What Challenges Does Generative AI Face with Respect to Data: Lessons Learnt From OpenAI, Google, Amazon, Apple, IBM, Meta, Tesla, and Many More

For generative AI to create accurate, useful outputs, it needs high-quality, diverse data.

Take OpenAI’s ChatGPT, for instance. Early users noticed that the AI sometimes gave answers that felt biased or overly generalized.

To improve, OpenAI implemented a feedback loop where users could rate responses, giving the model real-time input to refine its answers. They also expanded the training data and diversified the sources to improve accuracy and reduce bias.

Google faced a similar challenge with its BERT and MUM models, which power its search engine.

The solution? Constant retraining on multilingual datasets that represent users from every corner of the world.

This diversity allows Google Search to deliver relevant results for an Indian user looking up “cricket” versus an American fan researching “baseball.”

Generative AI reflects the data it’s trained on, and that data often has hidden biases.

How do we prevent these models from simply perpetuating the injustices and biases of the past?

Amazon learned this the hard way when it built a model to help with hiring. The AI, trained on historical data, began favoring male candidates because the data reflected years of hiring bias in the tech industry.

The company eventually scrapped the tool, realizing it was reinforcing existing inequalities.

Amazon’s experience became a powerful example of why AI models need to be scrutinized for hidden biases — before they’re deployed at scale.

Privacy is a constant tightrope in AI. How do we harness user data to improve AI without compromising their privacy?

Google and Apple are two giants tackling this challenge head-on.

Google uses a technique called federated learning in its mobile apps like Gboard, its popular keyboard app.

Instead of sending raw user data to central servers, Google’s model learns directly on users’ devices.

This allows Gboard to get smarter over time, refining predictions and autocorrect, all while keeping users’ keystrokes private.

Apple, known for its privacy-first stance, uses on-device processing for AI wherever possible.

Take Siri, for example. Most of Siri’s voice processing happens on your iPhone, keeping personal data secure while improving voice recognition over time.

Trust is critical when it comes to AI — especially when AI is making decisions that impact real lives.

IBM tackled this challenge with its AI Fairness 360 toolkit. The toolkit provides developers with tools to detect and mitigate bias, and make models explainable.

Over at Meta, the company behind Facebook, explainability has become a key focus in content recommendation algorithms.

Facebook allows users to see why certain posts or ads appear in their feed, fostering transparency and reducing the “black box” feeling that often surrounds AI-driven recommendations.

In doing so, Meta is addressing users’ concerns about the echo chambers created by recommendation engines.

AI models can become stale if they aren’t updated frequently. And TikTok has mastered the art of continuous learning with its recommendation engine.

TikTok’s AI constantly learns from user interactions, adjusting recommendations to reflect the latest trends.

This rapid feedback loop is crucial for the platform, where trends can come and go in a matter of hours.
Another example? Tesla’s Autopilot.

To make safe driving decisions, Autopilot continuously collects data from Tesla vehicles on the road, updating its model to handle new scenarios, road conditions, and traffic regulations.

AI impacts people’s lives — so why not let the people it affects have a say? Mozilla, known for its open-source roots, has built a community-driven approach into its AI ethics.

Mozilla’s Common Voice project is a stellar example.

Rather than relying on proprietary datasets, Mozilla invited people to donate their voices to build a more inclusive voice recognition dataset.

This approach not only makes the dataset richer but also more reflective of different accents, languages, and speech patterns.

One of the best ways to make AI smarter is to let it learn directly from users. By combining user feedback loops with LLM fine-tuning, AI systems can achieve higher levels of accuracy and personalization.

LinkedIn, for example, uses real-time feedback to continuously improve its job-matching algorithms.

Every time users take skill assessments or give feedback on job recommendations, LinkedIn uses that data to refine its AI models.

This direct feedback loop allows LinkedIn to offer more accurate job recommendations and skill matches.

YouTube has a similar system in place. YouTube’s content moderation AI learns from users’ flagging of inappropriate content.

These reports are reviewed, and problematic content is used to train the AI to better detect similar issues in the future.