# Towards Unlocking the Hidden Potentials of the Data-Centric AI Paradigm in the Modern Era
## 7 Challenges and Future Prospects
* In the absence of successful implementations, it is challenging to determine the order in which DC-AI techniques will be applied to yield robust AI systems. For example, determining when to apply data labeling and data completeness analysis in a gaze estimation application is challenging. Similarly, it is hard to decide when to not use a certain technique (e.g., data availability). Considering these circumstances, deciding the order and types of techniques is tricky. Similarly, the set of DC-AI techniques applied to tabular data may not yield feasible results in image data, and therefore, a set of unified DC-AI techniques is required for each data type. Furthermore, determining the optimal order and types of DC-AI techniques to apply for different datasets and applications is a very complex problem. In our recent work, we devised a general system by discussing the order and types of DC-AI techniques to be employed in stroke prediction scenarios, which can be used as a reference to determine the optimal order and types of DC-AI techniques to apply for different datasets and applications [45]. Similarly, some potential DC-AI techniques that can be generically applied across the ML pipeline are given in [PERSON] et al. [49]. However, determining the optimal order and types of DC-AI techniques to apply for different datasets and applications is a very challenging problem, and it requires further investigation from relevant communities. Ablation studies to determine appropriate combinations of DC-AI techniques for diverse domains is an exciting prospect to be explored in the DC-AI context. * Just like conventional AI approaches, some pre-processing techniques (e.g., sampling) do not work well with some AI models. The same problem can happen with DC-AI techniques, and therefore, analyzing the suitability of DC-AI techniques with respect to data and AI models requires in-depth investigation. Generalizing DC-AI techniques to effectively work in related applications with slight modifications is also very challenging. Hence, conducting tests and identifying suitable DC-AI techniques for each AI model requires further investigation from the AI community. * In conventional MC-AI, the data are evaluated just once (e.g., before feeding them into the AI model). By contrast, in DC-AI, the evaluation of the data is required at multiple stages in the AI system lifecycle. Hence, data quality analysis before building the model (e.g., during pre-processing) and after model training are different. In the pre-processing stage, data completeness/freshness is mandatory, but after training, balanced data utilization (whether the AI model utilizes all parts of the data equally or not) is imperative. To perform systematic analysis and quality assurance, sophisticated expertise may be required in each stage. However, there is a substantial lack of domain experts for each stage of the AI system lifecycle, which can hinder the applicability of DC-AI in practical scenarios. Identifying and documenting the list of requirements to be fulfilled concerning data in each stage is very challenging, and requires more investigation. * There is a genuine lack of procedures and standards concerning data quality. For example, 10% of the data being good may be sufficient in some applications, whereas some applications may require 90% to yield consistent results. Considering the huge diversity in data styles and applications, developing appropriate standards and procedures for gauging data quality is challenging. In the future, it is vital to develop formal procedures and well-defined standards for gauging data quality in multiple AI applications. * The key focus of DC-AI is to augment data quality. However, this is a very time-consuming, challenging, and laborious task, especially when the size of the data is very large. In addition, reducing the overall complexity of the data-quality-enhancement process is an interesting and urgent topic. At present, there is a lack of software or customized libraries to automatically perform most operations.