# Towards Unlocking the Hidden Potentials of the Data-Centric AI Paradigm in the Modern Era
## 1 Introduction  
Artificial intelligence (AI) is a mainstream technology with a wide range of promising applications in different sectors such as healthcare, smart cities, chatbots, etc. The representative AI applications in the medical area are disease classification using convolutional neural network (CNN) by leveraging image data [1], fetal brain MRI segmentation to identify brain abnormalities [2], accurate and effective segmentation of medical images for clinical assessment of different diseases [3], personalized healthcare and medical content generation for personalized medication and surgery planning [4], medical question-answer systems [5], and situational awareness for people who are visually impaired or blind in indoor environments [6], to name just a few. The representative AI applications in industry are product quality and design optimization [7], fault detection and failure mode prediction [8], industrial predictive modeling by extracting salient and far features with CNN [9], predictive maintenance [10], detecting defects in products [11], video surveillance [12], and robust continuous-flow manufacturing processes by imputing missing time series data [13], to name just a few. Due to compelling interests from both academia and industry, AI has vastly improved its architectural aspects, including the code of AI models, network architectures, and internal optimizations. Recent research is paying ample attention to further advancing AI technology via hyperparameter optimization, model size reduction (quantization), training AI models with as few data as possible, and pruning redundant weights [14]. Generally, AI contains a model (or algorithms) that determines a solution to a problem by finding key features (or observing patterns) from the given data. AI ishighly dependent on both code and data (i.e., \(AI=C+D\), where \(C\) denotes the models/algorithms and \(D\) is the data) to solve any real-world problem. In some cases, AI models can perform poorly due to incomplete or low-quality data, loose/strict learning rates, or drastic changes in the conditions of the deployment environments. At present, there are two practical ways to increase AI models' performance: data-centric AI (DC-AI) and model-centric AI (MC-AI). [PERSON] introduced the idea of DC-AI on 24 March 2021 [15]. According to [PERSON], the main idea of DC-AI is to systematically improve data quality rather than the AI model's code alone while developing AI-based systems. Since the introduction of DC-AI, many breakthrough developments have been made and many are underway. For instance, [PERSON] [16] applied the DC-AI approach to time series data and achieved 100% accuracy in anomaly detection. [PERSON] et al. [17] applied the DC-AI concept to train a deep neural network with fewer, but good-quality data. The authors offered a principled approach to improve accuracy while significantly reducing the model size. Some data-centric solutions have been developed for preparing high-quality data to make the training process fair and to reduce the computing overheads of the complex neural network model. There are many real-world scenarios like autonomous cars where data can be noisy, messy, and heterogeneous, owing to different sensor uses. Fusing data that stem from multiple sources often requires reliability, consistency, synergism, and alignment. This needs very careful attention to the cleaning/pre-processing, harmonization, formatting, transformation, and fusion of data. By leveraging a data-centric approach, we can pinpoint and resolve data-quality-related issues, i.e., missing/wrong data, and guarantee that the final data are dependable, robust, accurate, and suitable. Despite these developments, there are various challenges and misconceptions about DC-AI, which can diminish further developments in it. It is worth noting that some studies have discussed the importance of the DC-AI paradigm for relevant communities [18], but many important questions/concerns have remained unexplored.