As the data collection methods have extreme influence over the validity of the research outcomes, it is considered as the crucial aspect of the studies
May 2025 | Source: News-Medical
In the fast-paced digital ecosystem that surrounds us today, the success of AI/ML model development is largely dependent on high-quality datasets. The axiom “garbage in, garbage out” is particularly salient in machine learning—your model can only perform as well as the data it is trained on! Organizations should implement strong approaches to collect and curate data (and processes related to data collection coupled) with compliance and security guidelines, if they are looking to produce trustworthy, unbiased and accurate AI systems. [1][2]
The first step in building a trustworthy AI/ML model is to find a collection of data that is representative, diverse, and well-labelled. Using poorly labelled data can potentially lead to:
There are many effective ways that organizations and researchers can gather data:
While “data quality” can be a broad term based on size, it’s also about accuracy, diversity, relevance, and compliance. Comprehensive quality assurance will include:
Collecting data is only part of the challenge. Organizations must establish a secure storage strategy for sensitive information. They must consider the following:
Compliance with ISO 9001:2015, ISO/IEC 27001:2013, HIPAA Compliance, and GDPR Compliance ensures legal compliance, privacy of data, and credibility as a global organization. [5]
One of the processes used by AI systems is to label raw data so it can be transformed into machine-readable formats, for example:
Structured annotation workflows promote quality assurance, reduce manual error potential, and speed-up the pipeline of data for an AI system. [6]
It is important to incorporate both compliance and security at every stage of data collection at a time when concerns about privacy reach an all-time high. This may include protecting any possible personally identifiable information (PII) via anonymization, secure where data is stored, and audits. Any part of your AI Project making itself safe for PII helps to build trust and prevent reputational and legal risk. [7]
The foundation of AI/ML model development rests within effective data collection approaches. All forms of data collection, including surveys, sensor data, web scraping, and custom crowdsourcing, will require quality assurance, removal of bias, and regulations to facilitate the data collection process. Quality assurance encompasses data storage and data annotation practices so that companies can be sure they have a dataset that is reliable, ethical, and can scale when deploying an AI solution.
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