Democratizing AI is an essential step towards making artificial intelligence accessible to a broader audience, including individuals and organizations that lack specialized knowledge and resources in the field of AI. The democratization of AI has the potential to unlock innovation and drive economic growth by enabling individuals and organizations to develop and implement AI solutions that can solve real-world problems. Moreover, democratizing AI is crucial to ensure that the benefits of AI are not limited to a small group of experts, but rather are accessible to a wider audience, including those who might not have the expertise to develop AI solutions themselves.
To achieve this goal, it is necessary to make AI more transparent and accessible to non-experts. This can be accomplished by providing education and training on AI, promoting open-source AI tools and platforms, making AI solutions available on cloud-based platforms, creating AI tools that automate complex tasks, and encouraging collaboration between experts and non-experts. By democratizing AI, we can create a more inclusive and equitable society where everyone has the opportunity to benefit from the transformative power of AI.
Democratizing AI will make the technology accessible to a larger number of people. You may wonder whether that is necessary, and the answer is yes. Cast your mind back a few decades when computers used to be something reserved for experts only. At the time, very few users were able to use the machines and benefit from their powers.
As operating systems simplified the use of computers and personal computers found their way on (almost) every desk, companies reaped the benefits of greater efficiencies and increased productivity. Democratizing AI can achieve the same if not a greater effect. The transition has already started as AI is using techniques like natural language processing (NLP), including audio processing and the workings of neural networks to improve its understanding of human speech and the intentions behind it.
Democratizing AI further will remove barriers to use and allow global economies and humanity as a whole to take another huge step forward.
When we talk about democratizing AI, it is easy to drift off into abstract contemplation of the concept. Considering concrete aspects of AI makes it easier to imagine how the transition could play out in real life.
Some of those key aspects to consider include:
Data
Storage and computing
Algorithms
Model development
Marketplace
Democratizing Data
Training AI applications and machine learning models as well as algorithms requires huge amounts of data. Applications and algorithms use unstructured data such as videos and images and structured data like tables to recognize patterns and test scenarios.
Just a few years ago, only a handful of companies had access to large enough datasets and the computational power to use them. Google, for example, secured access to sizable datasets when it purchased the AI community Kaggle. Other datasets in source code are being shared publicly on platforms like GitHub. One example is Prajna Bhandary’s mask detection dataset. These sets allow more users to access them and develop AI apps than ever before. Google Cloud Platform is another example of AI-powered platforms that can be used to build image classifiers.
Democratizing Storage and Computing
Cloud storage and cloud computing options have undoubtedly democratized AI by making resources widely available on a subscription basis. Cloud solution providers like Amazon Web Services (AWS) are allowing developers to build and deploy AI models for others to test. These systems are hardware agnostic, facilitating widespread access.
Cloud-based solutions like these reduce the need for powerful hardware ownership by providing access to central processing units (CPUs) and graphics processing units. However, whilst these computing platforms are facilitating AI development, using them effectively continues to require specialist knowledge and certifications.
Democratizing AI and entire machine learning algorithms means making them accessible to other developers. Right now, researchers are uploading and sharing their algorithms in GitHub source code repositories. Theoretically, anyone can access those systems. In practice, though, users need a certain degree of mathematical, statistical, and computer science knowledge to use these algorithms efficiently. Without a firm grasp of the technology behind the application, users may be unable to spot erroneous outcomes.
Democratizing the AI Model Development Process
Creating a working AI product requires training a model that consistently returns correct results. Developing this sort of model requires access to different algorithms which are run over a dataset to see which one delivers the most reliable performance. AutoML can do that kind of ‘legwork’ for developers.
But the technology still relies on the developers themselves to interpret the outputs and determine which of them is correct. Let us assume a developer is training a facial recognition AI. To launch the app with confidence, the developer needs to be sure how the AI will classify an unknown face. AutoML can help with that, but developers must still ensure correct outcomes and take care to remove biased datasets from their training data. Bias could enter the data if the algorithm was trained using more male than female images, for example.
Democratizing the AI Marketplace
As for most other products, there is a marketplace for models, data, and algorithms. Kaggle led the industry in showing how the market for models could be democratized. The community held contests to find the best models. Cash prizes kept developers interested in participating.
