Distributed Learning for Democracy

Wisdomise Data Scientist - Hamid Reza Mazandarani, shares his insight on a very intriguing topic within the world of AI.

One of the most important elements to blockchain technology is distribution of control, ownership, and learning. Achieving distributed network architecture means high security and fair access for the masses among many other important concepts. Adding Artificial Intelligence (AI) to a decentralized ecosystem has the potential to elevate its capabilities alongside the utility for users. But how can we go about to accomplish this, and what must we keep a look out for?

Is Liberty in Danger?

The inspiring book of Yuval Noah Harari, 21 Lessons for the 21st Century, discusses the fundamental challenges humanity faces in the era of bio and data technologies. [1]

In chapter 3 of the book, titled Liberty, the author suggests that AI might be more potent in the hands of totalitarians. The following scenario clearly illustrates what Harari has in mind:

If an authoritarian government orders all its citizens to have their DNA scanned and to share all their medical data with some central authority, it would gain an immense advantage in genetics and medical research over societies in which medical data is strictly private.

But how is this possible? Furthermore, how can the AI and data science ecosystem change the game in favor of democracy and enshrine individuals with power rather than institutions? These are the questions we will address in this article.

The Rise of AI-Powered Totalitarianism

What Harari envisions about the impact of data science on democracy can be broadly illustrated in the following image.

Image by author, inspired from [1]

Indeed, since the emergence of data-processing techniques in the late twentieth century, democracies have enjoyed a golden age:

Given twentieth-century technology, it was inefficient to concentrate too much information and power in one place. Nobody [including dictatorships] had the ability to process all the information fast enough and make the right decisions.

However, the story might not have a happy-ending:

AI might make centralized systems far more efficient than diffused systems, because machine learning works better, the more information it can analyze.

Practitioners of AI and data science are familiar with the above sentences. The more data collected, the better the output. Consequently, authoritarian governments and big tech companies can gain a competitive edge thanks to well-structured bureaucracy and advanced surveillance systems. On top of this, enterprises, institutions, and governments have a strong incentive to make their data collection apparatuses better through research and development in order to maintain their advantage.

Level Playing Field

Democratic governments have limiting regulations (e.g., the well-known GDPR in Europe) that prevent them from accessing massive amounts of citizens’ data. Also, Small and Medium-sized Enterprises (SMEs) lack the massive datasets big tech companies possess. After all, who has access to -even an anonymized version of- Facebook’s Graph dataset? Data assets like this are simply beyond the reach of small businesses and individuals.

So, what can democratic governments and SMEs do? One possible technical solution has already been spoiled in the title of this article: Distributed Learning.

Here are two personal opinions before we dive in:

• Distributed learning can be beneficial in democratic countries as well. However, there is no binary classification of countries as democratic and totalitarian countries; there is a spectrum of democracy that each country lies within;
• Technological solutions are not a panacea for human rights issues such as the right to privacy. These challenges should be ultimately resolved with human centric solutions.

Learning, but distributed

Any Machine Learning (ML) model learns to produce better results by extracting patterns in the data.

Consider a CCTV camera connected to an ML model. The model tries to find anomalies in the scene (e.g., shoplifting in a store). The common mindset to develop such an ML model is as follows:

• Gather and store a sufficient amount of data (e.g., normal and abnormal scenes in the above example);
• Train a single model on a fraction of the available data;
• Test the model on the rest of the data to see if it works well.

The distributed learning mindset enhances this monolithic system to a modular one, illustrated in the following image adopted from a survey on the same topic [2].

In the case of data parallelism, one could train separate models on data chunks from different sources (for example, CCTV cameras of different stores).

In the case of model parallelism, data itself doesn’t change, but separate models (usually on different machines) collaboratively extract patterns in the data.

Both data or models can be parallelized [2]

Now, let’s focus on data parallelism, which is more in line with our topic. This approach helps democratic governments and SMEs leverage the knowledge of various data sources without breaking privacy regulations.

For example, different countries may agree on a standard format for financial transaction data. Then, each country can train its own model to detect fraudulent transactions in the data. Without the need to share sensitive financial data, they can share their models (i.e., the knowledge extracted from their data) in order to make a powerful global model.

The above scenario is a class of distributed learning called Federated Learning and can be applied in addition to finance in other domains, including medicine, cybersecurity, transportation, networking, etc.

The final word: As with the state of democracy in different countries, distributed learning schemes also lie on a spectrum of varying data concentration levels. The following image demonstrates different scenarios [2]. The most futuristic scenario is peer-to-peer distributed learning (item d in the image), which is similar to sharing wisdom in human societies.

Degrees of distribution vary in distributed learning schemes [2]

At Wisdomise AI Labs, we are continually challenging the capabilities of AI technology in a decentralized economy. We are using the collective wisdom of crowds to make the knowledge of web3 and finance available for all.

Visit us at wisdomise.io and see how we are working to become the DeepMind of DeFi. Explore our products and join our mission of masterminding web3 and AI technology for all.

References

[1] Harari, Yuval Noah. 21 Lessons for the 21st Century. Random House, 2018.

[2] Verbraeken, Joost, et al. “A survey on distributed machine learning.” ACM Computing Surveys (CSUR) 53.2 (2020): 1–33. (link)