MicroEthics: Harnessing Small Data Intelligence to Recode Big Data Ethics for a Responsible Digital Future

Abstract

The exponential growth of big data has revolutionized AI, yet it has simultaneously triggered ethical concerns involving over-collection, privacy erosion, opaque decision-making, and algorithmic bias. Here, the paper proposes changing the paradigm of data maximalism to small data intelligence (SDI)-based MicroEthics, which is more ethical-minimalist, transparent, and stakeholder-trustful. The proposed system does not use massive datasets but rather uses ethically curated low volume data with context, processed on interpretable AI models embedded on federated learning environments. There is a custom lightweight decision engine, Symbolic Decision Matrix Engine (SDME), that makes decisions without compromising privacy or explanations. In several fields such as healthcare, finance, and digital governance, the MicroEthics was compared to the conventional big data pipelines. The findings demonstrate a federated model accuracy of 93.2 with privacy compliance score of 0.97 that is better than centralized big data systems with privacy compliance score of 0.71. MicroEthics Ethical Risk Scores were 0.91, which is significantly more than 0.65 of the traditional models. Stakeholder ratings ensured increased transparency (0.93) and user trust (0.91) using MicroEthics than the big data methods. This paper confirms that MicroEthics is a practical ethical framework that unites the utility of AI with human values. Not only does it show similar performance, but it goes higher in terms of ethical compliance and reliability than those that are already in place. The solution is scalable, modular and easily applicable in areas where moral accountability and data sensitivity are paramount. The results promote the concept of a responsible transition to ethical AI frameworks that do not rely on the quantity of data, but on data virtue.

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