# Towards Unlocking the Hidden Potentials of the Data-Centric AI Paradigm in the Modern Era
## 7 Challenges and Future Prospects  
DC-AI is a very new paradigm and is prone to many technical challenges when it comes to actual use in real-world scenarios. For instance, in the absence of clear standards for gauging the quality of data, how can one use DC-AI? Similarly, there are no practical tools (or software) that can spot data vulnerabilities to alert developers. We identified and discussed multiple technical challenges of DC-AI in real-life scenarios from the perspective of data or models, or both, and presented various important directions for future research/development. Joint efforts to solve these challenges can spark technical developments in this line of work. It is worth noting that DC-AI is a present-day paradigm, and there is significant room for research from any aspect (the development of new techniques, models, frameworks, prototypes, etc.). The key challenges and important future research directions are given below:  
* There is a significant lack of technologies that can assist in realizing DC-AI. At present, there are only a few enabling technologies (synthetic data, transfer learning (TL), etc.) used in DC-AI. However, the potential synergies between DC-AI and other AI technologies, such as transfer learning and generative models, can expand the application horizon for DC-AI. For instance, the TL approach is handy in addressing data scarcity and availability issues, and it can foster data re-usability across identical domains/problems [68]. TL can assist in realizing the DC-AI concept in low-resource healthcare settings [68]. TL-like approaches are required to prepare data for diverse domains with minimal effort by transferring knowledge/information from one domain to another. On the other hand, generative models like GAN/TVAE are handy for generating new data (also known as synthetic data), which can be used to enlarge  
Figure 9: Structure of confusion matrix. This schematic shows the structure of the confusion matrix obtained from experiments via DC-AI. the data size, as well as the diversity [66]. The generative models can assist in solving imbalanced learning problems in machine learning (ML), and can increase prediction accuracy for minor classes [69, 70]. In some cases, they contribute to reducing the bias in ML/AI models [71]. Furthermore, generative models assist in performing complex tasks (e.g., human analysis) with AI, which are not possible in conventional settings, owing to limited high-quality data [72]. However, there is still a serious lack of technologies that can pinpoint faults in the data, and that can raise alerts when some parts of the data are misleading or labeled incorrectly. Therefore, developing new DC-AI-enabling technologies or modifying the existing technologies to accomplish the DC-AI criteria is very challenging. Thus, the development of enabling technologies that can assist in achieving multiple aspects of DC-AI is an exciting area of future research. * In some cases, the DC-AI approach is no longer required when the data are sound, complete, up-to-date, and highly diverse. However, there is a lack of methodologies/procedures through which one can identify when DC-AI is inherently fulfilled. Furthermore, the decision about when to use DC-AI also needs rigorous criteria/methods, because MC-AI yields better results in some areas. For example, if there are fewer vulnerabilities in the data, the corresponding AI model can overcome them without a DC-AI approach by utilizing averaging, or by employing an optimized \(\alpha\) (learning rate). To this end, one may not need the DC-AI approach. Hence, developing techniques that can distinguish whether DC-AI is imperative or not can be a feasible topic for future research. * It is very hard to decide how much quality enhancement is needed when it comes to different areas (e.g., predictive maintenance vs. human activity analysis). For example, an application for text analysis may need less pre-processing because the application needs to handle both noisy and well-written text.