What would the inputs be to a machine whose output is the destabilization of a robust democracy, or whose emanations could disrupt the political power of nations? In the recent essay "The Coming AI Hackers," Schneier (2021) proposed a future application of artificial intelligences to discover, manipulate, and exploit vulnerabilities of social, economic, and political systems at speeds far greater than humans' ability to recognize and respond to such threats. This work advances the concept by applying to it theory from machine learning, hypothesizing some possible "featurization" (input specification and transformation) frameworks for AI hacking. Focusing on the political domain, we develop graph and sequence data representations that would enable the application of a range of deep learning models to predict attributes and outcomes of political systems. We explore possible data models, datasets, predictive tasks, and actionable applications associated with each framework. We speculate about the likely practical impact and feasibility of such models, and conclude by discussing their ethical implications.

Recent research on fair regression focused on developing new fairness notions and approximation methods as target variables and even the sensitive attribute are continuous in the regression setting. However, all previous fair regression research assumed the training data and testing data are drawn from the same distributions. This assumption is often violated in real world due to the sample selection bias between the training and testing data. In this paper, we develop a framework for fair regression under sample selection bias when dependent variable values of a set of samples from the training data are missing as a result of another hidden process. Our framework adopts the classic Heckman model for bias correction and the Lagrange duality to achieve fairness in regression based on a variety of fairness notions. Heckman model describes the sample selection process and uses a derived variable called the Inverse Mills Ratio (IMR) to correct sample selection bias. We use fairness inequality and equality constraints to describe a variety of fairness notions and apply the Lagrange duality theory to transform the primal problem into the dual convex optimization. For the two popular fairness notions, mean difference and mean squared error difference, we derive explicit formulas without iterative optimization, and for Pearson correlation, we derive its conditions of achieving strong duality. We conduct experiments on three real-world datasets and the experimental results demonstrate the approach's effectiveness in terms of both utility and fairness metrics.

Probing or fine-tuning (large-scale) pre-trained models results in state-of-the-art performance for many NLP tasks and, more recently, even for computer vision tasks when combined with image data. Unfortunately, these approaches also entail severe risks. In particular, large image datasets automatically scraped from the web may contain derogatory terms as categories and offensive images, and may also underrepresent specific classes. Consequently, there is an urgent need to carefully document datasets and curate their content. Unfortunately, this process is tedious and error-prone. We show that pre-trained transformers themselves provide a methodology for the automated curation of large-scale vision datasets. Based on human-annotated examples and the implicit knowledge of a CLIP based model, we demonstrate that one can select relevant prompts for rating the offensiveness of an image. Deep learning models yielded many improvements in several fields. Particularly, transfer learning from models pre-trained on large-scale supervised data has become common practice in many tasks both with and without sufficient data to train deep learning models. While approaches like semisupervised sequence learning (Dai & Le, 2015) and datasets such as ImageNet (Deng et al., 2009), especially the ImageNet-ILSVRC-2012 dataset with 1.2 million images, established pre-training approaches, in the following years, the training data size increased rapidly to billions of training examples (Brown et al., 2020; Jia et al., 2021), steadily improving the capabilities of deep models.

Patent offices and courts around the world are being asked to tackle a similar question: can an artificial intelligence system qualify as an inventor for a patent? A test case making its way through several countries--from Saudi Arabia to Australia to Brazil--has spurred debate about advancements in artificial intelligence technology and questions about whether patent laws need to be revised to recognize machines as inventors. A judge in the U.S. District Court for the Eastern District of Virginia recently ruled that, under current U.S. law, AI can't be listed as an inventor on a patent. The ruling was in line with what U.S., British, and EU patent officials have concluded. The push to recognize AI as an inventor comes from Ryan Abbott, a University of Surrey law professor, and Stephen Thaler, a computer scientist from Missouri.

Using both primary and secondary sources, I discuss the role of espionage in early nuclear history. Nuclear weapons are analogous to AI in many ways, so this period may hold lessons for AI governance. Nuclear spies successfully transferred information about the plutonium implosion bomb design and the enrichment of fissile material. Spies were mostly ideologically motivated. Counterintelligence was hampered by its fragmentation across multiple agencies and its inability to be choosy about talent used on the most important military research program in the largest war in human history. Nuclear espionage most likely sped up Soviet nuclear weapons development, but the Soviet Union would have been capable of developing nuclear weapons within a few years without spying. The slight gain in speed due to spying may nevertheless have been strategically significant. Acknowledgements: I am grateful to Matthew Gentzel for supervising this project and Michael Aird, Christina Barta, Daniel Filan, Aaron Gertler, Sidney Hough, Nat Kozak, Jeffery Ohl, and Waqar Zaidi for providing comments. This research was supported by a fellowship from the Stanford Existential Risks Initiative. This post is a short version of the report, x-posted from EA Forum. The full version with additional sections, an appendix, and a bibliography, is available here. The early history of nuclear weapons is in many ways similar to hypothesized future strategic situations involving advanced artificial intelligence (Zaidi and Dafoe 2021, 4). And, in addition to the objective similarity of the situations, the situations may be made more similar by deliberate imitation of the Manhattan Project experience (see this report to the US House Armed Service Committee).

