Table 3.

Key components in designing an AI risk management framework for industrial systems.

Key activity	AI use case identification & description	Prioritization of use cases	Evaluation of existing risk management frameworks	Risk measurement & quantification	Legal and regulatory considerations	Dynamic regulation of algorithms	Support for safety & reliability	Source
Use case identification	✓							Stuurman and Lachaud (2022)
Scope clarification	✓							Zhang et al. (2022)
Use of templates	✓							Brunnbauer et al. (2021)
Risk evaluation & mitigation		✓		✓				Baquero et al. (2020); Greiner et al. (2022); Lauterbach (2019)
IT system maturity assessment		✓						Mäntymäki et al. (2022)
Screening of existing frameworks			✓					Butcher and Beridze (2019)
Adapting frameworks for industry			✓					de Almeida et al. (2021); Cheatham et al. (2019); Chesterman (2019)
Risk quantification				✓				Bannister and Connolly (2020); Wirtz et al. (2020); Schneider et al. (2023)
Algorithm transparency & impact				✓				Baquero et al. (2020); Bannister and Connolly (2020)
National AI regulations					✓			Mäntymäki et al. (2022)
International AI governance rules					✓			Chambers (2021); Ellul et al. (2021)
Avoiding AI innovation restrictions					✓			Baquero et al. (2020); Wirtz et al. (2020)
Dynamic algorithm regulation						✓		Nikitaeva and Salem (2022); de Almeida et al. (2021); Chambers (2021)
Safety & reliability							✓	Nikitaeva and Salem (2022); Zhang et al. (2022)
Process flow optimization							✓	Nikitaeva and Salem (2022)
Agile AI development support							✓	Baquero et al. (2020); Wirtz et al. (2020)