# A Maturity Model for Trustworthy AI Software Development
## 3 A Maturity Model for Trustworthy AI Software
### \"Pr9\" Case Study: Evaluating the Effectiveness of AI-MM
#### 4.2.2 Assessment Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Process Dimension**} & \multicolumn{6}{c}{**Capability Dimension**} & \multicolumn{2}{c}{**Assessment Evidence**} \\ \hline
**Area** & **Process** & **Base Practice** & **L1** & **L2** & **L3** & **L4** & **L5** & **Result** & \\ \hline \multirow{4}{*}{Data C\&P} & \multirow{2}{*}{DCO} & \multirow{2}{*}{Data bias assessment} & \multirow{2}{*}{O} & \multirow{2}{*}{O} & \multirow{2}{*}{} & \multirow{2}{*}{Level 2} & Biases are addressed by the “Everyone’s corpus” of the National Institute of the Korean Language, which was evaluated as “bias free” \\ \cline{3-7}  & & & & & & & Language, which was evaluated as “bias free” \\ \cline{3-7}  & \multirow{2}{*}{DCL} & \multirow{2}{*}{Data bias periodic check} & \multirow{2}{*}{X} & \multirow{2}{*}{} & \multirow{2}{*}{Level 0} & \multirow{2}{*}{Bias issues are reviewed only if there is a report related to data bias} \\ \cline{3-7}  & & & & & & & & \\ \cline{3-7}  & & & & & & & & \\ \cline{3-7}  & \multirow{2}{*}{DPR} & \multirow{2}{*}{Data distribution verification} & \multirow{2}{*}{N/A} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} \\ \hline \multirow{4}{*}{Model D\&E} & \multirow{2}{*}{TPM} & Bias review during model learning & \multirow{2}{*}{O} & \multirow{2}{*}{O} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} \\ \cline{3-7}  & & & & & & & & \\ \cline{3-7}  & \multirow{2}{*}{PEA} & Fairness maintenance check & N/A & & & & & \\ \cline{3-7}  & & & & & & & & \\ \cline{3-7}  & \multirow{2}{*}{FAM} & Model’s fairness evaluation result record & X & & & & & & \\ \hline \multirow{4}{*}{O\&M} & \multirow{2}{*}{AIN} & Fairness management infrastructure & \multirow{2}{*}{O} & \multirow{2}{*}{O} & \multirow{2}{*}{O} & \multirow{2}{*}{} & \multirow{2}{*}{} \\ \cline{3-7}  & & & & & & & & & \\ \cline{3-7}  & \multirow{2}{*}{AOM} & Monitoring model quality & \multirow{2}{*}{O} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} \\ \cline{3-7}  & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 13: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Languages**} & \multirow{2}{*}{**Source Input**} & \multicolumn{2}{c}{**Target Output (Translated Text)**} & \multirow{2}{*}{**Comparison Result**} \\ \cline{3-7}  & & **Global IT Service** & & **Our Service** & \\ \hline \multirow{4}{*}{Korean \(\rightarrow\) English} & \multirow{4}{*}{My brother} & \multirow{4}{*}{My younger sibling} & [Global IT service: Inferior] \\  & & & & & \({}^{\text{``brother'' is}}\) \\ \cline{3-7}  & & & & & \({}^{\text{``}}\) \\ \cline{3-7}  & & & & & & male-biased \\ \cline{3-7}  & & & & & & \\ \cline{3-7}  & & & & & & & \\ \cline{3-7}  & & & & & & & \\ \cline{3-7}  & & & & & & & \\ \cline{3-7}  & & & & & & & \\ \cline{3-7}  & & & & & & & \\ \hline \multirow{4}{*}{Spanish \(\rightarrow\) English} & [PERSON] nació en & [PERSON] nsor in Warsaw. She received the Nobel & [PERSON] was born in Warsaw. He received the Nobel Prize in 1903 and in 1911. & [Our service: Inferior] and in 1911. & [Our service: Inferior] \\ \cline{3-7}  & & & & & & & \\ \cline{3-7}  & & & & & & \\ \cline{3-7}  & & & & & & & \\ \cline{3-7}  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparison of fairness issues in two language translation services. information, such as \"It is equipped with the function of filtering abusive language that can offend users when outputting machine translation results.\"