Vosloo led the drafting of a new set of guidelines from Unicef designed to help governments and companies develop AI policies that consider children's needs. Released on September 16, the nine new guidelines are the culmination of several consultations held with policymakers, child development researchers, AI practitioners, and kids around the world. They also take into consideration the UN Convention on the Rights of the Child, a human rights treaty ratified in 1989. The guidelines aren't meant to be yet another set of AI principles, many of which already say the same things. In January of this year, a Harvard Berkman Klein Center review of 36 of the most prominent documents guiding national and company AI strategies found eight common themes--among them privacy, safety, fairness, and explainability.

Fox News senior judicial analyst Judge Andrew Napolitano weighs in on debate over USPS and mail-in voting. A U.S. judge on Thursday granted a request to temporarily block controversial Postal Service changes that have been accused of slowing mail nationwide, calling them "a politically motivated attack " ahead of the 2020 presidential election. Judge Stanley Bastian in Yakima, Wash., said he was issuing a nationwide preliminary injunction against the USPS sought by 14 states that sued the Trump administration and the Postal Service. The states, all led by Democratic attorneys general, challenged the Postal Service's so-called "leave behind" policy, where trucks have been leaving postal facilities on time regardless of whether there is more mail to load. They also sought to force the Postal Service to treat election mail as first-class mail.

New drone video captured the mess Hurricane Sally left behind on Alabama's coastline after tearing through early Wednesday morning with 105 mph winds, torrential rainfall and a powerful storm surge. The storm tore apart buildings, hurled boats and debris around, and dumped as much as 30 inches of rain in southern Alabama and the Florida Panhandle before downgrading to a tropical storm Wednesday. It continued to weaken to a post-tropical depression Thursday as it moved northeast. The wall of a residential high-rise building was sheared completely off in the storm, leaving multiple levels exposed, the video shows. Sally tore through bedrooms and furniture, leaving dresser drawers open and bedding unraveled, the video shows.

In environmental epidemiology, it is critically important to identify subpopulations that are most vulnerable to the adverse effects of air pollution so we can develop targeted interventions. In recent years, there have been many methodological developments for addressing heterogeneity of treatment effects in causal inference. A common approach is to estimate the conditional average treatment effect (CATE) for a pre-specified covariate set. However, this approach does not provide an easy-to-interpret tool for identifying susceptible subpopulations or discover new subpopulations that are not defined a priori by the researchers. In this paper, we propose a new causal rule ensemble (CRE) method with two features simultaneously: 1) ensuring interpretability by revealing heterogeneous treatment effect structures in terms of decision rules and 2) providing CATE estimates with high statistical precision similar to causal machine learning algorithms. We provide theoretical results that guarantee consistency of the estimated causal effects for the newly discovered causal rules. Furthermore, via simulations, we show that the CRE method has competitive performance on its ability to discover subpopulations and then accurately estimate the causal effects. We also develop a new sensitivity analysis method that examine robustness to unmeasured confounding bias. Lastly, we apply the CRE method to the study of the effects of long-term exposure to air pollution on the 5-year mortality rate of the New England Medicare-enrolled population in United States. Code is available at https://github.com/kwonsang/causal_rule_ensemble.

This paper presents TotalBotWar, a new pseudo real-time multi-action challenge for game AI, as well as some initial experiments that benchmark the framework with different agents. The game is based on the real-time battles of the popular TotalWar games series where players manage an army to defeat the opponent's one. In the proposed game, a turn consists of a set of orders to control the units. The number and specific orders that can be performed in a turn vary during the progression of the game. One interesting feature of the game is that if a particular unit does not receive an order in a turn, it will continue performing the action specified in a previous turn. The turn-wise branching factor becomes overwhelming for traditional algorithms and the partial observability of the game state makes the proposed game an interesting platform to test modern AI algorithms.

