Use of artificial intelligence is growing and expanding into applications that impact people's lives. People trust their technology without really understanding it or its limitations. There is the potential for harm and we are already seeing examples of that in the world. AI researchers have an obligation to consider the impact of intelligent applications they work on. While the ethics of AI is not clear-cut, there are guidelines we can consider to minimize the harm we might introduce.

Disney Parks have reportedly been urged to overhaul the theme of its famous Splash Mountain ride. The park's popular log flume ride has recently been getting calls for Disney to alter its whole attraction motif. In the wake of the protests against police brutality and racial injustices and amid the ongoing Black Lives Matter movement, fans and parkgoers have claimed that Disney's Splash Mountain is based on the 1946 Disney film "Song of the South" -- which has been dubbed one of the "most racist movies" for its stereotypes of black people. According to a report from CNN, the controversial live-action animated musical film has long been criticized for romanticizing the post-Civil War period in South America. "Song of the South" is set in a plantation in Georgia in the 1800s during the Reconstruction era.

Video conferencing software maker Zoom shut down the account of Chinese activist Zhou Fengsuo at the behest of the Chinese government. The account was closed because Zhou, and other activists, held a digital event commemorating the Tienanmen Square Massacre. The Tienanmen Square protests were a student movement for democratic rights in the country set against mass privatisation and neoliberal globalism enacted by Deng Xiaoping, according to historian and participant in the 1989 protest Wang Hui. Thousands of people were killed and wounded when, in what came to be known as the Tienanmen Square Massacre. Zhou had paid for a Zoom account associated with the U.S. nonprofit Humanitarian China.

Just two weeks ago, facial recognition technology seemed unstoppable. At the beginning of this year, for instance, news reports cast a light on the secretive company Clearview AI, which scraped social media sites for photos to build a database of more than more than 3 billion photos, sold to law enforcement. Then came a sea change: On Monday, in a letter to Congress, IBM announced it would stop the sale of "general purpose" facial recognition software. On Wednesday, Amazon announced a one-year moratorium on police use of its Rekognition technology by law enforcement, inviting Congress to "put in place stronger regulations to govern the ethical use" of the technology. Amazon in its statement said that, "Congress appears ready to take on this challenge," referring to the mounting pressure to make fundamental changes to U.S. law enforcement following the killing of George Floyd by the Minneapolis police, and law enforcement's heavy-handed and violent response to the Black Lives Matter protests.

On Wednesday, in a brief blog post, Amazon made a surprising announcement: that it would implement a one-year moratorium on police use of its facial recognition service, Rekognition. The post did not mention the furious nationwide demand for reform in response to the killings of George Floyd, Breonna Taylor, and too many other Black people. But it did cite developments "in recent days" indicating that Congress seemed prepared to implement "stronger regulations to govern the ethical use of facial recognition technology"--regulations that Amazon claims to be advocating for and ready to help shape in the coming year. But Amazon's sudden commitment to ostensibly transformative reform should be taken with a grain of salt hefty enough to unseat a Confederate monument from its rock-solid base. Americans won't receive the privacy and civil rights protections they need because a company like Amazon decides to give them to us.

Microsoft Corp. said it won't sell facial-recognition technology to U.S. police until there is a national law regulating its use, echoing similar commitments from Amazon.com Inc. and International Business Machines Corp. made this week. The trio of technology companies have called for clearer federal rules around the surveillance technology amid widespread concern about its potential for racial bias. Meanwhile, the popular fantasy card game, "Magic: The Gathering," removed several cards it deemed racist or culturally offensive from its database, including one depicting figures in pointed hoods. The Hasbro-subsidiary behind the game also pledged to review all cards for material deemed inappropriate. The moves are the latest public actions by businesses lining up to show their commitment to racial equality.

Federated Learning (FL) is very appealing for its privacy benefits: essentially, a global model is trained with updates computed on mobile devices while keeping the data of users local. Standard FL infrastructures are however designed to have no energy or performance impact on mobile devices, and are therefore not suitable for applications that require frequent (online) model updates, such as news recommenders. This paper presents FLeet, the first Online FL system, acting as a middleware between the Android OS and the machine learning application. FLeet combines the privacy of Standard FL with the precision of online learning thanks to two core components: (i) I-Prof, a new lightweight profiler that predicts and controls the impact of learning tasks on mobile devices, and (ii) AdaSGD, a new adaptive learning algorithm that is resilient to delayed updates. Our extensive evaluation shows that Online FL, as implemented by FLeet, can deliver a 2.3x quality boost compared to Standard FL, while only consuming 0.036% of the battery per day. I-Prof can accurately control the impact of learning tasks by improving the prediction accuracy up to 3.6x (computation time) and up to 19x (energy). AdaSGD outperforms alternative FL approaches by 18.4% in terms of convergence speed on heterogeneous data.

This paper describes an engine to optimize web publisher revenues from second-price auctions. These auctions are widely used to sell online ad spaces in a mechanism called real-time bidding (RTB). Optimization within these auctions is crucial for web publishers, because setting appropriate reserve prices can significantly increase revenue. We consider a practical real-world setting where the only available information before an auction occurs consists of a user identifier and an ad placement identifier. The real-world challenges we had to tackle consist mainly of tracking the dependencies on both the user and placement in an highly non-stationary environment and of dealing with censored bid observations. These challenges led us to make the following design choices: (i) we adopted a relatively simple non-parametric regression model of auction revenue based on an incremental time-weighted matrix factorization which implicitly builds adaptive users' and placements' profiles; (ii) we jointly used a non-parametric model to estimate the first and second bids' distribution when they are censored, based on an on-line extension of the Aalen's Additive model. Our engine is a component of a deployed system handling hundreds of web publishers across the world, serving billions of ads a day to hundreds of millions of visitors. The engine is able to predict, for each auction, an optimal reserve price in approximately one millisecond and yields a significant revenue increase for the web publishers.

As machine learning becomes more pervasive, the urgency of assuring its fairness increases. Consider training data that capture the behaviour of multiple subgroups of some underlying population over time. When the amounts of training data for the subgroups are not controlled carefully, under-representation bias may arise. We introduce two natural concepts of subgroup fairness and instantaneous fairness to address such under-representation bias in forecasting problems. In particular, we consider the learning of a linear dynamical system from multiple trajectories of varying lengths, and the associated forecasting problems. We provide globally convergent methods for the subgroup-fair and instant-fair estimation using hierarchies of convexifications of non-commutative polynomial optimisation problems. We demonstrate both the beneficial impact of fairness considerations on the statistical performance and the encouraging effects of exploiting sparsity on the estimators' run-time in our computational experiments.

Federated learning (FL) is an emerging distributed machine learning paradigm that stands out with its inherent privacy-preserving advantages. Heterogeneity is one of the core challenges in FL, which resides in the diverse user behaviors and hardware capacity across devices who participate in the training. Heterogeneity inherently exerts a huge influence on the FL training process, e.g., causing device unavailability. However, existing FL literature usually ignores the impacts of heterogeneity. To fill in the knowledge gap, we build FLASH, the first heterogeneity-aware FL platform. Based on FLASH and a large-scale user trace from 136k real-world users, we demonstrate the usefulness of FLASH in anatomizing the impacts of heterogeneity in FL by exploring three previously unaddressed research questions: whether and how can heterogeneity affect FL performance; how to configure a heterogeneity-aware FL system; and what are heterogeneity's impacts on existing FL optimizations. It shows that heterogeneity causes nontrivial performance degradation in FL from various aspects, and even invalidates some typical FL optimizations.