# Towards Unlocking the Hidden Potentials of the Data-Centric AI Paradigm in the Modern Era
## 3 Introduction of Model-Centric AI and Data-Centric AI  
In this section, we describe the MC-AI and DC-AI workflows and highlight the main differences between them. A typical MC-AI approach can be applied to any real-world problem with the help of six steps: (1) problem definition, (2) data collection, (3) the collected data's pre-processing, (4) AI model training, (5) AI model deployment, and (6) the deployed model's performance monitoring [45]. Specifically, AI models are trained on data collected from relevant users/environments after basic pre-processing; performance is analyzed, and then, the models are deployed in real environments. Subsequently, performance is gauged, and if the performance is poor, only architectural aspects of the AI model are improved (e.g., modifying the network structure, altering the model size, tuning the hyperparameters, and varying \(\alpha\)) [46]. In the MC-AI approach, the AI practitioners mainly focus on five things [27]: (i) advancing the network architecture of AI models, (ii) optimizing the architecture of AI models by removing unused layers or pruning redundant weights, (iii) developing new AI models, (iv) devising new training methods and advancing data modalities, and (v) improving performance via hyperparameter tuning. In MC-AI, three common practices are adopted to resolve performance-related issues:  
1. Rigorously fine-tune the code/algorithm of the AI model. 2. Obtain additional data for everything and take the average, or optimize the learning rate (\(\alpha\)). 3. Switch to an alternate AI model (\(\text{CNN}\rightarrow\text{LSTM}\) or \(\text{LSTM}\rightarrow\text{RNN}\)). All of the above practices have potential drawbacks when it comes to some specific areas. For example, the first practice may yield minimal enhancement in the AI model's result; the second practice might increase the time complexity (or start a never-finishing data curation/collection cycle); the third may significantly enlarge the commercialization time (e.g., development \(\rightarrow\) deployment) of AI products. To solve these problems, AI experts need to go back and examine the entire process (scrutinize the entire process), even when the data are not being curated. However, this practice (tracing each phase) can be costly, and can likely stop the AI implementation process for a long period. Hence, an alternate approach is needed to address these problems and to advance the status of AI developments. Figure 1 shows the workflow of the DC-AI paradigm when applied in any realistic setting. In contrast to MC-AI, DC-AI suggests rigorous inspection and debugging of data before building (or training) AI models and can be vastly successful in some specific areas involving limited/poor-quality data. Also, when an AI model gives poor results, developers are required to inspect the data, as well as the code/algorithm. The in-depth and rigorous screening of the data is performed (Step 3), and if the results are not satisfactory (Step 7), the code and data are both improved. By paying ample attention to the data in all phases of AI system development, misleading samples are not inadvertently propagated to the training process, and therefore, highly reliable AI systems can be developed [47; 48]. Fundamental differences exist in each approach. The prime focus of MC-AI is to optimize code and code-related aspects; in contrast, DC-AI explores ways to improve data quality and consistency. Quantitative Analysis/Comparison between MC-AI and DC-AI  
The essence of DC-AI is to improve data in order to accomplish fair learning and better performance. The key evaluation aspects to compare DC-AI and MC-AI are accuracy, model complexity (in terms of parameters), the least number of samples without degrading accuracy, the false positive/negative rate, precision, recall, sensitivity, specificity, the \(F_{1}\) measure, etc. To better highlight the promises of DC-AI and to compare it with MC-AI, we report quantitative results on the steel sheet inspection scenario [22].