This article describes how technical infrastructure developed by the nonprofit OpenMined enables external scrutiny of AI systems without compromising sensitive information. Independent external scrutiny of AI systems provides crucial transparency into AI development, so it should be an integral component of any approach to AI governance. In practice, external researchers have struggled to gain access to AI systems because of AI companies' legitimate concerns about security, privacy, and intellectual property. But now, privacy-enhancing technologies (PETs) have reached a new level of maturity: end-to-end technical infrastructure developed by OpenMined combines several PETs into various setups that enable privacy-preserving audits of AI systems. We showcase two case studies where this infrastructure has been deployed in real-world governance scenarios: "Understanding Social Media Recommendation Algorithms with the Christchurch Call" and "Evaluating Frontier Models with the UK AI Safety Institute." We describe types of scrutiny of AI systems that could be facilitated by current setups and OpenMined's proposed future setups. We conclude that these innovative approaches deserve further exploration and support from the AI governance community. Interested policymakers can focus on empowering researchers on a legal level.

This post is a continuation of "Remote Data Science Part 1: Today's privacy challenges in BigData". The previous blog talks about the importance of understanding privacy challenges in BigData and explains how "Remote Data Science" enables three privacy guarantees for the data scientist and the data owner. This blog explains the different components of Remote Data Science. Understand "Model-centric FL" and "Data-centric FL" while both are deployable in Remote Data Science Architecture. PyGrid is a peer-to-peer network of data curators/owners and data scientists who can collectively train AI models using PySyft on decentralised data (Data never leaves the device).

We are seeing overwhelming growth in AI/ML systems to process oceans of data that are being generated in the new digital economy. However, with this growth, there is a need to seriously consider the ethical and legal implications of AI. As we entrust increasingly more sophisticated and important tasks to AI systems, such as automatic loan approval, for example, we must be absolutely certain that these systems are responsible and trustworthy. Reducing bias in AI has become a massive area of focus for many researchers and has huge ethical implications, as does the amount of autonomy that we give these systems. The concept of Responsible AI is an important framework that can help build trust in your AI deployments.

Facebook AI Research (FAIR) has announced the release of Opacus, a high-speed library for applying differential privacy techniques when training deep-learning models using the PyTorch framework. Opacus can achieve an order-of-magnitude speedup compared to other privacy libraries. The library was described on the FAIR blog. Opacus provides an API and implementation of a PrivacyEngine, which attaches directly to the PyTorch optimizer during training. By using hooks in the PyTorch Autograd component, Opacus can efficiently calculate per-sample gradients, a key operation for differential privacy.

Federated Learning was introduced to collaboratively learn a shared prediction model while keeping all the training data on the device. This enabled machine learning developers to build pipelines that wouldn't require to store the data in the cloud. The main drivers behind FL are privacy and confidentiality concerns, regulatory compliance requirements, as well as the practicality of moving data to one central learning location. Here are a few libraries (mostly by OpenMined) for developers that can help in building federated learning systems for the edge devices. The developers can write the model and training plan in normal PyTorch and PySyft, and syft.js

With OpenMined, an AI model can be governed by multiple owners and trained securely on an unseen, distributed dataset. The mission of the OpenMined community is to create an accessible ecosystem of tools for private, secure, multi-owner governed AI. (Source: openmined.org/). An open-source community whose goal is to make the world more privacy-preserving by lowering the barrier-to-entry to private AI technologies. The company was founded in 2017 by Andrew Trask. This is a talk from him about privacy-preserving.

Artificial Intelligence is taking the world by storm and Machine Learning is everywhere. But is it actually useful without the enormous amount of data that is required for training these systems so that it can be used to infer on new data? Data will remain as the trump-card of most machine learning algorithms until there is a significant improvement in technologies like Single Shot Learning. We humans are considered to be the most valuable resource available right now, not for the fact that we can spend a lot of money, but for the fact that we can do generate a lot of content which can be used to make a lot of money. Yes, the data that we make is one of the most valuable resource out there right now.

For practical reasons: I think their work involves extremely relevant fields, combinations of which make the project extremely innovative. I know there's some confusion and the tendency to avoid buzzwords, but working with blockchains, encryption or artificial intelligence is currently a highly recommended career choice. It happened to be one of the most trendy projects on GitHub and Python too. For the developers out there, contributing to open-sourced projects is the best way to show off your skills or to learn something new, while, most importantly, having fun! Also for reasons that go beyond: Besides the community growing at an insanely fast rate, I personally feel that OpenMined's community is somewhat much warmer than any other ones I'm part of.