The Definitive Handbook on Making AI Accessible to the Masses

The aim of democratizing Artificial Intelligence is to make it accessible to everyone, regardless of their background or expertise. Major corporations like Microsoft and Google provide open-source datasets and tools to the public, making it possible for anyone to become a “citizen data scientist” and develop innovative AI applications, without the need for specialized knowledge in the field.

In this essay, we will extensively examine the significance of democratizing artificial intelligence (AI), highlighting the critical aspects of AI that need to be democratized, the process of democratizing them, and the recommended democratization framework. Furthermore, we will explore the consequences of AI democratization and the potential advantages it presents. Understanding the necessity of democratizing AI is crucial to ensuring its potential is equitably realized.

Examples of AI being made more accessible to the general public

  • Kaggle hosts a vast library of datasets that are publicly available to access. Users can conveniently download a new dataset from the internet and begin using it to train their models. Similarly, Google Cloud Platform (GCP) allows users to create and train an image classifier without requiring any coding.

    To train a model using Google Cloud Platform (GCP), users must provide an adequate number of labelled examples from each class and initiate the process by clicking the appropriate button. GCP will then use the most effective AI algorithm available to classify the images.
  • For developing advanced neural network models in the cloud, Google Colab is an open-source solution that utilises powerful graphics processing units (GPUs). Although high-performance hardware such as Nvidia GPUs is critical for training deep learning models, not all local systems can access such resources. To address this, Google offers Colab, granting individuals free access to their hardware via a Gmail account, making it possible for them to generate AI models.

    Colab Pro users can benefit from increased RAM and reduced training time for models. Additionally, users can integrate the trained model with their custom software. For instance, Colab users can train a convolutional neural network (CNN) model, save it, and then incorporate it into their local system’s Flask backend to use it as a blood cell classification picture classifier.

The benefits of democratizing AI for all

Removing Barriers to Entry

The advancement of Artificial Intelligence (AI) technology has made it more attainable to individuals and businesses. By leveraging cloud computing and open-source datasets, data science education has become less costly and accessible to people worldwide. To deepen their expertise in the field, individuals can participate in data hacking competitions and other such events.

Minimizing Costs

Democratizing Artificial Intelligence (AI) technologies presents an opportunity to decrease the expense of developing AI solutions. To create versatile and dependable AI solutions that can be used in various applications, companies are increasingly utilising open-source data, algorithms, and models that are hosted on cloud-based platforms. This approach enables organizations to access the most recent AI technologies without the need for costly hardware investments.

Generating Dependable Models

By leveraging tools such as transformers, TensorFlow, PyTorch, and ImageNet, model construction can be accelerated while maintaining accuracy, saving valuable time that would otherwise be spent on training new personnel. For example, the transformers library enables the selection and training of a natural language processing (NLP) model using a custom dataset designed for a specific application. Google’s Bidirectional Encoder Representations from Transformers (BERT) can be used to train a custom model and has been proven to outperform traditional methods in intention identification.

Emotion Analysis

The adoption of Artificial Intelligence technologies has been rapid and widespread due to its advantageous properties. Many websites now incorporate chatbots to handle common inquiries from visitors. Additionally, sentiment analysis, a form of Natural Language Processing, has become a pervasive practice. Companies can employ this method to understand the products and services their customers value most. Sentiment analysis is a technique used in text classification to determine the positive, negative or neutral tone of a text.

Detecting Discriminatory Language

To safeguard individuals from the dangers of cyberbullying, Artificial Intelligence (AI) is being employed to recognise hate speech on social media platforms. AI has reached a stage of sophistication where it can comprehend the meaning of language and differentiate between subtle nuances. This helps identify potential cases of cyberbullying, allowing actions to be taken to protect potential victims.

What Elements are Incompatible with a Democratic Society?

Identifying which components of an AI product should be made available to the public ahead of its launch is of utmost importance. In order of complexity, these five areas are: Market, Model Creation, Algorithms, Data Processing and Data Storage. Each of these elements necessitates meticulous consideration and planning to guarantee the successful deployment of the AI product to the public.


Data is an all-encompassing term utilised to denote substantial volumes of information used to inform and guide vital business decisions. This data can encompass structured data, such as rows and columns of numerical information presented in a tabular format, or it can also take the form of semi-structured or unstructured data, comprising of images, videos, audio files, text and emoticons.

Data democratization refers to the process of granting access to data for everyone, irrespective of their technical expertise or financial resources. Examples of such include Kaggle datasets and those made available on GitHub, including Prajna Bhandary’s mask detection dataset. This has enabled users to access high-quality data visualization tools without requiring expensive software or services. With the help of these tools, users can now analyze and visualize their data in a more comprehensive manner, leading to better insights derived from the data.

