Everything You
Need To Know
About Datasets

In the gold rush of AI adoption, companies are burning millions on a fatal assumption: Any dataset will do. 

But here’s the reality: Poor data is the silent killer of many AI models. In this whitepaper, we cut through the hype to reveal what leaders wish they’d known about datasets before they made costly missteps. 

Our experienced team also outlines how to make sure you’re using well-sourced, ethical datasets so you can get your approach to AI right and net a competitive advantage.

Because in today’s market, you only get one chance to create the clean datasets you need to meaningfully power your AI efforts. Let’s make it count!

Everything You NEED To Know About Datasets: How Not To Waste Your One Chance at ‘Getting AI Right’

It’s a data-driven world out there. 

AI is a game changer for every industry, but whenever a fast-evolving new technology launches, it’s not just about launching first; it’s about doing it correctly, and protecting your business every step of the way.

Datasets are the lifeblood of relevant, effective AI. But you only get one chance to create the clean datasets you need to power your AI efforts. A miss here means lengthy (and expensive) AI overhauls in the future or even potential legal and regulatory action.

Luckily, backed by our team’s experience in powering AI with ethical & clean voice and video dataset inventory, with fully documented chains of consent, there is a better way.

It all starts with understanding exactly why datasets are so crucial to your AI endeavors.

CONTENTS

  1. What Are Datasets? Why Do They Matter?
  2. The Risks of a Poorly Curated Dataset
  3. Trending: Why Datasets Are Dominating Headlines
  4. Ethical Datasets: The Smart Way To Use AI
  5. Why Should Your Business Care About Ethical Datasets?
  6. How Proprietary, Ethical Datasets From Voices Offer a Competitive Advantage
  7. Power Your Business Into the AI Future the Right Way

1. What Are Datasets? Why Do They Matter?

Datasets are structured collections of information used to train machine learning models. 

Imagine them as a teacher, giving the AI examples to help it ‘learn’ the patterns it needs. A dataset may be a collection of focused reports, thousands of images, language pairs for translation, or even a collection of naturally spoken voice data like the ones we offer.

There are many types of datasets out there, but they fall into three main categories:

Structured

Organized tables/matrices (e.g., business reports)

Unstructured

Raw data (e.g., audio files for speech recognition)

Semi-Structured

Mixed formats (e.g., product listings with text and images)

Today, you’ll find three primary sources for these datasets:

Internal

Company-specific data (e.g., customer interactions, sensor readings)

Public

Popular benchmarks (e.g., ImageNet, MNIST, CommonCrawl)

Purchased/ Licensed

Specific, focused datasets from specialized data providers (e.g., medical imaging data, financial data, or our voice collections)

These datasets are the powerhouses behind evolving AI technologies like natural language processing, computer vision, predictive analytics, and recommendation systems. But note that the quality of the underlying dataset is critical to how the AI performs.

What Makes a High-Quality AI Training Dataset?

For a dataset to be clean, ethical, and fit for purpose, it needs to meet specific criteria. 

High-quality AI training datasets must have the following:

  • Balanced Representation: No biased distributions

  • Scale: Sufficient examples to learn patterns correctly

  • Diversity: Covers edge cases and variations as well as core data

  • Clean Labeling: Accurate ground truth annotations

  • Ethical Collection: Respects privacy and consent

  • Documentation: Clear data cards and limitations

  • Format Consistency: Standardized preprocessing

  • Validation Splits: Separate test sets for evaluation

How well a dataset benchmarks against these standards will impact the model’s performance and, more importantly, its trustworthiness.

2. The Risks of a Poorly Curated Dataset

Without a great dataset, there is no trustworthy AI. 

The launch (and success) of Large Language Models, like ChatGPT from OpenAI, has highlighted AI’s potential. However, as with any evolving technology, the frontrunners also teach us valuable lessons about what can go wrong.

Dustbin

Garbage In, Garbage Out: It’s Not Just a Cliché

‘AI hallucination’ is where false or misleading information appears plausible when in fact it’s anything but.

You may have heard of issues like:

  • Making up citations and even entirely non-existent research papers

  • Inventing non-existent events

  • Creating false but convincing explanations

  • Generating fake statistics or data

  • Blending real and imaginary details

AI is like a puppy or toddler; it can’t say no to your requests and always provides an answer to ‘please’ you. Nor does AI truly understand the data it is trained on. It makes educated guesses, but it can’t tell the truth from fiction.

