![](bg8.png)
work’s users. Hence, those without prior experience
in developing ML could not be included. Second,
each participant had differing abilities as well as fa-
miliarity with models, specifications, and general ML
development. We tried to minimize this threat by se-
lecting participants with similar expertise and experi-
ence. Additionally, we randomized the groups to min-
imize the threat.
5 CONCLUSION AND FUTURE
WORKS
This paper proposes and evaluates the Multi-View
Modeling for ML System (M
3
S) framework. The
case study and experiment revealed several vital find-
ings. On the positive side, the framework facilitates
the traceable multi-view approach to analyze safety-
critical ML systems. However, efficient utilization of
the framework requires a support tool for decision-
making of the solutions.
In the future, we plan to explore several different
directions. We will continue developing and evaluat-
ing the support tool. We want to explore the possibil-
ity of creating a guide by extracting existing solutions
into a catalog and extend the framework’s traceability
into the ML model’s training pipeline.
ACKNOWLEDGEMENTS
This work was supported by JST-Mirai Program grant
Number JPMJMI20B8, JST SPRING grant Number
JPMJSP2128, JSPS JPJSBP 120209936, and JSPS
KAKENHI 21KK0179.
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