Synthetic media technologies are rapidly advancing, making it easier to generate nonveridical media that look and sound increasingly realistic. So-called "deepfakes" (owing to their reliance on deep learning) often present a person saying or doing something they have not said or done. The proliferation of deepfakesa creates a new challenge to the trustworthiness of visual experience, and has already created negative consequences such as nonconsensual pornography,11 political disinformation,19 and financial fraud.3 Deepfakes can harm viewers by deceiving or intimidating, harm subjects by causing reputational damage, and harm society by undermining societal values such as trust in institutions.7 What can be done to mitigate these harms?

In January 2020, Robert Williams of Farmington Hills, MI, was arrested at his home by the Detroit Police Department. He was photographed, fingerprinted, had his DNA taken, and was then locked up for 30 hours. He had not committed one; a facial recognition system operated by the Michigan State Police had wrongly identified him as the thief in a 2018 store robbery. However, Williams looked nothing like the perpetrator captured in the surveillance video, and the case was dropped. Rewind to May 2019, when Detroit resident Michael Oliver was arrested after being identified by the very same police facial recognition unit as the person who stole a smartphone from a vehicle.

Seeking to call into question the mental acuity of his opponent, Donald Trump looked across the presidential debate stage at Joseph Biden and said, "So you said you went to Delaware State, but you forgot the name of your college. Biden chuckled, but viewers may have been left wondering: did the former vice president misstate where he went to school? Those who viewed the debate live on an app from the London-based company Logically were quickly served an answer: the president's assertion was false. A brief write-up posted on the company's website the next morning provided links to other fact-checks from National Public Radio and the Delaware News Journal on the same claim, which explain that Biden actually said his first Senate campaign received a boost from students at the school. Logically is one of a number of efforts, both commercial and academic, to apply techniques of artificial intelligence (AI), including machine learning and natural language processing (NLP), to identify false ...

LOS ANGELES – NASA scientists on Monday unveiled first-of-a-kind home movies from last week's daredevil Mars rover landing, vividly showing its supersonic parachute inflation over the red planet and a rocket-powered hovercraft lowering the science lab on wheels to the surface. The footage was recorded on Thursday by a series of cameras mounted at different angles of the multistage spacecraft as it carried the rover, named Perseverance, through the thin Martian atmosphere to a gentle touchdown inside a vast basin called Jezero Crater. Thomas Zurbuchen, NASA associate administrator for science, called seeing the footage "the closest you can get to landing on Mars without putting on a pressure suit." The video montage was played for reporters tuning in to a news briefing webcast from NASA's Jet Propulsion Laboratory (JPL) near Los Angeles four days after the historic landing of the most advanced astrobiology probe ever sent to another world. NASA also presented a brief audio clip captured by microphones on the rover after its arrival that included the murmur of a light wind gust -- the first ever recorded on the fourth planet from the sun.

A United Kingdom court dropped Epic Games' suit against Apple and its request to make the tech giant reinstate its popular video game "Fortnite" into the App Store. In the meantime, sorry, Apple users, you are still shut out from playing "Fortnite" with your friends who are blasting away on PlayStations and Xboxes, for instance. The U.S., where Epic Games and Apple are headquartered, would be "the appropriate forum" for the cases to be tried, said Judge Justice Roth of the Competition Appeal Tribunal in a ruling Monday. This legal battle, which began in August 2020 when Epic offered a direct payment method for Fortnite mobile players, spans the globe. Last week, Epic Games filed an antitrust complaint against Apple in the European Union.

When assessing causal effects, determining the target population to which the results are intended to generalize is a critical decision. Randomized and observational studies each have strengths and limitations for estimating causal effects in a target population. Estimates from randomized data may have internal validity but are often not representative of the target population. Observational data may better reflect the target population, and hence be more likely to have external validity, but are subject to potential bias due to unmeasured confounding. While much of the causal inference literature has focused on addressing internal validity bias, both internal and external validity are necessary for unbiased estimates in a target population. This paper presents a framework for addressing external validity bias, including a synthesis of approaches for generalizability and transportability, the assumptions they require, as well as tests for the heterogeneity of treatment effects and differences between study and target populations.

We advocate the development of a discipline of interacting with and extracting information from models, both mathematical (e.g. game-theoretic ones) and computational (e.g. agent-based models). We outline some directions for the development of a such a discipline: - the development of logical frameworks for the systematic formal specification of stylized facts and social mechanisms in (mathematical and computational) social science. Such frameworks would bring to attention new issues, such as phase transitions, i.e. dramatical changes in the validity of the stylized facts beyond some critical values in parameter space. We argue that such statements are useful for those logical frameworks describing properties of ABM. - the adaptation of tools from the theory of reactive systems (such as bisimulation) to obtain practically relevant notions of two systems "having the same behavior". - the systematic development of an adversarial theory of model perturbations, that investigates the robustness of conclusions derived from models of social behavior to variations in several features of the social dynamics. These may include: activation order, the underlying social network, individual agent behavior.

Corruption continues to be one of the biggest societal challenges of our time. New hope is placed in Artificial Intelligence (AI) to serve as an unbiased anti-corruption agent. Ever more available (open) government data paired with unprecedented performance of such algorithms render AI the next frontier in anti-corruption. Summarizing existing efforts to use AI-based anti-corruption tools (AI-ACT), we introduce a conceptual framework to advance research and policy. It outlines why AI presents a unique tool for top-down and bottom-up anti-corruption approaches. For both approaches, we outline in detail how AI-ACT present different potentials and pitfalls for (a) input data, (b) algorithmic design, and (c) institutional implementation. Finally, we venture a look into the future and flesh out key questions that need to be addressed to develop AI-ACT while considering citizens' views, hence putting "society in the loop".

Real world applications such as economics and policy making often involve solving multi-agent games with two unique features: (1) The agents are inherently asymmetric and partitioned into leaders and followers; (2) The agents have different reward functions, thus the game is general-sum. The majority of existing results in this field focuses on either symmetric solution concepts (e.g. Nash equilibrium) or zero-sum games. It remains vastly open how to learn the Stackelberg equilibrium -- an asymmetric analog of the Nash equilibrium -- in general-sum games efficiently from samples. This paper initiates the theoretical study of sample-efficient learning of the Stackelberg equilibrium in two-player turn-based general-sum games. We identify a fundamental gap between the exact value of the Stackelberg equilibrium and its estimated version using finite samples, which can not be closed information-theoretically regardless of the algorithm. We then establish a positive result on sample-efficient learning of Stackelberg equilibrium with value optimal up to the gap identified above. We show that our sample complexity is tight with matching upper and lower bounds. Finally, we extend our learning results to the setting where the follower plays in a Markov Decision Process (MDP), and the setting where the leader and the follower act simultaneously.

The US Army is developing its most powerful laser yet that is a million times more powerful than current systems. Most laser weapons fire a continuous beam until a target melts or catches fire, but the Tactical Ultrashort Pulsed Laser (UPSL) for Army Platforms will emit short, pulse-like bursts. Its being designed to reach a terawatt for a brief 200 femtoseconds, which is one quadrillionth of a second, compared to the 150-kilowatt maximum of current systems. It's also thought such a burst would disrupt nearby electronics systems, making it a functional electromagnetic pulse (EMP). The US military group is aiming to have a working prototype by August 2022.