# AI in Context: Harnessing Domain Knowledge for Smarter Machine Learning
## 2 The Synergy of AI/ML and Domain Knowledge
### Bridging the Gap Between Data Scientists and Domain Experts  
Bridging the gap between data scientists and domain experts is crucial for the success of AI/ML projects, yet it presents several challenges that stem from the diverse backgrounds, perspectives, and languages of these two groups.  
1. Differences in Terminology and Communication: Data scientists and domain experts often speak different \"languages\" [49]. The technical jargon and complex statistical terms used by data scientists can be bewilder to domain experts who may be more familiar with industry-specific terminology [50]. This language barrier can lead to misunderstandings or misinterpretations of data insights or project requirements [51].  
2. Varied Approaches to Problem-Solving: The two groups may have different approaches to problem-solving. Data scientists often focus on data-driven solutions and may prioritize model accuracy and computational efficiency [52]. In contrast, domain experts may emphasize practical feasibility, interpretability, and the broader context of the problem, including regulatory and ethical considerations [53].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Challenge** & **Solution** & **Example** \\ \hline \multirow{3}{*}{Communication gaps} & Cross-disciplinary training and regular structured meetings & Workshops and collaborative platforms \\ \hline \multirow{3}{*}{Data misinterpretation} & Involvement of domain experts in data preprocessing and feature engineering & Joint feature selection sessions \\ \cline{2-3}  & Implementing continual learning and active learning & Online learning models updating in real time \\ \hline \multirow{3}{*}{Ethical compliance} & Close collaboration with domain experts to ensure adherence to laws and ethical standards & Regular audits and compliance checks \\ \hline \multirow{3}{*}{Trust and adoption} & Use of interpretable models and visualization tools to improve transparency & Interactive dashboards for end-users \\ \hline \hline \end{tabular}
\end{table}
Table 3: Challenges and solutions in integrating domain knowledge.  
3. Differing Expectations and Goals: There may be a misalignment in the expectations and end goals [54]. Data scientists might aim to develop the most advanced model, whereas domain experts might be more interested in actionable insights that fit into existing workflows and decision-making processes [55; 56].  
4. Challenges in Data Interpretation: Domain experts might lack the technical expertise to fully understand and interpret complex models and results presented by data scientists [57]. Conversely, data scientists might not fully appreciate the nuances and implications of the data without the domain-specific insights, leading to an oversimplified or incorrect interpretation of the data [58].  
5. Collaboration and Trust Issues: Effective collaboration requires trust and mutual respect, which can be hindered by a lack of understanding of each other's expertise and contributions [59; 60]. Building trust takes time and effort, and without it, collaboration can be strained, slowing down the progress of AI/ML projects [60; 61] (Figure 2).  
To overcome these challenges, it is important for both data scientists and domain experts to engage in open, frequent, and clear communication; appreciate each other's expertise; and work towards a common goal. Tools and methodologies that facilitate mutual understanding, like shared data visualization platforms or collaborative workshops, can also be instrumental in bridging this gap.