Is Data — the Lifeblood of AI — Facing a Critical Scarcity?

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AI relies heavily on ample human-generated data, but this resource is finite. AI needs to overcome data scarcity — caused by privacy laws or simply poorly formatted or inaccessible data — with synthetic data as one of the options.

Artificial Intelligence (AI) has been staring at a scarcity of data for a while — and there’s good reason to think this might impede AI’s progress.

Various factors, such as legal and privacy concerns, data quality, acquisition costs, and compatibility, have been responsible for the data available for AI training models beginning to dry up.

Data is the new currency, but it may be lean in some areas. AI can help in medical diagnosis, but its uses become limited if information about rare diseases is locked up in private medical records.

Similarly, in the finance world, if AI cannot access up-to-the-minute financial information, predictive models will be hampered.

Data Locked Behind Privacy Laws

Meanwhile, governments have been making laws stricter to prevent wrongful acquisition and misuse of data, with the General Data Protection Regulation (GDPR) in the EU being one of the most prominent examples.

AI, particularly ChatGPT, has faced significant resistance in the European Union. Italy took a strong stance, accusing OpenAI of violating privacy rules around the Italian Data Protection Authority (GPDP) and mandating corrective actions, limiting OpenAI’s activities in the country.

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While other EU nations are not as stringent, Spain, Germany, and France have initiated investigations into OpenAI, signaling a potential limitation on data collection.

Companies have been forced to look at other data sources but risk acquiring poor-quality data.

The quality of data has been a persistent challenge for machine learning (ML) in particular.

Poorly labeled data, bias in data, inconsistent formatting, and redundant information hamper ML processes, leading to subpar output. The investment required to process such data is substantial, posing challenges for organizations.

Facing the reality of data scarcity, AI must chart a course forward. Here are key strategies:

  1. Cooperation with Regulatory Bodies: AI corporations must align with regulatory bodies to ensure responsible and legal data collection. Transparency and commitment to data privacy are paramount.
  2. Clear Data Collection Policies: Establishing clear and consistent data collection policies modeled on existing regulations, such as GDPR, is crucial. Declarations of data type, intended use, and transparency in the collection process are essential components.
  3. Generative AI as a Solution: In overcoming data scarcity, generative AI emerges as a viable solution. By generating unique datasets based on textual, image, audio, and video inputs, Generative AI can provide high-quality data for training ML applications. This approach minimizes legal and ethical concerns, although ongoing assessment of its capabilities is necessary.

If real-world data is limited and hard to come by — be it through poor formatting, gaps in the data, or various countries’ data protection rules stopping data ingesting over privacy concerns- generative AI may be one of the cures.

By 2024, Gartner predicts 60% of data for AI will be synthetic — up from 1% in 2021.

Gartner suggests that “relieving the burden of obtaining real-world data [allows] machine learning models to be trained effectively”.

READ MORE: The Best AI Image Generators

The Bottom Line

Navigating the complex landscape of regulatory frameworks requires concerted efforts from AI corporations.

While compliance is challenging, Generative AI presents a promising avenue to address data scarcity and ensure the continued advancement of AI technology.

By refining and enhancing Generative AI capabilities, corporations can independently generate high-quality data, offering a pathway forward that minimizes scrutiny and maximizes progress.

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Kaushik Pal
Technology writer
Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…