In this set of lessons, I will write about privacy in machine learning, answering some confusing questions about he most essential concepts in the digital era. Many legal systems protect a right to privacy, however privacy remains an elusive and controversial concept.

A federal court in Australia has ruled that AI systems can be credited as inventors under patent law in a case that could set a global precedent. Ryan Abbott, a professor at University of Surrey, has launched over a dozen patent applications around the world – including in the UK, US, New Zealand, and Australia – on behalf of US-based Dr Stephen Thaler. The twist here is that it's not Thaler which Abbott is attempting to credit as an inventor, but rather his AI device known as DABUS. "In my view, an inventor as recognised under the act can be an artificial intelligence system or device," said justice Jonathan Beach, overturning Australia's original verdict. "We are both created and create. Why cannot our own creations also create?"

Coinbase has built the world's leading compliant cryptocurrency platform serving over 30 million accounts in more than 100 countries. With multiple successful products, and our vocal advocacy for blockchain technology, we have played a major part in mainstream awareness and adoption of cryptocurrency. We are proud to offer an entire suite of products that are helping build the cryptoeconomy and increase economic freedom around the world. There are a few things we look for across all hires we make at Coinbase, regardless of role or team. First, we look for signals that a candidate will thrive in a culture like ours, where we default to trust, embrace feedback, disrupt ourselves, and expect sustained high performance because we play as a championship team.

Although the fairness community has recognized the importance of data, researchers in the area primarily rely on UCI Adult when it comes to tabular data. Derived from a 1994 US Census survey, this dataset has appeared in hundreds of research papers where it served as the basis for the development and comparison of many algorithmic fairness interventions. We reconstruct a superset of the UCI Adult data from available US Census sources and reveal idiosyncrasies of the UCI Adult dataset that limit its external validity. Our primary contribution is a suite of new datasets derived from US Census surveys that extend the existing data ecosystem for research on fair machine learning. We create prediction tasks relating to income, employment, health, transportation, and housing. The data span multiple years and all states of the United States, allowing researchers to study temporal shift and geographic variation. We highlight a broad initial sweep of new empirical insights relating to trade-offs between fairness criteria, performance of algorithmic interventions, and the role of distribution shift based on our new datasets. Our findings inform ongoing debates, challenge some existing narratives, and point to future research directions. Our datasets are available at https://github.com/zykls/folktables.

EuroVoc is a multilingual thesaurus that was built for organizing the legislative documentary of the European Union institutions. It contains thousands of categories at different levels of specificity and its descriptors are targeted by legal texts in almost thirty languages. In this work we propose a unified framework for EuroVoc classification on 22 languages by fine-tuning modern Transformer-based pretrained language models. We study extensively the performance of our trained models and show that they significantly improve the results obtained by a similar tool - JEX - on the same dataset. The code and the fine-tuned models were open sourced, together with a programmatic interface that eases the process of loading the weights of a trained model and of classifying a new document.

Apple is pushing back against criticism over its plan to scan photos on users iPhones and in iCloud storage in search of child sexual abuse images. In a Frequently Asked Questions document focusing on its'Expanded Protections for Children,' Apple insisted its system couldn't be exploited to seek out images related to anything other than child sexual abuse material (CSAM). The system will not scan photo albums, Apple says, but rather looks for matches based on a database of'hashes' - a type of digital fingerprint - of known CSAM images provided by child safety organizations. While privacy advocacies worry about'false positives, Apple boasted that'the likelihood that the system would incorrectly flag any given account is less than one in one trillion per year.' Apple also claims it would'refuse any such demands' from government agencies, in the US or abroad.

Banks, consumer advocates and think tanks are weighing in to federal bank regulators about potential pitfalls in the use of artificial intelligence and machine learning in making loan decisions. In responses to regulators' call for comments, many expressed interest in an increased use of AI and machine learning in the banking business, along with caveats about fair lending and unlawful discrimination concerns. FinRegLab, a Washington-based research group that says it has launched a broad inquiry into the use of AI in financial services, told the agencies that machine learning could be "transformational," as current gaps "increase the cost or risk of serving particular consumer and small-business populations using traditional models and data." At the same time, the predictive power of machine learning models can increase potential risks "due to the models' greater complexity and to their potential to exacerbate historical disparities and flaws in underlying data," FinRegLab said. AI and machine learning might amplify patterns of historical discrimination and financial exclusion through reliance on flawed data or mistakes in development.

Attorney Jack Cohen knows both sides of the blame and claim game. He's spent nearly 40 years practicing law, much of it trying injury suits for insurance companies. But he's also written a novel called "Bad Faith," which tells the story from the victim's side. Cohen knows his limitations--you can't fight artificial intelligence (AI). You have to work with it.

In a highly unusual undertaking in the technology industry, the Alphabet Workers Union was formed by over 400 Google engineers and other workers in early January. The union now has about 800 members. The Alphabet Workers Union is a minority union, representing a fraction of the company's more than 260,000 full-time employees and contractors. The workers stated at the outset that it was primarily an effort to give structure to activism at Google, rather than to negotiate for a contract. The union is affiliated with the Communications Workers of America (CWA), a union representing workers in telecommunications and media in the US and Canada.

This report from the Montreal AI Ethics Institute covers the most salient progress in research and reporting over the second quarter of 2021 in the field of AI ethics with a special emphasis on "Environment and AI", "Creativity and AI", and "Geopolitics and AI." The report also features an exclusive piece titled "Critical Race Quantum Computer" that applies ideas from quantum physics to explain the complexities of human characteristics and how they can and should shape our interactions with each other. The report also features special contributions on the subject of pedagogy in AI ethics, sociology and AI ethics, and organizational challenges to implementing AI ethics in practice. Given MAIEI's mission to highlight scholars from around the world working on AI ethics issues, the report also features two spotlights sharing the work of scholars operating in Singapore and Mexico helping to shape policy measures as they relate to the responsible use of technology. The report also has an extensive section covering the gamut of issues when it comes to the societal impacts of AI covering areas of bias, privacy, transparency, accountability, fairness, interpretability, disinformation, policymaking, law, regulations, and moral philosophy.