Despite the exciting prospect of a more open marketplace, several major obstacles remain. The sheer number of deep learning frameworks is one of them. Source framework standardization will make ML more accessible and lead to a consolidation of current technology vendors.
Aside from non-standardized deep learning frameworks, limitations to the democratization of marketplaces include the risk of misinterpretation of results. This could lead to faulty applications of models, data, and algorithms provided.
Whom Do You Democratize AI for?
Put simply, everyone. But let us take a closer look. Democratizing AI means making more technology available to a larger group of your employees, ideally across the entire organization. Just like PCs are no longer reserved for a few senior managers, AI is no longer just the remit of elite professionals.
Scaling AI across the business and making the technology available to larger groups of your employees can dramatically increase productivity throughout entire companies. Rather than limiting technology to analytics and data science teams, working with AI and ML will become part of the majority of the jobs your business offers. Granted, some of this rollout will be limited by the type of industry you operate in and your line of business. However, most sectors will benefit from allowing more employees greater access to AI.
Having considered what elements of AI need to be democratized and who to democratize the technology for, it is time to think about how this can be achieved without compromising the science process. Moreover, choosing individuals with a commitment to ethical AI is key for company leaders.
Providing Affordable Access
Democratizing AI will only be possible if developers have cost-effective access to a wide variety of datasets, algorithms, model development, and storage space. Algorithms stored on GitHub repositories are a great example of shared technology that is free to use. Kaggle’s open-source datasets are another.
Whilst not charging at all may not always be feasible and lead to financial losses, neither is asking business users to pay thousands of dollars for datasets to train algorithms. Those charges would benefit the owners of the data, but they would not help the technology or the community behind it to expand.
Ensuring Abstraction
The concept of democratization of machine learning and AI means making the technology accessible to everyone, not only larger companies. That means removing the need for excessive programming knowledge. Just like drag-and-drop apps have made it possible to create websites without much effort, abstraction is necessary to allow users without knowledge of SQL queries or other advanced commands to access the data they need.
Companies intending to democratize technology and become AI-driven organizations need to ensure that all elements are accessible to those with limited specialist knowledge.
Enabling Control of Stack Elements
This step is about allowing users to control all the elements of the tech stack they are using. They should be in charge of what they are executing, when they are using it, and how they can interpret the results of their work.
Google’s Colab is an example of accessible technology with powerful graphical processing units that do not require the installation of additional packages. Instead, the system provides a range of support to train even complex AI, including neural network models.
Inspecting Ownership
Democratizing does not mean neglecting ownership of data or intellectual property. Technology vendors and users should consider which organizations are behind the data they are using and who benefits from continued usage.
Casual users may apply algorithms in the wrong context or misinterpret outcomes in this context.
Providing Training
Democratizing AI will require adequate training, especially for casual users. They need to know enough to allow them to utilize algorithms correctly and draw meaningful conclusions. In addition, power users should understand the mathematics behind the results. Ideally, this kind of information could be shared through a user manual.
Without proper training, the risk of business leaders and other people with access misusing algorithms or misinterpreting results remains high.
Ensuring Governance and Control
With the power of democratization come certain responsibilities. We touched on those when we talked about the importance of proper training. Successful computational model development depends on the accuracy and explainability of its output. Ensuring governance and control also means identifying and removing biased models before they can be deployed on cloud platforms. If a model delivers results that cannot be explained it needs to be kept from further development.
Specifying Intellectual Property Rights
Useful frameworks for democratization should specify whose intellectual property certain AI elements are. Clear data ownership can help drive and strengthen democratization. It is equally important to close the loop from ownership to accessibility of machine learning platforms.
Allowing Open-Sourcing
True democratization of AI is impossible without open-sourcing in a way that respects confidentiality, privacy, and competitive dynamics.
The goal is to allow everyone to learn about and experiment with AI programming, including studying, changing, and distributing software. To avoid issues caused by misinterpretation or wrong application of results, the industry needs to adhere to a democratization framework.
Benefits Of Democratizing AI
Throughout this article, we have touched on the benefits of democratizing AI and ML. Here is a more structured, in-depth look at those benefits.
