Data Unions

How to Protect Your Data and Your Job from the Greedy Clutches of AI Models

Dec 15, 2022

In the age of big data and machine learning, data has become a valuable commodity. Many companies and organizations are using data to train AI models that can perform a wide range of tasks, from diagnosing medical conditions to generating art. However, the vast majority of people who generate this data have little control over how it is used or how they are compensated for its use.

Data Unions, also known as Data DAOs or Data Trusts, offer a solution to this problem. These organizations give individuals a way to control their data and ensure that they are fairly compensated if their data is used in profitable AI models. By creating a collective bargaining power for data, Data Unions give individuals a say in how their data is used and allow them to share in the profits generated from its use.

Data Exploitation and Job Replacement

One of the major problems with the current state of data usage is the exploitation of individuals' data without their consent or compensation. For example, many online services collect vast amounts of data from their users, often without their knowledge or understanding of how that data is being used. This data is then used to train AI models that can be used for a variety of purposes, such as targeted advertising or credit scoring. In these cases, the individuals who generated the data have no control over how it is used or how they are compensated for its use.

Another problem is the threat of powerful machine learning models trained on worker data replacing jobs. For example, radiologists are currently training the AI models that may eventually replace their jobs. In this case, the data generated by the radiologists is used to train AI models that can perform the same tasks as the radiologists, potentially making their jobs obsolete. However, the radiologists are not compensated for their contribution to the training of these AI models, nor do they have any control over how their data is used.

Customer service representatives are also at risk of having their jobs replaced by AI models. These models are trained on large amounts of data generated by customer service representatives as they interact with customers and resolve their issues. As a result, customer service representatives are effectively training their own replacements without being compensated for their contribution to the training of these AI models.

Artists have raised concerns about the use of their art to train AI models. In some cases, machine learning models have generated images that are similar to the training examples, leading to accusations of copyright infringement. However, the artists have not been compensated for the use of their art in the training of these AI models. This presents a significant problem for artists, as it puts their work at risk of being reproduced without permission or compensation. It also raises broader issues around the ownership and control of data.

These are just a few examples of workers whose work is being used to train machine learning models that may replace their jobs. As AI technology continues to advance, it is likely that many more workers will face similar challenges.

Collective Solutions

A Data Union would work by allowing individuals to pool their data and negotiate collectively with companies and organizations that wish to use it. The Data Union would provide a decentralized platform for individuals to join and manage their data, as well as vote on how it should be used and receive a share of the profits generated from its use.

Here is a possible example of how a Data Union might work:

An individual decides to join a Data Union and contributes their data to the pool. The data could include information such as their medical records, shopping habits, social media activity, and other personal information.
The Data Union negotiates with companies and organizations that wish to use the data for machine learning and other purposes. These companies would pay a fee to access the data and agree to certain terms and conditions, such as using the data ethically and transparently.
The profits generated from the sale of the data are distributed among the members of the Data Union, according to the terms agreed upon by the members. For example, the profits could be distributed equally among all members or based on the amount of data each member contributed.
The members of the Data Union can also vote on how their data should be used and hold companies accountable for any misuse of the data. This ensures that the data is used in a way that aligns with the values and interests of the members.

By enabling individuals to negotiate and enforce data usage agreements, Data Unions can help to generate new forms of income and wealth. This is particularly important for marginalized communities, who may have limited access to traditional sources of income and wealth. By providing new economic opportunities, Data Unions can help to reduce inequality and promote social justice.

Data Unions can support the development of local and community-based solutions to data management and governance. By enabling individuals and communities to assert their ownership and control over their data, Data Unions can support the emergence of local, democratic, and inclusive data governance structures. This can help to reduce the dominance of large corporations and organizations in the data economy, and promote the development of diverse and resilient data ecosystems.

Additionally, Data Unions can provide mechanisms for enforcing data usage agreements and resolving disputes. This can include arbitration or other forms of dispute resolution, as well as penalties for companies that violate the terms of their data usage agreements. By providing these mechanisms, Data Unions can help to ensure that companies are held accountable for their actions and that users are protected from unfair or unethical data usage practices.

Data Unions as DAOs?

DAO (Decentralized Autonomous Organization) tooling is one effective way to implement Data Unions because it provides the necessary infrastructure for enabling decentralized decision-making and governance within a Data Union. DAO tooling includes tools for voting, proposal creation, and dispute resolution, as well as mechanisms for incentivizing participation and collaboration among Data Union members.

By using DAO tooling, Data Unions can enable individuals to collectively assert their ownership and control over their data. This can include the creation of data usage agreements that are transparent, fair, and enforceable, as well as providing automated mechanisms for ensuring that individuals are always compensated for the use of their data.

As DAOs, Data Unions can be made accessible to people living outside the jurisdiction of the United States. Decentralized blockchains provide a global and neutral platform for enabling data ownership and control, regardless of the location of the individuals involved. This can be particularly beneficial for people living in oppressed or marginalized communities, who may not have access to traditional forms of data governance or protection.

Additionally, DAO tooling can support the development of flexible and adaptable governance structures for Data Unions. This can include the ability to modify and update governance rules and procedures as needed, in response to changing circumstances or the emergence of new challenges. By providing this flexibility and adaptability, DAO tooling can help to ensure that Data Unions are able to evolve and adapt to changing conditions in the data economy.

Work to be Done

There is still much work to be done to fully realize the potential of Data Unions. Some of the key areas that require further attention include:

Developing legal frameworks and regulations to support the operation of Data Unions and protect the rights of individuals who participate in them.
Building scalable and user-friendly systems for managing data ownership and control, including tools for negotiating and enforcing data usage agreements.
Conducting research on the economic and social impacts of Data Unions, including their potential to generate new forms of income and empower individuals.
Engaging with policymakers, industry leaders, and other stakeholders to promote the adoption of Data Unions and support their development.

Overall, there is a need for continued research, development, and advocacy to advance the field of Data Unions and ensure that they are able to deliver on their promise of giving individuals ownership and control over their data.

An Ironic Twist

In conclusion, this essay was created by a machine learning model that didn't compensate the writers whose work was used to train the model. This ironic twist serves as a reminder of the importance of data ownership and control in the age of machine learning, and the need for solutions such as Data Unions to give individuals a say in how their data is used and to ensure that they are compensated for its use.

It is worth noting that there may be sentences in this essay that were stolen from various writers without attribution or compensation, further highlighting the need for solutions such as Data Unions to protect the rights of creators and prevent the exploitation of their work.

For more reading on Data DAOs, I highly recommend the paper Decentralised Autonomous Organisations (DAOs) as Data Trusts: A General-purpose Data Governance Framework for Decentralised Data Ownership, Storage, and Utilisation, by Kelsie Nabben. Wouldn’t be surprised if the Chat GPT referenced it without citation.