Wow, I absolutely loved this week’s readings, especially since we have been talking about data privacy in my HKS classes. I really do think federated learning and encrypted computation have a place in helping us move towards more equitable data as a public asset. If privacy can be fully achieved, we can more safely make data sets available to both the public and private sector for mass use. Governments could more effectively deploy open source data projects from these privacy technologies. This could eventually provide information in several industries, including healthcare which can leave to lives saved. Differential privacy, which allows mathematical guarantees around privacy preservation, still seems nebulous to me, because can something really produce guarantees? Also its not clear how the application of differential privacy works. If we can properly institute ways to input clean data then blockchain based machine learning marketplaces can be great to ensure data privacy and increased AI intelligence via private machine learning, which allows for training to be done on sensitive private data without revealing it. Decentralized machine learning marketplaces can dismantle the data monopolies of the current tech giants. They standardize and commoditize the main source of value creation on the internet by shifting value from data to algorithms. Other reasons include garnering top models through economic incentives, democratizes powerful machine learning and accelerates access to the open marketplace for data. I thought the most interesting benefit was how web search gets inverted with products competing to find their user. Overall, if these marketplaces do come to fruition, I’m still a bit confused about the need for a meta model as outlined in this article: https://medium.com/@FEhrsam/blockchain-based-machine-learning-marketplaces-cb2d4dae2c17
Machine learning marketplaces do have risks though in regards to the argument of garbage in and garbage out in regards to the quality of data that the machine learning algorithm is building upon. I see AI Ethics to be more pressing as compared to Blockchain ethics, since AI would be an input into blockchain, and there are still many risks with algorithm bias and data inputs. From a policy perspective I would start with AI first and propose some of the following recs:
- First we must continue strong R&D investment to drive technological breakthroughs, economic competitiveness and national security. A number of tools exist here including direct funding of government, research facilities, universities, private sector research, and tax incentives. Government funding spurs spinoffs and spillover effects for society.
- Second, it is vital to ensure the U.S. is driving the development of technical standards for the deployment and adoption of AI technologies. We will institute proper governance checks by having a community of stakeholders verify the framing of problems, collection of data, and preparation of data for AI algorithms.
- Third, as we think about the jobs of the future and homeland security, I have a plan to make sure we train current and future generations of American citizens with the skills to develop and apply AI techniques, through mandatory education and workforce modules. Technology jargon shouldn’t be a bunch of laughable buzz words but rather something we truly understand!
- Fourth, the U.S. must foster public trust in AI technologies and ensure that its usage protects civil liberties and privacy of the American people. We must target the issue of data privacy and ownership which will require cooperation with our innovation arms (such as the Defense Advanced Research Projects Agency) and the private sector.
- Finally, we must promote an international environment that supports AI community standards and open markets for our industries. This includes working with other countries to implement open data initiatives or democratizing access to the internet.