Reducing Entry Barriers
Reducing barriers to entry helps organizations and individuals become data scientists. With datasets needed to train AI available on the cloud, learning about and training AI is no longer forbiddingly expensive. No-code AI tools also remove some of the science challenges from the process. Participating in global contests and so-called datathons can further help companies and individual enthusiasts expand their knowledge of and exposure to AI.
Minimizing Costs
Building AI solutions used to be impossible for smaller operators because of the associated costs. Democratizing AI through open-source data, models, and algorithms on the cloud allows anyone to build powerful AI apps.
Building Highly Accurate Models
Users can even pick up natural language processing models like Google’s BERT from the transformers’ library and train them for custom applications. Using these business tools makes it easier and faster to build highly accurate models that can recognize intent, too.
Analyzing Sentiments
Sentiment analysis is another common use of NLP. This allows companies to move beyond basic analyses and detect not only the fact that their products are being talked about but also how users are feeling about their products. Understanding whether a product or service is met with mostly positive, negative, or neutral sentiments simply delivers more actionable data for brands.
Detecting Hate Speech
Hate speech and cyberbullying have made headlines for months, if not years. Both are widespread on social media and can be extremely damaging to the individuals targeted. As AI evolves to better detect and interpret the semantics and intentions of language, applications will become better at interpreting hateful and potentially damaging undertones.
Conclusion
Democratizing AI and ML, including deep learning models and deep learning solutions is the key to mass adoption of these technologies across American organizations of all sizes. Platforms like Google Cloud Platform and Amazon Web Services are already making data, algorithms, and other tools more accessible than ever before.
Establishing buckets for training employees across businesses and ensuring solid network management is critical for the creation of reliable AI and ML applications. Just like the spread of personal computers several decades ago, the democratization of AI has the potential to change work as we know it to work as we could have only imagined it within just a few years.
References
Ludik, Jacques. Democratizing Artificial Intelligence to Benefit Everyone: Shaping a Better Future in the Smart Technology Era. 2021.
Lane, Julia. Democratizing Our Data: A Manifesto. MIT Press, 2021.
Democratizing Artificial Intelligence
Democratizing AI is an essential step towards making artificial intelligence accessible to a broader audience, including individuals and organizations that lack specialized knowledge and resources in the field of AI. The democratization of AI has the potential to unlock innovation and drive economic growth by enabling individuals and organizations to develop and implement AI solutions that can solve real-world problems. Moreover, democratizing AI is crucial to ensure that the benefits of AI are not limited to a small group of experts, but rather are accessible to a wider audience, including those who might not have the expertise to develop AI solutions themselves.
To achieve this goal, it is necessary to make AI more transparent and accessible to non-experts. This can be accomplished by providing education and training on AI, promoting open-source AI tools and platforms, making AI solutions available on cloud-based platforms, creating AI tools that automate complex tasks, and encouraging collaboration between experts and non-experts. By democratizing AI, we can create a more inclusive and equitable society where everyone has the opportunity to benefit from the transformative power of AI.
Table of contents
Also Read: What is a Digital Worker? How Do they Improve Automation?
Why Should We Democratize AI?
Democratizing AI will make the technology accessible to a larger number of people. You may wonder whether that is necessary, and the answer is yes. Cast your mind back a few decades when computers used to be something reserved for experts only. At the time, very few users were able to use the machines and benefit from their powers.
As operating systems simplified the use of computers and personal computers found their way on (almost) every desk, companies reaped the benefits of greater efficiencies and increased productivity. Democratizing AI can achieve the same if not a greater effect. The transition has already started as AI is using techniques like natural language processing (NLP), including audio processing and the workings of neural networks to improve its understanding of human speech and the intentions behind it.
Democratizing AI further will remove barriers to use and allow global economies and humanity as a whole to take another huge step forward.
What to Democratize?
When we talk about democratizing AI, it is easy to drift off into abstract contemplation of the concept. Considering concrete aspects of AI makes it easier to imagine how the transition could play out in real life.
Some of those key aspects to consider include:
Democratizing Data
Training AI applications and machine learning models as well as algorithms requires huge amounts of data. Applications and algorithms use unstructured data such as videos and images and structured data like tables to recognize patterns and test scenarios.