Decisions made by machine learning systems have increasing influence on the world, yet it is common for machine learning algorithms to assume that no such influence exists. An example is the use of the i.i.d. assumption in content recommendation. In fact, the (choice of) content displayed can change users' perceptions and preferences, or even drive them away, causing a shift in the distribution of users. We introduce the term auto-induced distributional shift (ADS) to describe the phenomenon of an algorithm causing a change in the distribution of its own inputs. Our goal is to ensure that machine learning systems do not leverage ADS to increase performance when doing so could be undesirable. We demonstrate that changes to the learning algorithm, such as the introduction of meta-learning, can cause hidden incentives for auto-induced distributional shift (HI-ADS) to be revealed. To address this issue, we introduce `unit tests' and a mitigation strategy for HI-ADS, as well as a toy environment for modelling real-world issues with HI-ADS in content recommendation, where we demonstrate that strong meta-learners achieve gains in performance via ADS. We show meta-learning and Q-learning both sometimes fail unit tests, but pass when using our mitigation strategy.

In an increasingly autonomous manner AI systems make decisions impacting our daily life. Their actions might cause accidents, harm or, more generally, violate regulations -- either intentionally or not. Thus, AI systems might be considered suspects for various events. Therefore, it is essential to relate particular events to an AI, its owner and its creator. Given a multitude of AI systems from multiple manufactures, potentially, altered by their owner or changing through self-learning, this seems non-trivial. This paper discusses how to identify AI systems responsible for incidents as well as their motives that might be "malicious by design". In addition to a conceptualization, we conduct two case studies based on reinforcement learning and convolutional neural networks to illustrate our proposed methods and challenges. Our cases illustrate that "catching AI systems" seems often far from trivial and requires extensive expertise in machine learning. Legislative measures that enforce mandatory information to be collected during operation of AI systems as well as means to uniquely identify systems might facilitate the problem.

This paper reviews the current state of the art in Artificial Intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically Machine Learning (ML) algorithms, is provided including Convolutional Neural Network (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement Learning (DRL). We categorise creative applications into five groups related to how AI technologies are used: i) content creation, ii) information analysis, iii) content enhancement and post production workflows, iv) information extraction and enhancement, and v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, machine learning-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of machine learning in domains with fewer constraints, where AI is the `creator', remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human centric -- where it is designed to augment, rather than replace, human creativity.

Imagine a scenario where we are trying to detect a shooting using microphones deployed in a city: shooting is a situation of interest that we want to identify from a high-throughput (audio) data stream. Complex Event Processing (CEP) is a type of approach aimed at detecting such situations of interest, called complex events, from a data stream using a set of rules. These rules are defined on atomic pieces of information from the data stream, which we call events--or simple events, for clarity. Complex events can be formed from multiple simple events. For instance, shooting might start when multiple instances of the simple event gunshot occur. For simplicity, we can assume that when we start to detect siren events, authorities have arrived and the situation is being dealt with, which would conclude the complex event. Using the raw data stream implies that usually we cannot directly write declarative rules on that data, as it would imply that we need to process that raw data using symbolic rules; though theoretically possible, this is hardly recommended. Using a machine learning algorithm such a neural network trained with back-propagation is also infeasible, as it will need to simultaneously learn to understand the simple events within the data stream, and the interrelationship between such events to compose a complex event. While possible, the sparsity of data makes this a hard problem to solve.

Motivated by high-stakes decision-making domains like personalized medicine where user information is inherently sensitive, we design privacy preserving exploration policies for episodic reinforcement learning (RL). We first provide a meaningful privacy formulation using the notion of joint differential privacy (JDP)--a strong variant of differential privacy for settings where each user receives their own sets of output (e.g., policy recommendations). We then develop a private optimism-based learning algorithm that simultaneously achieves strong PAC and regret bounds, and enjoys a JDP guarantee. Our algorithm only pays for a moderate privacy cost on exploration: in comparison to the non-private bounds, the privacy parameter only appears in lower-order terms. Finally, we present lower bounds on sample complexity and regret for reinforcement learning subject to JDP.