An AI model simply predicts the likely following content based on the prompt, combining its learned patterns in new ways. This can accidently create coherent, but false, information. This risk is amplified when the underlying training data is poorly sourced, contains errors or biases, or is simply lacking.

While these errors seem innocuous, they can cause serious downstream issues. Especially when people don’t understand how vulnerable the underlying dataset is. 

Common problems include:

  • Misinformation

  • Academic integrity issues

  • Spreading convincing but fake ‘facts’

  • Decision-making errors from faulty data

  • Accidental leakage of sensitive data

There is also the concept of ‘data drift’ to address, where the AI model starts to deviate from what it learned. This requires retraining and further data input, which can be difficult to manage when you have no control over the dataset contents and no access to the original talent.

Person with pen

The Long Arm of the Law

Training AI models on unethical, or improperly sourced, datasets can also create several significant legal vulnerabilities for companies:

  • Copyright Infringement: Using copyrighted materials without proper licensing or fair use justification can lead to lawsuits, as demonstrated by recent cases against AI companies by authors and artists.

  • Privacy Law Violations: Training on personal data without consent may violate privacy regulations, resulting in substantial fines and mandatory data deletion.

  • Bias-Related Liability:Using biased training data leads to discriminatory outputs and could even violate civil rights laws.

  • Breach of Contract:Using data in violation of terms of service or licensing agreements can result in litigation.

  • Securities Law Issues:Public companies may face SEC scrutiny or shareholder lawsuits for inadequate disclosure of training data risks or misrepresenting data sourcing practices.

  • Trade Secret Misappropriation:Training on confidential or proprietary information could trigger trade secret litigation, especially if the data was compiled (scraped) without authorization.

  • FTC Enforcement:The Federal Trade Commission has signaled its interest in pursuing companies for deceptive practices related to AI training data sources and usage.

3. Trending: Why Datasets Are Dominating Headlines

“This backlash against using personal data has arisen where people are realizing this is taking a lot of important information. It’s impacting their privacy rights, taking their persona without their permission. So there’s been much focus on the best way to source data that will be a wide enough dataset that it’s useful and can build a powerful AI tool, but that it’s not going to raise these ethical and legal questions about misappropriating people’s IP or personal/privacy rights to, essentially, profit off of what people have made before.”

– Samantha Rothaus, partner at Davis+Gilbert, LLP

Using scraped data and open-source datasets can amplify any lack of quality control and the risks associated with it.

You have no control over where the data comes from, the legalities that may have been bypassed to create it, or the accuracy and fairness of its contents. You also don’t know whether there are any issues regarding consent to use that data.

All of this isn’t great if you’re using AI to boost your brand while still maintaining high brand value and trustworthiness for your clients. No wonder concerns about datasets are trending. AI is only practical in business where people trust it, and unethical data collection and blatant biases in the algorithms destroy that trust.

Data scraping, in particular, is a massive issue for dataset creation.

Software ‘scrapes,’ or extracts data from… well, anywhere it pleases, including websites, social media, other brand products and services, academic and news sources, and so on. Permission, quality, accuracy, and copyright fall by the wayside.

That’s why we’re now seeing data-use controversies dominating headlines, especially regarding:

  • Legal battles over copyright and fair use

  • Questions about consent and privacy

  • Debates over ownership of scraped content and dataset documentation

  • Fears about the cost of quality data curation

This creates a host of ethical concerns, as well as legal gray areas, including:

  • Privacy implications,

  • Bias detection and removal,

  • Content creators not being compensated,

  • Accidental (and bad actor) creation and misrepresentation of personas that emulate real individuals, 

  • Concerns around claims of false advertising,

  • Potential misuse of personal data,

  • Competitive disadvantages,

  • Varying and inconsistent laws,

  • Strong copyright implications,

  • Terms of service conflicts,

  • Personal data protection clashes, and

  • Fair use debates.

AI

Where LLMs and Datasets Intersect

LLMs are very much the ‘public face’ of AI. 

These are AI systems trained on massive text collections to understand and generate human-like language, e.g., ChatGPT. Newer models are also evolving to include voice-based interactions, such as AI-generated voice overs and voice applications for smart home devices, advertising, conversational AI, and more.