Just a few years ago, only a handful of companies had access to large enough datasets and the computational power to use them. Google, for example, secured access to sizable datasets when it purchased the AI community Kaggle. Other datasets in source code are being shared publicly on platforms like GitHub. One example is Prajna Bhandary’s mask detection dataset. These sets allow more users to access them and develop AI apps than ever before. Google Cloud Platform is another example of AI-powered platforms that can be used to build image classifiers.
Democratizing Storage and Computing
Cloud storage and cloud computing options have undoubtedly democratized AI by making resources widely available on a subscription basis. Cloud solution providers like Amazon Web Services (AWS) are allowing developers to build and deploy AI models for others to test. These systems are hardware agnostic, facilitating widespread access.
Cloud-based solutions like these reduce the need for powerful hardware ownership by providing access to central processing units (CPUs) and graphics processing units. However, whilst these computing platforms are facilitating AI development, using them effectively continues to require specialist knowledge and certifications.
Also Read: Impact of AI in Smart Homes
Democratizing AI Algorithms
Democratizing AI and entire machine learning algorithms means making them accessible to other developers. Right now, researchers are uploading and sharing their algorithms in GitHub source code repositories. Theoretically, anyone can access those systems. In practice, though, users need a certain degree of mathematical, statistical, and computer science knowledge to use these algorithms efficiently. Without a firm grasp of the technology behind the application, users may be unable to spot erroneous outcomes.
Democratizing the AI Model Development Process
Creating a working AI product requires training a model that consistently returns correct results. Developing this sort of model requires access to different algorithms which are run over a dataset to see which one delivers the most reliable performance. AutoML can do that kind of ‘legwork’ for developers.
But the technology still relies on the developers themselves to interpret the outputs and determine which of them is correct. Let us assume a developer is training a facial recognition AI. To launch the app with confidence, the developer needs to be sure how the AI will classify an unknown face. AutoML can help with that, but developers must still ensure correct outcomes and take care to remove biased datasets from their training data. Bias could enter the data if the algorithm was trained using more male than female images, for example.
Democratizing the AI Marketplace
As for most other products, there is a marketplace for models, data, and algorithms. Kaggle led the industry in showing how the market for models could be democratized. The community held contests to find the best models. Cash prizes kept developers interested in participating.
Despite the exciting prospect of a more open marketplace, several major obstacles remain. The sheer number of deep learning frameworks is one of them. Source framework standardization will make ML more accessible and lead to a consolidation of current technology vendors.
Aside from non-standardized deep learning frameworks, limitations to the democratization of marketplaces include the risk of misinterpretation of results. This could lead to faulty applications of models, data, and algorithms provided.
Whom Do You Democratize AI for?
Put simply, everyone. But let us take a closer look. Democratizing AI means making more technology available to a larger group of your employees, ideally across the entire organization. Just like PCs are no longer reserved for a few senior managers, AI is no longer just the remit of elite professionals.
Scaling AI across the business and making the technology available to larger groups of your employees can dramatically increase productivity throughout entire companies. Rather than limiting technology to analytics and data science teams, working with AI and ML will become part of the majority of the jobs your business offers. Granted, some of this rollout will be limited by the type of industry you operate in and your line of business. However, most sectors will benefit from allowing more employees greater access to AI.
Also Read: How Technology Has Changed Teaching and Learning
How Do You Democratize AI?
Having considered what elements of AI need to be democratized and who to democratize the technology for, it is time to think about how this can be achieved without compromising the science process. Moreover, choosing individuals with a commitment to ethical AI is key for company leaders.
Providing Affordable Access
Democratizing AI will only be possible if developers have cost-effective access to a wide variety of datasets, algorithms, model development, and storage space. Algorithms stored on GitHub repositories are a great example of shared technology that is free to use. Kaggle’s open-source datasets are another.
Whilst not charging at all may not always be feasible and lead to financial losses, neither is asking business users to pay thousands of dollars for datasets to train algorithms. Those charges would benefit the owners of the data, but they would not help the technology or the community behind it to expand.
Ensuring Abstraction
The concept of democratization of machine learning and AI means making the technology accessible to everyone, not only larger companies. That means removing the need for excessive programming knowledge. Just like drag-and-drop apps have made it possible to create websites without much effort, abstraction is necessary to allow users without knowledge of SQL queries or other advanced commands to access the data they need.
