AI Data Quality Copilots: Enhancing Intelligent Systems with Real-Time Data Integrity, Scalability, and Ethical AI Practices
Year: 2024
Type: article
Abstract: AI has proven to be a quickly growing field; hence, data quality is essential when attaining proper, equitable, and lasting AI solutions. Data quality is critically important as organizations rely more on AI for their decisions, training, and decisions. This paper aims to establish AI Data Quality Copilots; sophisticated systems focused on addressing data-related issues through automatic real-time data quality evaluation and enhancement. Future issues – data drift, privacy, and inclusion – as verified by AI Data Quality Copilots will remain non-triggering for the AI model's reliability and nonpartisan nature. This paper examines how DQ Copilot enables enterprises to answer those challenges arising from new trends affecting data, with examples from healthcare, financial, and retail industries, among many more. It also talks about how the applications of copilots foster more decision-making content at base levels in real time and reduce false fraud signals, as well as how ethical AI is achievable through identifying biases and tweaking them. By connecting these copilots, enterprises can extend and enrich AI with suitable data management and keep valuable AI models with constant good data inputs. The paper also looks at the various ethical aspects that are associated with the management of data quality. This illustrates how AI Data Quality Copilots bring fairness to AI thinking. The paper concludes by arguing that data quality copilots are crucial in both the sustainability and variability of AI within the relevant domains.
Author: Siva Karthik Devineni
Language: English
Topic: Data Quality and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6949540905
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