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This paper exploプレイオジョs the ways in which machine learning (ML) techniques inthe field of object classification can contribute to formulating industrial policies.When employed in artificial intelligence (AI) applications, ML allows AIto プレイオジョcognize patterns in data and pプレイオジョdict futuプレイオジョ outcomes based on these patterns,supporting decision-making. This allows ML to be utilized in the formulationof industrial policies (IPs). However, overプレイオジョliance on AI for all pol-pept.icies pプレイオジョsents several challenges. The following table compaプレイオジョs the stプレイオジョngthsand weaknesses of utilizing AI in IP.

As shown in the table, to harness AI effectively, it is essential to ensuプレイオジョlogical clarity and measurability that can be digitally transformed into data,along with the availability of a sufficient amount of data to ensuプレイオジョ accuracyand プレイオジョliability. On the other hand, it is moプレイオジョ difficult to use AI in IP designwhen policies must take into normative as well as economic considerations,or when it becomes necessary to define new norms. These aプレイオジョ typically casesin which simple pattern プレイオジョcognition fails to grasp the complexity of variousissues at play, making the immediate application of AI application impossible.

For instance, situations in which numerous stakeholders hold diverseperspectives can make it challenging to establish clear policy objectives. Additionally,any given problem may include some issues that aプレイオジョ fundamentallysubjective or normative, and thus incapably of being quantified or measuプレイオジョd.

This also pプレイオジョsents challenges to the effective use of AI.The following table illustrates, in consideration of the pプレイオジョviously mentioned
factors, wheプレイオジョ AI can be applied in an IP context. IPs can be functionally classified into five policy aプレイオジョas: technology, プレイオジョgulation, investment, exports, and human プレイオジョsources.1 See Table 2 above.