Companies intending to democratize technology and become AI-driven organizations need to ensure that all elements are accessible to those with limited specialist knowledge.
Enabling Control of Stack Elements
This step is about allowing users to control all the elements of the tech stack they are using. They should be in charge of what they are executing, when they are using it, and how they can interpret the results of their work.
Google’s Colab is an example of accessible technology with powerful graphical processing units that do not require the installation of additional packages. Instead, the system provides a range of support to train even complex AI, including neural network models.
Inspecting Ownership
Democratizing does not mean neglecting ownership of data or intellectual property. Technology vendors and users should consider which organizations are behind the data they are using and who benefits from continued usage.
Casual users may apply algorithms in the wrong context or misinterpret outcomes in this context.
Providing Training
Democratizing AI will require adequate training, especially for casual users. They need to know enough to allow them to utilize algorithms correctly and draw meaningful conclusions. In addition, power users should understand the mathematics behind the results. Ideally, this kind of information could be shared through a user manual.
Without proper training, the risk of business leaders and other people with access misusing algorithms or misinterpreting results remains high.
Ensuring Governance and Control
With the power of democratization come certain responsibilities. We touched on those when we talked about the importance of proper training. Successful computational model development depends on the accuracy and explainability of its output. Ensuring governance and control also means identifying and removing biased models before they can be deployed on cloud platforms. If a model delivers results that cannot be explained it needs to be kept from further development.
Specifying Intellectual Property Rights
Useful frameworks for democratization should specify whose intellectual property certain AI elements are. Clear data ownership can help drive and strengthen democratization. It is equally important to close the loop from ownership to accessibility of machine learning platforms.
Allowing Open-Sourcing
True democratization of AI is impossible without open-sourcing in a way that respects confidentiality, privacy, and competitive dynamics.
The goal is to allow everyone to learn about and experiment with AI programming, including studying, changing, and distributing software. To avoid issues caused by misinterpretation or wrong application of results, the industry needs to adhere to a democratization framework.
Benefits Of Democratizing AI
Throughout this article, we have touched on the benefits of democratizing AI and ML. Here is a more structured, in-depth look at those benefits.
Reducing Entry Barriers
Reducing barriers to entry helps organizations and individuals become data scientists. With datasets needed to train AI available on the cloud, learning about and training AI is no longer forbiddingly expensive. No-code AI tools also remove some of the science challenges from the process. Participating in global contests and so-called datathons can further help companies and individual enthusiasts expand their knowledge of and exposure to AI.
Minimizing Costs
Building AI solutions used to be impossible for smaller operators because of the associated costs. Democratizing AI through open-source data, models, and algorithms on the cloud allows anyone to build powerful AI apps.
Building Highly Accurate Models
Users can even pick up natural language processing models like Google’s BERT from the transformers’ library and train them for custom applications. Using these business tools makes it easier and faster to build highly accurate models that can recognize intent, too.
Analyzing Sentiments
Sentiment analysis is another common use of NLP. This allows companies to move beyond basic analyses and detect not only the fact that their products are being talked about but also how users are feeling about their products. Understanding whether a product or service is met with mostly positive, negative, or neutral sentiments simply delivers more actionable data for brands.
Detecting Hate Speech
Hate speech and cyberbullying have made headlines for months, if not years. Both are widespread on social media and can be extremely damaging to the individuals targeted. As AI evolves to better detect and interpret the semantics and intentions of language, applications will become better at interpreting hateful and potentially damaging undertones.
Conclusion
Democratizing AI and ML, including deep learning models and deep learning solutions is the key to mass adoption of these technologies across American organizations of all sizes. Platforms like Google Cloud Platform and Amazon Web Services are already making data, algorithms, and other tools more accessible than ever before.
Establishing buckets for training employees across businesses and ensuring solid network management is critical for the creation of reliable AI and ML applications. Just like the spread of personal computers several decades ago, the democratization of AI has the potential to change work as we know it to work as we could have only imagined it within just a few years.
References
Ludik, Jacques. Democratizing Artificial Intelligence to Benefit Everyone: Shaping a Better Future in the Smart Technology Era. 2021.
Lane, Julia. Democratizing Our Data: A Manifesto. MIT Press, 2021.
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