Training LLMs for both text and voice-based applications requires enormous collections of data, but quality still matters more than pure quantity. Clean, diverse, high-quality data improves results and reduces hallucinations and drift. The future of LLMs therefore depends heavily on solving key dataset challenges while maintaining ethical standards.

As Dheeraj Jalali, Voices Chief Technology Officer, notes, ethical datasets “…are pivotal in empowering large language models to serve Fortune 1000 companies with unprecedented accuracy and relevance. By providing diverse, high-quality data, we enable these models to understand nuanced language, culture, and industry-specific contexts. This helps businesses make informed decisions, enhance customer experiences, and drive innovation.”

If you’re curious to learn more about how LLMs and datasets are revolutionizing the voice over and voice tech industry, you can find out more in Jalali’s detailed guide.

Key Dataset Trends to Know

Another reason datasets are dominating headlines is because they represent a significant and growing market share.

In 2022, the global AI dataset market stood at 1.73 billion US dollars. That expanded to US$2.45 billion in 2023 (or possibly US$3.91 billion, sources differ) and is anticipated to reach between US$11.75 billion to US$27.38 billion by 2032, with projected Compound Annual Growth Rates (CAGRs) of 17%-24%. 

A key driver is the fact that so-called ‘Big Data’ increasingly depends on AI to handle high-level abstract concepts through data extraction and analysis.

Because of this value, the AI industry is focused on strategies to generate massive datasets in ways that are fair, ethical, and ‘respect the human.’

Other current trends in the dataset space include:

  • Synthetic data generation

  • Multi-modal training (text, images, code)

  • Specialized datasets

  • Human feedback incorporation

  • Constitutional training approaches

4. Ethical Datasets: The Smart Way To Use AI

“In the age of artificial intelligence, the voices that power our technology hold immense potential. At Voices, we understand that this potential hinges on the foundation of ethical datasets. Responsible data collection and use are not just checkboxes — they’re the cornerstones of trust and innovation. By prioritizing ethical considerations throughout the data lifecycle, we ensure that the voices shaping our future empower a world where technology reflects the best of humanity.” 

– Jay O’Connor, Voices CEO.

For many companies, the sentiments highlighted in O’Connor’s quote above reflect a growing business reality: Demand for ethically sourced, consent-based datasets is increasing, along with a push for more efficient training using smaller, higher-quality datasets.

These ethics should be present from the start, not applied only after the law comes knocking on your door. The cost of replacing shoddy datasets and existing AI models, not to mention the loss of consumer trust, is simply too high otherwise.

Remember: Ethical datasets are those gathered, maintained, and used in ways that respect privacy, fairness, and human rights. Any ethical dataset should meet Voices ‘Three Cs’ rule: consent, compensation, and control, ensuring:

Privacy and Consent:

  • Explicit user permission for data collection

  • Proper anonymization techniques

  • Secure storage and handling

  • The ‘right to be forgotten’ and to withdraw contributed data should its final use conflict with the data provider’s comfort or ethics.

Bias and Representation:

  • Diverse demographic inclusion

  • Fair representation of all groups

  • Avoiding historical prejudices

  • Employing cultural sensitivity and correct use of language and cultural frameworks

Transparency and Accountability:

  • Clear documentation of sources and permissions with full disclosure of intended use to participants and voice actors

  • Accessible data lineage

  • Regular auditing processes

  • Published limitations

  • Disclosing the use of AI within promotional activities

Together, this helps to address many of the challenges we’ve identified by:

  • Balancing utility with privacy

  • Maintaining data quality

  • Keeping up with regulations

  • Meeting the cost of ethical data collection

  • Handling bias

  • Managing consent at scale

  • Ensuring cross-border compliance

  • Evolving ethical standards

  • Offering content that is representative and well-understood by the target audience

Using these ethically sourced datasets also helps companies stay compliant with evolving AI regulations, including the EU’s GDPR, California’s CCPA, and industry-specific guidelines like HIPAA as well as finance regulations. It also helps demonstrate to end users that this isn’t just a tool that’s been rushed to market to take advantage of a trend, but rather an ethical, genuine product you can stand behind and be proud of.

5. Why Should Your Business Care About Ethical Datasets?

“You can do (almost anything), but the real question is: should you? Does that represent your brand? Does it protect what your brand is, your reputation? It’s not just a question of money and innovation, it needs to be tempered by other considerations.”

