# A Systematic Mapping of Quality Models for AI Systems, Software and Components
## 5 Analysis and Results
### Data Synthesis
#### 5.2.3. RQ3: What Are the Quality Models for AI Software Components? Currently, there are several software development paradigms [75]. In addition, the specificity of each software paradigm requires the development of its own software quality model. For instance, quality models for traditional software, object-oriented software, service-oriented software, and component-based paradigms have been discussed so far. Recently, few quality models for the AI and ML software paradigm have been proposed. The AI software component paradigm, specifically, the ML software component paradigm, differs from the AI and ML software paradigm because such components are black boxes (i.e., their internal structure is hidden). The quality of such components can only be \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Quality Level** & **Evaluation** & **Weakness** \\ \hline
[10] & AI systems & No evaluation & This study proposed a few quality characteristics for AI systems in the public sector. It did not conduct evaluation of the acceptability of the proposed quality characteristics. This study is a position paper and lacks comprehensiveness in terms of the quality characteristics it covered. \\ \hline
[21] & ML systems & Case study & The study proposed quality characteristics for ML systems. The proposed quality model was based on four views: Data view, System view, Infrastructure view, and Environment view. Three case studies have been conducted for the evaluation for the proposed quality characteristics. \\ \hline
[23] & AI systems & No evaluation & Only provided holistic insights incorporating the ML nature and AI ethics to SQuaRe from traditional software engineering. Only focused on external quality characteristics for SQuaRe, and evaluation was not performed either empirically or practically. \\ \hline
[73] & AI systems & No evaluation & Only proposed limited quality characteristics for AI system architecture. Empirical evaluation for the proposed quality characteristics from experts is also missing. \\ \hline
[74] & AI systems & Empirical evaluation & This study proposed evAIa (evaluate AI approaches) for AI system quality assurance (QA). EvAIa leads to transparency about the current state of QA. EvAIa received fast acceptance, for example, in a centralized AI competence centre which introduced evAIa as a standard for its project QA. The study conducted evaluation of the acceptability of the proposed quality characteristics. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Existing Quality Models for AI Systems. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Quality Level** & **Quality Characteristics** \\ \hline
[10] & AI Systems & Security, privacy, data-centricity, sustainability, and explain-ability. \\ \hline
[23] & AI Systems & Risk on human autonomy, collaboratability, fairness, privacy, accountability, maintainability, modifiability, operability, modularity, integrity, and uncertainty \\ \hline
[73] & AI Systems & Testability, usability, readability, and self-descritiveness. \\ \hline
[74] & AI Systems & Adequacy, Bias, Completeness, Compliance, Execution environment, Worst cases (compute power, memory size), Third party reliability, Completeness, Process-chain, Regulations/compliance, Training and testing chain, Model transparency, Model completeness, Configuration, Execution environment, Monitoring, Validation, Training and testing chain, Third party, and “Model fitting” \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Quality Characteristics for AI Systems. measured through external interfaces. Quality assessments of individual ML software components will have an impact on the overall system and software. The SMS found no quality models for AI software components. [PERSON] et al. [76] supported the need of AI software component quality models, specifically ML software components. To the best of the authors' knowledge, there are no available quality assurance models for AI software components, specifically ML software components.