Read an interesting article this week in The Atlantic, Why Technology Favors Tyranny by Yuvai Noah Harari, about the inevitable future of technology and how the use of data will drive it.
At the end of the article Harari talks about the need to take back ownership of our data in order to gain some control over the tech giants that currently control our data.
In part 3, Harari discusses the coming AI revolution and the impact on humanity. Yes there will still be jobs, but early on less jobs for unskilled labor and over time less jobs for skilled labor.
Yet, our data continues to be valuable. AI neural net (NN) accuracy increases as a function of the amount of data used to train it. As a result, he has the most data creates the best AI NN. This means our data has value and can be used over and over again to train other AI NNs. This all sounds like data is just another form of capital, at least for AI NN training.
If only we could own our data, then there would still be value from people’s (digital) exertions (labor), regardless of how much AI has taken over the reigns of production or reduced the need for human work.
Safe by cjc4454 (cc) (from flickr)What we need is data (savings) banks. These banks would hold people’s data, gathered from social media likes/dislikes, cell phone metadata, app/web history, search history, credit history, purchase history, photo/video streams, email streams, lab work, X-rays, wearables info, etc. Probably many more categories need to be identified but ultimately ALL the digital data we generate today would need to be owned by people and deposited in their digital bank accounts.
Social media companies, telecom, search companies, financial services app companies, internet providers, etc. anywhere you do business should supply a copy of the digital data they gather for a person back to that persons data bank account.
There are many technical problems to overcome here but it could be as simple as an object storage bucket, assigned to each person that each digital business deposits (XML versions of) our digital data they create for everyone that uses their service. They would do this as compensation for using our data in their business activities.
How to change data ownership?
Today, we all sign user agreements which essentially gives a company the rights to our data in perpetuity. That needs to change. I see a few ways that this change could come about
- Countries could enact laws to insure personal data ownership resides in the person generating it and enforce periodic distribution of this data
- Market dynamics could impel data distribution, e.g. if some search firm supplied data to us, we would be more likely to use them.
- Societal changes, as AI becomes more important to profit making activities and reduces the need for human work, and as data continues to be an important factor in AI success, data ownership becomes essential to retaining the value of human labor in society.
Probably, all of the above and maybe more would be required to change the ownership structure of data.
How to profit from data?
Technical entities needing data to train AI NNs could solicit data contributions through an Initial Data Offering (IDO). IDO’s would specify types of data required and a proportion of AI NN ownership, they would cede to all data providers. Data providers would be apportioned ownership based on the % identified and the number of IDO data subscribers.
Data banks would extract the data requested by the IDO and supply it to the IDO entity for use. For IDOs, just like ICO’s or IPO’s, some would fail and others would succeed. But the data used in them would represent an ownership share sort of like a stock (data) certificate in the AI NN.
Data bank responsibilities
Data banks would have various responsibilities and would need to collect fees to perform them. For example, data banks would be responsible for:
- Protecting data deposits – to insure data deposits are never lost, are never accessed without permission, are always trackable as to how they are used..
- Performing data deposits – to verify that data is deposited from proper digital entities, to validate that data deposits are in a usable form and to properly store the data in a customers object storage bucket.
- Performing data withdrawals – upon customer request, to extract all the appropriate data requested by an IDO, anonymize it, secure it, package it and send it to the IDO originator.
- Reconciling data accounts – to track data transactions, data banks would supply a monthly statement that identifies all data deposits and data withdrawals, data revenues and data expenses/fees.
- Enforcing data withdrawal types – to enforce data withdrawal types, as data withdrawals can have many different characteristics, such as exclusivity, expiration, geographic bounds, etc. Data banks would need to enforce withdrawal characteristics, at least to the extent they can
- Auditing data transactions – to insure that data is used properly, a consortium of data banks or possibly data accountancies would need to audit AI training data sets to verify that only data that has been properly withdrawn is used in trying the NN. .
AI NN, tools and framework responsibilities
In order for personal data ownership to work well, AI NNs, tools and frameworks used today would need to change to account for data ownership.
- Generate, maintain and supply immutable data ownership digests – data ownership digests would be a sort of stock registry for the data used in training the AI NN. They would need to be a part of any AI NN and be viewable by proper data authorities
- Track data use – any and all data used in AI NN training should be traceable so that proper data ownership can be guaranteed.
- Identify AI NN revenues – NN revenues would need to be isolated, identified and accounted for so that data owners could be rewarded.
- Identify AI NN data expenses – NN data costs would need to somehow be isolated, identified and accounted for so that data expenses could be properly deducted from data owner awards. .
At some point there’s a need for almost a data profit and loss statement as well as a data balance sheet for at an AI NN level. The information supplied above should make auditing data ownership, use and rewards much more feasible. But it all starts with identifying data ownership and the data used in training the AI.
There are a thousand more questions that come to mind. For example
- Who owns earth sensing satellite, IoT sensors, weather sensors, car sensors etc. data? Everyone in the world (or country) being monitored is laboring to create the environment sensed by these devices. Shouldn’t this sensor data be apportioned to the people of the world or country where these sensors operate.
- Who pays data bank fees? The generators/extractors of the data could pay in addition to providing data deposits for the privilege to use our data. I could also see the people paying. Having the company pay would give them an incentive to make the data load be as efficient and complete as possible. Having the people pay would induce them to use their data more productively.
- What’s a decent data expiration period? Given application time frames these days, 7-15 years would make sense. But what happens to the AI NN when data expires. Some way would need to be created to extract data from a NN, or the AI NN would need to cease being used and a new one would need to be created with new data.
- Can data deposits be rented/sold to data aggregators? Sort of like a AI VC partnership only using data deposits rather than money to fund AI startups.
- What happens to data deposits when a person dies? Can one inherit a data deposits, would a data deposit inheritance be taxable as part of an estate transfer?
In the end, as data is required to train better AI, ownership of our data makes us all be capitalist (datalists) in the creation of new AI NNs and the subsequent advancement of society. And that’s a good thing.