– Oita Coleman, Open Voice TrustMark Initiative

As AI systems become prevalent in business and decision-making, these considerations are more important than ever. AI might be ‘the next big thing,’ but what does using it (and how you use it) communicate to your clients?

Customers are increasingly demanding data transparency, and stakeholders value responsible AI practices, too. You’ve spent years building long-term brand loyalty, but all it could take is one low-quality AI advert, or one sniff of controversy around the training data used, to pull it all down. Not to mention AI “junk” eating up your own ad budget on false premises. 

Fortunately, those issues are all avoidable if you’re sourcing your data smartly.

The Evolving Legal Landscape

Using curated, ethical datasets prevents your business from being the test case in the looming legal battles surrounding AI, prevents PR disasters, and protects you against discrimination and compensation claims. 

They also reduce regulatory scrutiny, keep you aligned with sustainability goals and support community values. Using an ethical AI dataset also ensures better data quality and accuracy, reduces bias, and offers more reliable real-world performance and long-term results. 

For example: 

  • Stronger customer relationships

  • Full, non-ambiguous consent from contributing parties

  • Clear ownership of data and usage rights

  • Ethical and legal use of voice or other creative works

  • Reduced legal exposure

  • Better AI performance

  • Positive brand reputation and competitive advantages

  • Defendable IP with no ambiguity on use rights or the training data used

In other words, ethical datasets, responsible use, and careful curation are fast becoming the separators between leaders and laggards in AI usage. 

As Colin McIlveen, our Vice President of Customer Operations, notes: “Demand for high-quality datasets has surged dramatically over the past year, driven by the rapid advancement of AI and machine learning technologies. As these technologies mature, we anticipate an unwavering demand from the world’s largest tech companies for diverse and comprehensive datasets to fuel their innovative projects and drive groundbreaking advancements.”

6. How Proprietary, Ethical Datasets From Voices Offer a Competitive Advantage

A curated, ethical voice dataset like those we offer gives considerable advantages. 

For starters, by using professionally recorded voice talent who have explicitly consented to AI training, you substantially reduce the legal and reputational risks your company might face. You also get the benefit of a pre-made, thoughtful AI use policy.

And because these datasets are recorded with standardization, in specific environments as requested by the client, they ensure consistent, high-quality audio as well; eliminating the technical issues common in scraped data, such as background noise or varying audio quality. Which in turn translates into more accurate and reliable AI performance.

The diverse talent pool available through Voices also means your company can develop more inclusive AI systems, ones that accurately represent different accents, languages, and speech patterns. This helps avoid bias issues that could affect global market penetration.

A diverse talent pool also lets brands stay true to their own ethics and voice, by giving them access to a wealth of talent across all walks of life to match their brand persona and tone. 

Another benefit of using ethical voice datasets is that they show your company’s commitment to ethical AI development. This can positively impact investor relations and customer trust and save your brand from poor PR related to mistakes, ‘stolen’ voices, and unhappy talent.

Instead, you help reinforce and bolster the talent that helps bring these datasets to life, while still placing your company at the cutting edge of this exciting, evolving technology.

Lastly, but critically, having clear data provenance and usage rights documentation simplifies compliance and helps companies meet emerging AI governance requirements.

Mic

7. Power Your Business Into the AI Future the Right Way

By taking a proactive approach to ethical AI datasets, you not only gain a competitive advantage, you also avoid the high monetary and legal costs of replacing unethical AI models down the line. 

At the same time, ethical AI empowers your brand with a fresh, unique, voice. With full consent and documentation, you also gain the peace of mind of knowing you retain your IP protections, even though the voice you use may be AI generated and not that of specific talent. And getting there? It’s as easy as reaching out to us.

We’d be happy to walk you through exactly how our ethically sourced data and white glove dataset services can help you boost your brand. Our datasets are backed by one of the largest communities of global contributors and with access to hundreds of dialects, languages, and styles. All while empowering your company to be one of the leaders in shaping a better, more trustworthy AI-powered future.

Voices would like to extend a hearty thanks to Oita Coleman of the OpenVoice TrustMark Initiative and Samantha Rothaus from Davis+Gilbert, LLP, as well as the much-valued experts on the Voices leadership team whose insight helped to shape this whitepaper.