In a new bold move, the German Social Democratic Party (SDP) made a radical presentation. It suggested that tech giants like Google should make their data available to the public. This includes their competitors.
The SDP is a part of the coalition government led by Angela Merkel. This means the idea has a fair shot of happening.
The SDP Paper and Data Privacy
According to Andrew Nahles who is the party leader, the organization first flirted with the idea when the Handelsblatt article came to light. This article showed the parallels between modern-day data-economy to the industrialization and gold rush period of the 19th century.
By amassing user data, corporations like Google, Facebook and Amazon have grown to become tech behemoths. Their annual turnover would put the GDP of many countries to shame. According to the article, these corporations leverage that data to hurt customers. They also limit the growth of start-ups who want to enter the industry.
According to a paper released by the SPD on Wednesday, it’s important to establish a democratic balance in the digital order of things. This is based on truth and competition as well as equitable wealth distribution.
In practice, this would mean forcing these corporations to give up proprietary data. They would have to share it with others in order to create a mutually thriving ecosystem.
According to the party, making this data available to the public through APIs could help do away with data monopolies. This is all while maintaining protective measures for data that requires legal confidentiality and business’ trade secrets.
Besides that, public data sharing on this large scale would allow for the availability of high-quality data. This data can help innovate and widen the competitive landscape to make for a more robust playing field.
Data Privacy and Tech Innovation
The SDP paper further states that data is the main driver behind innovation. Tech companies can greatly benefit from sharing that data with one another instead of hoarding it and keeping it in silos.
Public data sharing on this scale would escalate machine learning and fast-track innovation. This is because more people would be able to work on ideas and solutions based on vast stores of information that for now remain hidden.
Of course, the paper acknowledges the importance of maintaining business models based on data collection. In this case, it’s important to ensure that data sharing does not threaten the motivation to collect data.
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The Role of Publicized Data
To many, the SDP’s paper comes as no surprise, as the public continues to put pressure on tech giants like Google and Amazon. For instance, the German antitrust authority targeted Facebook’s data collection policies and exploitative business terms. Facebook argued that the party should leave such work to data-protection authorities in an effort to shake off the scrutiny.
The Future of Data Privacy Solutions
The phenomenon of data monopoly is rather new to the human stage, so it’s refreshing to see creative and innovative solutions from policymakers and regulators. Gavin Newson, the Governor of California, recently made a proposal to force Google and Facebook to actually pay or use data through “data dividends.”
In addition to regulating big tech, the SDP proposal is also concerned with helping to accelerate AI innovation, particularly in Germany. Through the combination of data protection and data availability, Germany aims to fast-track tech innovation by releasing data from the world tech giants, most of which are located in China and the US, and both countries allow corporations to their data instead of sharing it to unlock greater possibilities.
The SDP plans to hold a discussion with relevant stakeholders and experts in order to move the needle a little closer to their goal, as communicated in the draft paper. With that said, a parliamentary representative from the Christian Democratic Union (CDU) stated that while the proposal is sound, it doesn’t appear well thought out, because data anonymization would diminish its value both for AI and analysis purposes. But, SDP asserts that this all depends on the type of AI training that’s conducted.
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