Group fairness ensures that the outcome of machine learning (ML) based decision making systems are not biased towards a certain group of people defined by a sensitive attribute such as gender or ethnicity. Achieving group fairness in Federated Learning (FL) is challenging because mitigating bias inherently requires using the sensitive attribute values of all clients, while FL is aimed precisely at protecting privacy by not giving access to the clients' data. As we show in this paper, this conflict between fairness and privacy in FL can be resolved by combining FL with Secure Multiparty Computation (MPC) and Differential Privacy (DP). In doing so, we propose a method for training group-fair ML models in cross-device FL under complete and formal privacy guarantees, without requiring the clients to disclose their sensitive attribute values. Empirical evaluations on real world datasets demonstrate the effectiveness of our solution to train fair and accurate ML models in federated cross-device setups with privacy guarantees to the users.

The ongoing 'digital transformation' fundamentally changes audit evidence's nature, recording, and volume. Nowadays, the International Standards on Auditing (ISA) requires auditors to examine vast volumes of a financial statement's underlying digital accounting records. As a result, audit firms also 'digitize' their analytical capabilities and invest in Deep Learning (DL), a successful sub-discipline of Machine Learning. The application of DL offers the ability to learn specialized audit models from data of multiple clients, e.g., organizations operating in the same industry or jurisdiction. In general, regulations require auditors to adhere to strict data confidentiality measures. At the same time, recent intriguing discoveries showed that large-scale DL models are vulnerable to leaking sensitive training data information. Today, it often remains unclear how audit firms can apply DL models while complying with data protection regulations. In this work, we propose a Federated Learning framework to train DL models on auditing relevant accounting data of multiple clients. The framework encompasses Differential Privacy and Split Learning capabilities to mitigate data confidentiality risks at model inference. We evaluate our approach to detect accounting anomalies in three real-world datasets of city payments. Our results provide empirical evidence that auditors can benefit from DL models that accumulate knowledge from multiple sources of proprietary client data.

AI systems can create, propagate, support, and automate bias in decision-making processes. To mitigate biased decisions, we both need to understand the origin of the bias and define what it means for an algorithm to make fair decisions. Most group fairness notions assess a model's equality of outcome by computing statistical metrics on the outputs. We argue that these output metrics encounter intrinsic obstacles and present a complementary approach that aligns with the increasing focus on equality of treatment. By Locating Unfairness through Canonical Inverse Design (LUCID), we generate a canonical set that shows the desired inputs for a model given a preferred output. The canonical set reveals the model's internal logic and exposes potential unethical biases by repeatedly interrogating the decision-making process. We evaluate LUCID on the UCI Adult and COMPAS data sets and find that some biases detected by a canonical set differ from those of output metrics. The results show that by shifting the focus towards equality of treatment and looking into the algorithm's internal workings, the canonical sets are a valuable addition to the toolbox of algorithmic fairness evaluation.

Federal researchers are looking for updates and feedback on a video analytics initiative regarding ethical technology development, as government agencies plan to incorporate more artificial intelligence research and systems into their operations. The Networking and Information Technology Research and Development Program, a federally-funded research organization that specializes in advanced information technology solutions, issued a request for comment on its updated guidance on federal computer vision and AI technology research. Originally published in March 2020, the NITRD's Federal Video and Image Analytics Research and Development Action Plan is open to public comments and suggestions on how to improve the plan's key pillars to bridge noted gaps in federal research investment, particularly surrounding the incorporation of responsible AI. "As we move into new and novel applications for technology, we must be cognizant of potential harm that AI and other technologies can bring into society, such as identifying and discriminating against certain individuals," a NITRD spokesperson told Nextgov. "The VIA Team seeks input from the public on potential revisions to the VIA R&D action plan to reflect changes in technology and the socio-technical environment--how humans and technology are interrelated in the workplace and in the broader society." The VIA team is particularly interested in revision suggestions that mitigate potential risks related to individual rights and privacy, while establishing "a foundation for human rights" belying AI implementation.

Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today. However, such efforts have coalesced around a small subset of languages, leaving behind the vast majority of mostly low-resource languages. What does it take to break the 200 language barrier while ensuring safe, high quality results, all while keeping ethical considerations in mind? In No Language Left Behind, we took on this challenge by first contextualizing the need for low-resource language translation support through exploratory interviews with native speakers. Then, we created datasets and models aimed at narrowing the performance gap between low and high-resource languages. More specifically, we developed a conditional compute model based on Sparsely Gated Mixture of Experts that is trained on data obtained with novel and effective data mining techniques tailored for low-resource languages. We propose multiple architectural and training improvements to counteract overfitting while training on thousands of tasks. Critically, we evaluated the performance of over 40,000 different translation directions using a human-translated benchmark, Flores-200, and combined human evaluation with a novel toxicity benchmark covering all languages in Flores-200 to assess translation safety. Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art, laying important groundwork towards realizing a universal translation system.

More than fifty years after a research group at Dartmouth University launched work on a new field called "Artificial Intelligence," the United States still lacks a national strategy on artificial intelligence (AI) policy. The growing urgency of this endeavor is made clear by the rapid progress of both U.S. allies and adversaries. The European Union is moving forward with two initiatives of far-reaching consequence. The EU Artificial Intelligence Act will establish a comprehensive, risk-based approach for the regulation of AI when it is adopted in 2023. Many anticipate that the EU AI Act will extend the "Brussels Effect" across the AI sector as the earlier European data privacy law, the General Data Privacy Regulation, did for much of the tech industry.

Another social media company is paying up due to Illinois' Biometric Information Privacy Act. Snap Inc. (the parent company of Snapchat) has reached a $35 million settlement in an Illinois class action lawsuit over its use of facial recognition technology. The lawsuit alleges that Snapchat violated the BIPA law by collecting and storing the biometric data of users who used its lenses and filters -- without their consent. Illinois residents who resided in the state after November 17th, 2015 and used Snapchat's popular AR features may be eligible for a cut of the settlement. Snap Inc. is only the latest company to get penalized under BIPA -- which requires companies to ask for consent before it can collect biometric data from users.

MACAU, August 24 - Rostam J Neuwirth, head of the Department of Global Legal Studies of the University of Macau (UM) Faculty of Law, has published a new book titled'The EU Artificial Intelligence Act: Regulating Subliminal AI Systems'. Through exploring legal, ethical, and scientific issues related to artificial intelligence (AI), the book aims to show how cognitive, technological, and legal questions are intrinsically interwoven and to stimulate a transdisciplinary and transnational global debate between students, academics, practitioners, policymakers, and citizens. The book has been published by the British publisher Routledge. It contextualises the future regulation of AI as proposed by the European Union, specifically addressing the regulatory challenges relating to the planned prohibition of the use of AI systems that deploy subliminal techniques to manipulate the human mind and alter human behaviour. Subliminal perception usually refers to perception received below the threshold of awareness, such as images flashed quickly before the eyes or background music embedded with hidden messages, and these external stimuli can affect people without their being aware of it. In this respect, Prof Neuwirth points out that the convergence of AI with various related technologies, such as brain–computer interfaces, functional magnetic resonance imaging, robotics, and big data, already allows for'mind reading' or'dream hacking' through brain spyware, as well as other practices that intrude on cognition and the right to freedom of thought.

CRIMINALS could be tracked and controlled via brain chip monitoring in the future, according to neurotechnology law experts. Legal theorists are preparing for a future with widespread use of brain chips and augmented humans. Neurotechnology is the field of outfitting electronic devices for integration with the nervous system. While war-gaming the possibilities of neurotechnology impacting the law, Dr Allan McCay theorized that the courts could force criminals to adopt microchips for monitoring or controlling behavior in a report for The Law Society. "The political conditions might emerge for seeing neurotechnology as a broader solution to crime might come into place," McCay wrote.

Artificial intelligence (AI) may be a distant and little-known subject for some people, but the reality is that it is much closer than many people believe. Through Artificial Intelligence, it is possible to combat violence against women; assist lawyers, law firms, and departments with document analysis and monitoring of changes in legislation; assist clients with financial matters; make farmers have better productivity; help the elderly to have a better quality of life, among thousands of other things. AI advancements will be no less significant. For example, AI will soon be able to accelerate drug discovery and green energy research. According to Andrew Ng, one of the world's leading AI experts, AI's advancement can add more than $10 trillion to the global economy by 2030. Many people fail to recognize that this isn't necessarily a bad thing or something to be afraid of.