7. January 2022
The development and usage of AI and machine learning have been among the hottest topics in the past decade. The technologies are proven to be efficient in identifying trends and patterns, automation, continuous improvement, capability in data handling, and other applications. We witnessed its rapid advancement in the 21st century, which has brought transformative changes to the tech industry and the whole world.
However, well-performing AI and machine learning models are dependent on high-quality data, which also includes how relevant the data is to the project. If AI innovation and machine learning model is not based on high-quality data, it may result in inaccurate analytics and unreliable decisions. Therefore, data quality plays a vital role in training AI and machine learning models.
What Is Data Quality And Why It’s Important
Data quality is most commonly measured by consistency, accuracy, validity, integrity, and completeness criteria. But these criteria are not absolute: the data should fit the planning and purpose of the project. A dataset with high completeness may be of high quality for project A, but not complete enough for project B focusing on a larger scale.
High-quality data can enhance data diagnosticity and speed up the decision-making process with more information. For businesses, it means an increase in revenues [1] . Low-quality data, usually reflected by incomplete, inconsistent, and missing values, can result in “drastic degradation in prediction” [2], and bias. Besides inaccurate results, the latter may also lead to discrimination against women, ethnic minorities, the elderly, etc. For example, if only a small number of female voices are included in the training dataset of a voice recognition system, the system may have questionable performance when used by women [3] . The problem of data quality can potentially impact the final result of AI and machine learning models.
Data Quality Under The GDPR
The GDPR requires businesses to have correct and complete personal data. However, having accurate and complete personal data does not guarantee GDPR compliance. Processing personal data is also under strict regulation under the GDPR. In order to be GDPR compliant, many businesses are not able to process personal data, though the data in their possession could be significant training data for AI and machine learning models. There are many ways of processing personal information so that companies do not avoid violating data protection regulations. But in execution, valuable information is replaced or blocked to prevent data leakage. The inconsistent and inaccurate data hinder the effectiveness of AI innovation and machine learning training.
How To Enable Data Analytics While Complying With The GDPR
The frustrating trade-off between data analytics and data protection can be avoided with anonymization, or specifically – AI-generated synthetic data. The technology creates a synthetic overlay of the original data, protects personal information, and keeps data quality for machine learning.
In the area of smart image and video analytics, brighter AI’s Deep Natural Anonymization is the world’s most advanced automatic redaction software, with state-of-the-art features for face and license plate anonymization. It guarantees the quality of the image and video, keeps the main characteristics of the data subject, and is GDPR-compliant for AI innovation and machine learning training. If you’d like to learn more about how we at brighter AI anonymize data and protect every identity in public, check out the case studies below, or contact us here.
[1] Ghasemaghaei & Galic; “Can big data improve firm decision quality? The role of data quality and data diagnosticity”;2019
[2] Gudivada, et al.; “Data Quality Considerations for Big Data and Machine Learning: Going Beyond Data Cleaning and Transformations”; 2017
[3] EU Agency for Fundamental Rights; Data quality and artificial intelligence – mitigating bias and error to protect fundamental rights; 2019