Data Handling and Processing

To efficiently store and process data, a growing number of organizations are opting for cloud-based services such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). These services offer a “pay as you go” payment model, giving users access to diverse resources, such as CPUs and GPUs, databases, and storage for uploaded datasets. It is crucial that users possess the necessary credentials to fully utilize these services.


The employment of machine learning techniques, such as decision trees, support vector machines, and artificial intelligence algorithms like BERT, CNN, RNNs and LSTMs, is now accessible to all. This provides users with a range of alternatives to choose from, depending on their requirements. However, it is essential to acknowledge that a fundamental understanding of computer science, mathematics, and statistics is necessary to utilize these technologies effectively.

Researchers have recently shared their latest Artificial Intelligence (AI) algorithms on the open-source platform, GitHub. It is feasible to craft personalized algorithms for specific use-cases in either a local environment or in the cloud. Hosting the algorithms in the cloud offers various perks such as access to a considerable number of graphics processing units (GPUs) and the ability for users to easily collaborate by sharing the program’s direct connection. Moreover, this system is available for anyone who wants to make use of it.

Model Creation

To ensure the successful development of AI products, model training is an indispensable prerequisite. Automated Machine Learning (AutoML) offers an excellent illustration of how model creation can be made accessible to a wider audience. AutoML scrutinizes a dataset and runs various algorithms to determine the one that performs most effectively. Nevertheless, developers who employ AutoML should receive adequate training to ensure a strong model. It is also crucial to comprehend the deterministic results generated by the model.

In a machine learning system, such as an AI image classification model, it is plausible that healthy samples may be labelled inaccurately as infected. This gives rise to the question of what actions ought to be taken if results are produced that do not correspond to any of the expected categories when a scan of another disease is entered. A prime example of this is facial recognition technology, where it is unclear how the system will classify an individual who is not included in its training set. To ensure the accuracy of the AI system, it is crucial that the developer possesses the confidence to address such concerns.

It is imperative that the developed model is devoid of any form of prejudice. Unfortunately, we are susceptible to generating unintentionally biased datasets due to our personal biases. This bias is typically evident when a dataset is excessively dominated by a particular demographic, such as when there are more males than females or more individuals of a specific skin colour. Another instance of this bias can be observed in datasets used for facial recognition that have a greater representation of white surgical masks than colourful ones. It is unavoidable that any model trained on such skewed data will exhibit the same biases, which is why it is crucial to address and prevent these issues to the fullest extent possible.


On the opposite end of the spectrum is the idea of a data science or artificial intelligence (AI) marketplace, where algorithms and models can be bought and sold. Kaggle is the primary example of a platform that holds competitions to identify the most effective model, with the chance to win attractive prizes. Nevertheless, these exchanges are subjected to their own limitations resulting from the inappropriate usage of the available data, algorithms, or models.

Approaches for Enhancing Accessibility to AI-Relevant Data

In the following sections, we will discuss the four primary principles of democratizing AI.

Enable Accessibility at a Reasonable Cost

To foster the creation and application of artificial intelligence, it is crucial to guarantee the affordability of data, algorithms, storage, model creation, and the market. One approach to achieve this is by offering open access to datasets on platforms like Kaggle. Furthermore, there are multiple AI algorithms accessible in the GitHub repositories that can be leveraged at no cost.

Offering these datasets and algorithms for free is not possible. Nevertheless, they must be priced reasonably to ensure the wider availability to people. Charging users exorbitant prices reaching thousands of dollars to access datasets or algorithms is unjustifiable and counteracts the goal of “democratizing AI” by making it inaccessible to the majority.

Attain Abstraction

The second stage in the process is to guarantee that data is abstracted in a manner that people with limited knowledge of SQL queries or complex terminal commands can utilize it. To ensure the feasibility of democratizing Artificial Intelligence for all, it is crucial to verify that the necessary components for AI development can be obtained from the company that supports this mission. To accomplish this, it is vital to minimize the amount of coding required to access the data.

Permit Manipulation of Stack Items

The third phase in the process is to achieve proficiency in the individual components of the technology stack. Users should have the capability to make informed judgments concerning which elements of the stack to use, when to deploy them, and how to leverage the results.

With the pre-installed packages available on Google Colab, users do not need to install any new software to start developing models. The platform’s efficient hardware support, such as GPUs and RAM, makes it an optimal choice for creating Artificial Intelligence (AI) models, especially complex neural networks. Additionally, users may effortlessly capture screenshots of the model’s classification reports or download it as an h5 file for use in their own applications. Limiting access to such control over AI models can have an adverse consequence on the decentralization of AI.

