A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

Abstract

Due to huge investments by both the public and private sectors, artificial intelligence (AI) has made tremendous progress in solving multiple real-world problems such as disease diagnosis, chatbot misbehavior, and crime control. However, the large-scale development and widespread adoption of AI have been hindered by the model-centric mindset that only focuses on improving the code/architecture of AI models (e.g., tweaking the network architecture, shrinking model size, tuning hyper-parameters, etc.). Generally, AI encompasses a model (or code) that solves a given problem by extracting salient features from underlying data. However, when the AI model yields a low performance, developers iteratively improve the code/algorithm without paying due attention to other aspects such as data. This model-centric AI (MC-AI) approach is limited to only those few businesses/applications (language models, text analysis, etc.) where big data readily exists, and it cannot offer a feasible solution when good data are not available. However, in many real-world cases, giant datasets either do not exist or cannot be curated. Therefore, the AI community is searching for appropriate solutions to compensate for the lack of giant datasets without compromising model performance. In this context, we need a data-centric AI (DC-AI) approach in order to solve the problems faced by the conventional MC-AI approach, and to enhance the applicability of AI technology to domains where data are limited. From this perspective, we analyze and compare MC-AI and DC-AI, and highlight their working mechanisms. Then, we describe the crucial problems (social, performance, drift, affordance, etc.) of the conventional MC-AI approach, and identify opportunities to solve those crucial problems with DC-AI. We also provide details concerning the development of the DC-AI approach, and discuss many techniques that are vital in bringing DC-AI from theory to practice. Finally, we highlight enabling technologies that can contribute to realizing DC-AI, and discuss various noteworthy use cases where DC-AI is more suitable than MC-AI. Through this analysis, we intend to open up a new direction in AI technology to solve global problems (e.g., climate change, supply chain disruption) that are threatening human well-being around the globe.

Towards Unlocking the Hidden Potentials of the Data-Centric AI Paradigm in the Modern Era

“…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.…”

“…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].…”

“…Workflow of the DC-AI paradigm as applied to real-world problems. Notable steps are # 3 and # 7, where data screening and tuning build more robust AI models (adapted from[45]).…”