 critical issues confronting the adoption of AI in CSCs which were subsequently subjected to fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) approach. The findings from the study reveal that “Lack of trust in AI outcomes”, “Exploitation by hackers, cybercrimes and privacy intrusion”, “Risk and cost associated with construction projects”, “Uncertain processing and functions of AI algorithms”, and “Unclear profits and advantages” were the top five influential causal issues that affect the adoption of AI in CSCs. This study is a novel attempt in the direction to identify and prioritize the potential issues in the adoption of AI-based frameworks in the Indian CSCs.

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Artificial intelligence in product lifecycle management

Cited by 93 publications

References 163 publications

A seven-layer model with checklists for standardising fairness assessment throughout the AI lifecycle

A seven-layer model with checklists for standardising fairness assessment throughout the AI lifecycle

Untitled

Identifying issues in adoption of AI practices in construction supply chains: towards managing sustainability

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