In a recently published paper, a pair of academics propose that the application of artificial intelligence can offer a potent weapon against antitrust behavior in the Big Tech sector. This is the very industry that has advanced this technology, noted one of those academics, Giovana Massarotto, a Center for Technology, Innovation and Competition academic fellow at the University of Pennsylvania Carey Law School and an adjunct professor at the University of Iowa. She underscored this fact in an article for Bloomberg Law, in which she maintains that "the present economic democracy propaganda against Big Tech is not the solution to increase competition in fast-moving technology markets." In fact, she says, the industry's ingenuity is needed to achieve our nation's pro-competition goals. Massarotto and University of Liege (Belgium) Associate Professor Ashwin Ittoo write about their "antitrust machine learning application" (AML) which shows the potential for AI to "assist antitrust agencies in detecting anticompetitive practices faster."

The European Commission announced first-of-its-kind legislation regulating the use of artificial intelligence in April. This unleashed criticism that the regulations could slow AI innovation, hamstringing Europe in its competition with the U.S. and China for leadership in AI. For example, Andrew McAfee wrote an article titled "EU proposals to regulate AI are only going to hinder innovation." Anticipating this criticism and mindful of the example of GDPR, where Europe's thought-leadership position didn't necessarily translate into data-related innovation, the EC has tried to address AI innovation directly by publishing a new Coordinated Plan on AI. Released in conjunction with the proposed regulations, the plan is full of initiatives intended to help the EU become a leader in AI technology.

Earlier this month, a federal district court issued the first judicial decision in the country addressing whether an AI system can be an "inventor" under U.S. patent law. The decision was rendered by the U.S. District Court for the Eastern District of Virginia in Thaler v. Hirshfeld on appeal from the U.S. Patent and Trademark Office's (USPTO) decision that refused to allow Thaler's two patent applications to proceed because he listed DABUS (an AI machine) as the inventor. Thaler filed the applications in 2018--one for an invention used to contain food and the other for a flashing beacon for attracting attention in emergencies. In statements filed in support of the applications, Thaler listed DABUS as the inventor, claiming that he had acquired the right to the grant of the patents by "ownership of the creativity machine." In affirming the USPTO's denial of the applications, the court held that based on the plain statutory language of the U.S. Patent Act and Federal Circuit authority, an AI machine cannot be an inventor because an inventor must be an "individual," which under common interpretation and court precedent means a natural person. The court stated that Thaler's argument was based on policy considerations and the purpose of the patent clause of the U.S. Constitution, and that the decision to expand the scope of inventorship is squarely within the authority of Congress.

The European Commission announced first-of-its-kind legislation regulating the use of artificial intelligence in April. This unleashed criticism that the regulations could slow AI innovation, hamstringing Europe in its competition with the U.S. and China for leadership in AI. For example, Andrew McAfee wrote an article titled "EU proposals to regulate AI are only going to hinder innovation." Anticipating this criticism and mindful of the example of GDPR, where Europe's thought-leadership position didn't necessarily translate into data-related innovation, the EC has tried to address AI innovation directly by publishing a new Coordinated Plan on AI. Released in conjunction with the proposed regulations, the plan is full of initiatives intended to help the EU become a leader in AI technology.

Customer experience will drive your bottom line, and AI will help you win that race. In the meanwhile, privacy is becoming the key to keeping those wins. In today's era of digital business customer experience is that sole factor that distinguishes your service from your competitors'. When McKinsey had forecasted that the era of hyper-personalisation is dawning upon us, Gartner had published multiple reports within a span of a year, remarking on the rising value of AI - after all, great, personalised experiences inevitably leverage AI across multiple functions, achieving astonishing results in the process. At the same time, legislations were setting new benchmarks for the cost of privacy breaches. In 2021, Amazon was slammed with a €746m fine for non-compliance with the General Data Protection Regulation (GDPR).