How To Democratize Generative AI

Recently, Gartner predicted that by 2026, over 80% of enterprises will have used Generative AI (genAI) APIs and models and deployed genAI-enabled applications in production environments, up from less than 5% in early 2023. This rapid, continued adoption and use of AI technology will lead to more democratic AI.

What is Democratic AI? 

AI democratization refers to making artificial intelligence technology more accessible and available to a diverse range of users. The goal is to ensure that the benefits of AI are distributed equitably and the technology serves the greater good of society. This can involve public participation in AI governance, ethical frameworks, human-in-the-loop testing, and regulations to mitigate potential biases and discrimination.

We expect this concept to be a continued topic of discussion as AI becomes more prevalent.

How To Achieve Democratic AI

Traditionally (although traditions don’t go back too far yet), genAI platforms have been expensive to build and use. Large language models (LLMs) for genAI require tremendous computing power, hardware, and resources, making them easier and more accessible to develop and leverage for some than others. AI democratization involves negating the monopolization of AI by big tech companies and making the technology available to everyone who has a problem to solve.

Imagine your local hair salon being able to predict the number of customers on a rainy Saturday afternoon, make appointments for customers based on when they most likely next need a haircut or suggest hairstyles based on virtual images of customers. Insights such as these can be transformative for small businesses, not only in maximizing profits but also in improving customer experience and retention.

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The question is how companies can build the AI models needed to generate business-transformational insights. They first need to collect their own historical data, in addition to data from similar businesses. Easy tools, abstractions, and ready-made datasets are fundamental blocks in the journey to implementing AI. Developers also need to use the right models with little to no coding expertise, so simple user interfaces and pre-trained models will help companies train a model with minimum investments.

Taking AI to the masses involves creating accessible data sets, reusable algorithms, and access to affordable computing power. Examples of the democratization of AI include:

• Readily available open and accessible dataset repositories from Kaggle for training AI models (https://www.kaggle.com/datasets)
• Google’s Colab (https://colab.google/) allows you to run AI models on CPUs, GPUs and CPUs
• Amazon’s Sagemaker (https://aws.amazon.com/sagemaker/) to quickly build, train, and deploy Ml models

It is worth noting that the democratization of AI can also have negative effects. An ill-conceived AI model can proliferate untrustworthiness and ethical concerns. Rapidly emerging technologies also often result in widening the digital divide. By encouraging companies that prioritize digital inclusivity and accessibility, we can influence the spread of the right set of technologies. In parallel, governance and regulations play a key role in ensuring equitable access and fostering an environment of inclusivity and innovation.

AI Governance, Frameworks and Regulations

Recently, the European Union (EU) agreed on landmark AI intelligence rules, which will set regulations for the U.S. and the rest of the world in motion. These EU regulations are set to include:

• High-Risk Systems: AI systems deemed as having significant potential harm to health, safety, etc., must comply with requirements, such as fundamental rights impact assessments.
• General Purpose AI Systems and Models: These systems and models will be subject to transparency requirements, such as complying with EU copyright laws and disseminating detailed summaries of the content used for algorithm training.

The democratization of AI raises key questions when it comes to accountability and copyright.

Who is responsible for data sourced publicly?

Who is liable for the accuracy of the algorithm?

Tracing information sources and assessing liability gets complicated where multiple stakeholders are involved.

The EU has the ball rolling on AI guardrails, which will only increase from here. The hope is the continued development of these regulations will aid the goal of democratic AI while also addressing other significant concerns, such as AI accuracy and bias. Although the governance around AI is still new, understanding how data is collected and tested in genAI applications should be critical for privacy and compliance. We’ll see the continued conversation around AI governance, ethical frameworks and regulations to mitigate potential biases and discrimination that stop AI benefits from being distributed equitably.

The Role Users Play in Generative AI Trends

Proper governance is just one step towards generative AI democratization.

While governing bodies can and should create legislation that promotes ethical AI development, real people must be involved in the development process to ensure AI outputs are unbiased, accurate, and appropriate. With a human-in-the-loop approach towards AI, in which real people oversee and test AI outcomes, we ensure AI is created for the people and by the people. Giving companies the confidence that AI technology has been tested by humans for quality and ethics will help speed up its adoption and ensure we democratize the right AI.