## 3 Definition and Problems
### Problems  
The risks with AI systems mainly come from data accountability [24; 59] and algorithm accountability [60; 61; 62]. The connotation of data accountability mainly includes data ownership, storage, use, and sharing, while algorithm accountability emphasizes determining who is responsible for the output of AI algorithms. The interplay of these two risks also raises the question of mission inclusivity [63; 64; 65], which mainly focuses on whether the AI system is effective for diverse user populations. Bias effects in machine learning are shown in Figure 2.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Data Bias** & **Definition** & **Main Cause** & **References** \\ \hline Individual Fairness & Similarity at the individual level & Treat similar individuals similarly & [40; 41; 48; 49] \\ \hline Group Fairness & Equitable outcomes for demographic groups & Avoid disparities among groups & [42; 50; 51] \\ \hline Fairness through Unawareness & Ignoring sensitive attributes & Treat individuals as if attributes are unknown & [44; 52; 45] \\ \hline Equality of Opportunity & Equal chances for similar qualifications & Ensure equal chances for outcomes & [11; 53; 54] \\ \hline Disparate Impact & Disproportionate negative effects & Evaluate disparities in outcomes & [6; 46; 55] \\ \hline Disparate Treatment & Explicit unequal treatment & Detect explicit biases in treatment & [43; 56; 57] \\ \hline Subgroup Fairness & Fairness attributes &
\begin{tabular}{p{113.8 pt}} Consider fairness for multiple groups \\ \end{tabular} & [47; 58] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fairness definition.  
Figure 2: Bias effects in machine learning.  
Biases in AI models can arise from data collection, labeling, and partitioning, affecting data integrity. Human factors during training, including optimization objectives and parameter configurations also contribute to bias. Inclusivity applications involve tuning and updates guided by user feedback, which holds substantial influence. Techniques like K-fold mitigate dataset bias, but original bias persists, highlighting the importance of robust data accountability. Subjective optimization objective design exacerbates bias effects. Addressing these issues requires comprehensive strategies for data and model development.  
Feedback bias in AI model tuning can arise if participants providing feedback are disproportionately represented in specific communities or feature sets. This concentration can lead to model adjustments aligning more closely with the preferences of that group. Grouping can be based on experimental settings or attributes, aiding the analysis of cognitive and labeling differences' impact on bias patterns. Statistical features synthesized from group evaluations can influence machine learning model outcomes.  
The inclusivity of intelligent computing services relies on data and algorithm accountability to mitigate bias and ensure fairness in machine learning processes. While industry practices often exclude sensitive attributes to address fairness concerns, this approach overlooks the potential influence of non-sensitive attributes in reflecting bias. Additionally, evaluating fairness using static test sets poses challenges, including potential incompleteness and inherent bias carried over from existing systems.  
Moreover, the feedback loop between machine learning system outputs and inputs can perpetuate and reinforce biases, necessitating the analysis of algorithms in dynamic systems. Label noise further complicates the picture, as large datasets essential for deep network training can inadvertently incorporate incorrect labels, undermining model accuracy and performance.  
Addressing these challenges, ongoing research focuses on detecting and mitigating bias while designing fair machine learning mechanisms and intelligent systems. Bias analysis and fair training emerge as critical areas, aiming to enhance the technical understanding and current status of each direction.