Validate Ownership Claims

Reviewing the ownership status is the fourth phase. Here are a few questions that must be answered:

  • Who rightfully owns the data?
  • Do those who produce the data or those who analyze it and draw conclusions own the data?
  • Is it owned by a single proprietor, or can it be jointly owned by multiple individuals?

It is crucial for all four stakeholders participating in the democratization of Artificial Intelligence (AI) — users and the companies facilitating this process — to consider the cost implications. If two datasets are accessible for the same problem statement, but one is priced at $250 while the other costs $10 (or is free), customers will typically choose the lower-priced option (Source-B).

Numerous companies have embraced the approach of offering datasets for free initially, followed by charging customers for their use in order to increase their business’s success. To guarantee maximum customer satisfaction and loyalty, it is essential for companies to be transparent about their data policy and the steps taken to prevent the misuse or abuse of the datasets. By acting transparently, businesses are more prone to gain their customers’ faith and obtain a long-term competitive edge.

Over the past few years, there has been growing concern regarding the misuse of Artificial Intelligence (AI) models that includes misplaced algorithms and miscomprehended mathematical outcomes. As a result, it is crucial to educate those who casually utilize data on the correct methods of collecting and distributing information.

Moreover, it is crucial to provide expert users with an explanation of the mathematical principles that support the outputs of the different AI models. To enable the efficient utilization of the latest AI components, it is imperative not only to make the data, models, and algorithms available online but also to furnish a comprehensive guide that illustrates how to optimally use them. Such a strategy would ensure that individuals have thorough knowledge in effectively utilizing the technology.

A System to Promote Democracy

AI professionals, encompassing developers, testers, and maintainers, must possess a comprehensive comprehension of the industry and exhibit a profound commitment to ethical AI. Additionally, their knowledge in the sector should be unrivaled. To avoid potential misapplication, misuse, bias, and other concerns associated with AI, industry leaders should put in place training, governance, and IP management approaches, as well as open-sourcing initiatives.


To ensure secure utilization of AI, users must undergo adequate data science training. One example is to correctly split a given dataset into training, testing, and validation sets. This will enable users to acquire the necessary skills to ensure the safe use of AI and proper handling of data.

A detailed examination of the diagram below shows the division of the provided image dataset into training, testing, and validation sets. Initially, we allocated 80% of the dataset for training while the remaining 20% was reserved as a test set. Afterwards, the training dataset was split further into a validation set (20%) and a training set (80%). This division ratio is based on the traditional 80-20% split.

The validation dataset is utilized to evaluate the performance of the model on new, unseen data. To start, the model is trained with the training set data. Afterward, the model is assessed on the validation dataset to determine its accuracy. Lastly, the model is tested on the test dataset to validate its predictive capacity. This model evaluation process is known as the “validation set approach”.

Dividing a dataset into two distinct groups is a common technique for training and testing models. By assigning the first half of the dataset to training and the second half to testing, only one simple step is necessary to adequately split the two groups. Failure to do so may result in an ill-fitting model that produces inaccurate results.

Leadership and Oversight

Having a clear comprehension of who owns the data, who holds authority over it, and who holds the rights to any inferences derived from it is critical. The development of Shadow AI using unmanaged data by teams outside the organization responsible for data quality is troubling. If data that was originally gathered to create a distinct AI model becomes open-source, it is vital to use AI/ML models that are trained with correctly monitored, secured, and comprehended data.

To achieve successful model development, it is important to possess relevant validation metrics, such as accuracy, and to be capable of explaining the findings. It is also crucial to detect and remove any biased models before creating and implementing them on the cloud. Additionally, models that generate inscrutable or non-deterministic outcomes should be avoided. By adhering to these governance principles, organizations can ensure the development and deployment of trustworthy and dependable models.

IPR stands for Intellectual Property Rights

Democratizing ownership of the intellectual property (IP) rights connected to Artificial Intelligence (AI) components is crucial in ensuring equitable access to digital services. Despite the possibility of anonymous processing of sensitive data, many enterprises are hesitant to adopt cloud-based picture and audio processing due to the uncertainty of data ownership. Cloud services and other technological advancements can certainly help democratize data ownership, but the real drive for democratization comes from establishing unequivocal ownership protocols for digital assets.


Companies dedicated to democratizing AI should guarantee that their clients are at liberty to replicate, customize, and distribute their software and source code to serve their own purposes. To elaborate, to make AI more accessible, it must be open-sourced in a way that does not put users’ private data, proprietary information, or market dynamics at risk.

Thanks to recent democratization efforts in Artificial Intelligence (AI), anyone can now attempt AI development tasks, leading to a considerable reduction in required time and funding to include support for GPUs. However, with the increasing availability of AI components on the internet, there is an elevated threat of harmful exploitation of these models. To tackle this challenge, following a democratization framework is advisable. This approach enables us to address this problem more effectively.

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