Recent AI-related scandals have shed a spotlight on accountability in AI, with increasing public interest and concern. This paper draws on literature from public policy and governance to make two contributions. First, we propose an AI accountability ecosystem as a useful lens on the system, with different stakeholders requiring and contributing to specific accountability mechanisms. We argue that the present ecosystem is unbalanced, with a need for improved transparency via AI explainability and adequate documentation and process formalisation to support internal audit, leading up eventually to external accreditation processes. Second, we use a case study in the gambling sector to illustrate in a subset of the overall ecosystem the need for industry-specific accountability principles and processes. We define and evaluate critically the implementation of key accountability principles in the gambling industry, namely addressing algorithmic bias and model explainability, before concluding and discussing directions for future work based on our findings. Keywords: Accountability, Explainable AI, Algorithmic Bias, Regulation.

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.

Algorithmic Decision Making (ADM) has permeated all aspects of society. Government organizations are also affected by this trend. However, the use of ADM has been getting negative attention from the public, media, and interest groups. There is little to no actionable guidelines for government organizations to create positive impact through ADM. In this case study, we examined eight municipal organizations in the Netherlands regarding their actual and intended use of ADM. We interviewed key personnel and decision makers. Our results show that municipalities mostly use ADM in an ad hoc manner, and they have not systematically defined or institutionalized a data science process yet. They operate risk averse, and they clearly express the need for cooperation, guidance, and even supervision at the national level. Third parties, mostly commercial, are often involved in the ADM development lifecycle, without systematic governance. Communication on the use of ADM is generally responsive to negative attention from the media and public. There are strong indications for the need of an ADM governance framework. In this paper, we present our findings in detail, along with actionable insights on governance, communication, and performance evaluation of ADM systems.

Topic models provide a useful text-mining tool for learning, extracting and discovering latent structures in large text corpora. Although a plethora of methods have been proposed for topic modeling, a formal theoretical investigation on the statistical identifiability and accuracy of latent topic estimation is lacking in the literature. In this paper, we propose a maximum likelihood estimator (MLE) of latent topics based on a specific integrated likelihood, which is naturally connected to the concept of volume minimization in computational geometry. Theoretically, we introduce a new set of geometric conditions for topic model identifiability, which are weaker than conventional separability conditions relying on the existence of anchor words or pure topic documents. We conduct finite-sample error analysis for the proposed estimator and discuss the connection of our results with existing ones. We conclude with empirical studies on both simulated and real datasets.

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).

Photographs and computer vision techniques using artificial intelligence are able to detect the presence of bats on bridges automatically with over 90% accuracy, according to our new study. More than 40 species of bats are found in the U.S., and many of them are endangered or threatened. Bats often nest by the hundreds or thousands underneath bridges, so transportation departments are required to survey for them before conducting repair or replacement projects. I conducted the recently published study with colleagues at the University of Virginia's MOB Lab in collaboration with the Virginia Transportation Research Council. Bridge surveys are important for protecting threatened and endangered bat species.

Ukraine said it will build a factory to produce Turkish armed drones that Kyiv previously bought to use against pro-Russian separatists in the east, a deal that might upset Kyiv's adversary Moscow. "A land plot on which the factory will be built has already been chosen," Ukrainian Foreign Minister Dmytro Kuleba said at a news conference on Thursday with Turkish counterpart Mevlut Cavusoglu in the western Ukrainian city of Lviv. "There were a number of obstacles to the implementation [of this project] but all of them have been removed," he added, without providing further details. Pleased to welcome my Turkish colleague and friend @MevlutCavusoglu in Lviv and expand our diplomatic geography. Cavusoglu did not speak specifically about the subject but stressed that Kyiv and Ankara were "in the process of strengthening their relations in many sectors", including defence.

The Post played the Beta on an Alienware laptop with an Intel Core i7-9700K CPU (3.60GHz) with 16 GB of RAM and an NVIDIA GeForce RTX 2070 graphics card. The game looks stunning, with rain swirling and foliage swaying in the wind. Dice emphasized in the preview briefing that the team had added a lot of fixes and polish that would not be seen in the beta, but still, there were a good number of bugs. Unseen forces would spastically yank the pixels of some dead bodies, helicopters struck by tank shells emerged unscathed, and the map's rocket centerpiece, which blew up on launch when shot by a tank, exploded, stopped and then exploded again.

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.