# Towards Unlocking the Hidden Potentials of the Data-Centric AI Paradigm in the Modern Era
## 1 Introduction
For instance, many AI practitioners think that DC-AI is simply a form of pre-processing; however, this is not the case, because DC-AI encompasses more sophisticated techniques that are not part of pre-processing. Similarly, the AI research community has a lack of clear understanding of when to properly amalgamate DC-AI with MC-AI to solve some real-world problems. On the other hand, some of the DC-AI techniques are difficult to implement as measuring the quality of entire datasets is a complex task, especially when the data volume is high and the number of modalities is high. Recently, some libraries like Influenciae have been introduced, but still, it is very challenging to identify bad parts of data [19]. Similarly, Python libraries like deepchecks have also been recently developed to evaluate ML models and datasets [20]. Lastly, there is an inadequate understanding of the current status of this paradigm and what lies ahead. Motivated by these research gaps, we present a detailed analysis of this fledgling paradigm for the AI and computer science community. Although MC-AI gives comparable results for many real problems, i.e., natural language processing (NLP) [21], it is usually applicable to very limited scenarios, where a large volume of data are available. In most real-life scenarios, large datasets are either unavailable or may not be easily curated due to limited budgets or incompetence in handling big datasets. Therefore, how to address limited data issues while achieving comparable accuracy with AI models is a complex task. To that end, DC-AI can be a feasible alternate that encompasses a large number of data-specific tools/techniques and can improve the AI model's results [22]. DC-AI is introduced to combat the most dominant MC-AI trend/mindset, enhancing AI technology advancement and development in the coming years [23]. Furthermore, DC-AI can likely enhance AI adoption in industrial and commercial settings [24]. This paper will have scholarly impacts on the boundaries of data quality enhancement, data optimization, machine/deep learning, and AI use in real situations. It is likely that this paper and its extensions will lead to further technical advancement in the AI field, and will democratize AI developments that are currently in the hands of high-resourcedorganizations [25]. Consequently, many existing limitations of AI models can be rectified and AI benefits can likely be extended to marginalized groups of our society. Next, we clarify the major goals of the study in terms of the conceptual, theoretical, design, and application perspectives. Conceptually, this paper explores an emerging paradigm that remains relatively less explored than MC-AI and holds significant potential to solve many real-world problems. Also, this topic can be a potential avenue for AI and database researchers who intend to explore new frontiers in AI systems. Theoretically, this paper can open up a new research dimension that can create opportunities for formal methods to prove the efficacy of DC-AI techniques, as well as their limitations. From a design perspective, it provides a reference/generic architecture and proofs of concept, which can be fine-tuned or customized depending on the problem/domain, leading to technical advancement in the AI field. From the application point of view, this paper provides insight into extending AI applications to domains where data are limited or the quality of the data is below par. Overall, this paper aims to systematically introduce an emerging paradigm, named DC-AI, along with the associated concepts/examples to increase AI developments and possibly reduce the harms of AI. Our major contributions are summarized as follows:  
* _Systematic introduction of DC-AI_: We systematically present what DC-AI entails and how it can be systematically applied in practice. We also discuss the role of DC-AI in advancing AI technology, which has not been comprehensively discussed in the literature [26].