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<p>The purpose of this study was to investigate explainable artificial intelligence 5.0 (XAI 5.0) in the context of emerging technologies for responsible innovation. A technology-foresight mapping review was initially conducted, identifying N<sub>1</sub> = 2,864 records. Subsequently, N<sub>4</sub> = 1,091 full-text assessments were screened, resulting in a final sample of N<sub>6</sub> = 338 Scopus-indexed publications published between 1 January 2020 and 1 October 2025. Technology foresight modelling illustrated that the cubic polynomial model<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:({P}_{t}=0.62{t}^{3}-4.13{t}^{2}+22.41t+34.12)\)</EquationSource> </InlineEquation> provided the best fit, with coefficients of determination (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\:{R}_{poly}^{2}=0.94\)</EquationSource> </InlineEquation> and <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\:{R}_{log}^{2}=0.91\)</EquationSource> </InlineEquation>). The results showed that the disruptive scenario (MI ≈ 0.52), the conservative scenario (MI ≈ 0.68), and the infrastructure scenario (MI ≈ 0.85) were identified as key trajectories within emerging responsible innovation. The results indicated that an innovation shift from narrow algorithmic interpretability toward broader accountability architectures and governance frameworks is necessary for emerging socio-technical ecosystems across industrial sectors.</p>

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Explainable artificial intelligence 5.0 for emerging technologies of responsible innovation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月6日

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  • Hanvedes Daovisan

摘要

The purpose of this study was to investigate explainable artificial intelligence 5.0 (XAI 5.0) in the context of emerging technologies for responsible innovation. A technology-foresight mapping review was initially conducted, identifying N1 = 2,864 records. Subsequently, N4 = 1,091 full-text assessments were screened, resulting in a final sample of N6 = 338 Scopus-indexed publications published between 1 January 2020 and 1 October 2025. Technology foresight modelling illustrated that the cubic polynomial model \(\:({P}_{t}=0.62{t}^{3}-4.13{t}^{2}+22.41t+34.12)\) provided the best fit, with coefficients of determination ( \(\:{R}_{poly}^{2}=0.94\) and \(\:{R}_{log}^{2}=0.91\) ). The results showed that the disruptive scenario (MI ≈ 0.52), the conservative scenario (MI ≈ 0.68), and the infrastructure scenario (MI ≈ 0.85) were identified as key trajectories within emerging responsible innovation. The results indicated that an innovation shift from narrow algorithmic interpretability toward broader accountability architectures and governance frameworks is necessary for emerging socio-technical ecosystems across industrial sectors.