The adoption of automated, data-driven decision making in an ever expanding range of applications has raised concerns about its potential unfairness towards certain social groups. In this context, a number of recent studies have focused on defining, detecting, and removing unfairness from data-driven decision systems. However, the existing notions of fairness, based on parity (equality) in treatment or outcomes for different social groups, tend to be quite stringent, limiting the overall decision making accuracy. In this paper, we draw inspiration from the fairdivision and envy-freeness literature in economics and game theory and propose preference-based notions of fairness--given the choice between various sets of decision treatments or outcomes, any group of users would collectively prefer its treatment or outcomes, regardless of the (dis)parity as compared to the other groups. Then, we introduce tractable proxies to design margin-based classifiers that satisfy these preference-based notions of fairness. Finally, we experiment with a variety of synthetic and real-world datasets and show that preference-based fairness allows for greater decision accuracy than parity-based fairness.

We introduce a novel framework for statistical analysis of populations of nondegenerate Gaussian processes (GPs), which are natural representations of uncertain curves. This allows inherent variation or uncertainty in function-valued data to be properly incorporated in the population analysis.

One of California's new AI laws, which aims to prevent AI deepfakes related to elections from spreading online, has been blocked a month before the US presidential elections. As TechCrunch and Reason report, Judge John Mendez has issued a preliminary injunction, preventing the state's attorney general from enforcing AB 2839. California Governor Gavin Newsom signed it into law, along with other bills focusing on AI, back in mid-September. "I just signed a bill to make this illegal in the state of California," he wrote. I just signed a bill to make this illegal in the state of California.

Evaluating a policy by deploying it in the real world can be risky and costly. Off-policy policy evaluation (OPE) algorithms use historical data collected from running a previous policy to evaluate a new policy, which provides a means for evaluating a policy without requiring it to ever be deployed. Importance sampling is a popular OPE method because it is robust to partial observability and works with continuous states and actions. However, the amount of historical data required by importance sampling can scale exponentially with the horizon of the problem: the number of sequential decisions that are made. We propose using policies over temporally extended actions, called options, and show that combining these policies with importance sampling can significantly improve performance for long-horizon problems. In addition, we can take advantage of special cases that arise due to options-based policies to further improve the performance of importance sampling. We further generalize these special cases to a general covariance testing rule that can be used to decide which weights to drop in an IS estimate, and derive a new IS algorithm called Incremental Importance Sampling that can provide significantly more accurate estimates for a broad class of domains.

Officer Wendy Venegas spoke softly in Spanish to the 14-year-old standing on the side of a narrow residential road in East Palo Alto. The girl's face was puffy from crying as she quietly explained what had happened. The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. The girl said her father had caught her and her boyfriend "doing stuff" that morning, and her dad had either struck or pushed the boy, Venegas later explained.

'LEFT' IN LIMBO – Biden administration accused of burying Americans' voting concerns. 'THE ANSWER IS…' – Biden's response on whether America will back Israel if it strikes Iran. CHECK THE RECEIPT – Vance fulfills debate pledge after moderator asks him to explain'what evidence' he has. 'MINI-CIRCUS' – Diddy party photographer drops disturbing revelations about kids at risqué gatherings. 'COMPLEX' CASE – Prosecutors in trial for Trump's suspected would-be assassin throw major curveball.

Then detection and identification of extreme weather events in large-scale climate simulations is an important problem for risk management, informing governmental policy decisions and advancing our basic understanding of the climate system. Recent work has shown that fully supervised convolutional neural networks (CNNs) can yield acceptable accuracy for classifying well-known types of extreme weather events when large amounts of labeled data are available. However, many different types of spatially localized climate patterns are of interest including hurricanes, extra-tropical cyclones, weather fronts, and blocking events among others. Existing labeled data for these patterns can be incomplete in various ways, such as covering only certain years or geographic areas and having false negatives. This type of climate data therefore poses a number of interesting machine learning challenges. We present a multichannel spatiotemporal CNN architecture for semi-supervised bounding box prediction and exploratory data analysis. We demonstrate that our approach is able to leverage temporal information and unlabeled data to improve the localization of extreme weather events. Further, we explore the representations learned by our model in order to better understand this important data. We present a dataset, ExtremeWeather, to encourage machine learning research in this area and to help facilitate further work in understanding and mitigating the effects of climate change.

Caloza was once a community organizer for President Obama and a Los Angeles Board of Public Works commissioner. She also served in the Obama administration's Department of Education and as a staffer to former L.A. Mayor Eric Garcetti. Her main priorities are protecting reproductive health and access to abortion by fully funding Planned Parenthood and making it easier for the organization to open more locations across California. She also plans to focus on how artificial intelligence is replacing jobs and making sure public education in the state is fully funded. The presidential race between Democratic Vice President Kamala Harris and Republican former President Trump is at the top of the ticket, but Californians will vote on a number of other races.

Structured prediction methods have been remarkably successful in learning mappings between input observations and output configurations [1; 2; 3]. The central guiding formulation involves learning a scoring function that recovers the configuration as the highest scoring assignment. In contrast, in a game theoretic setting, myopic strategic interactions among players lead to a Nash equilibrium or locally optimal configuration rather than highest scoring global configuration. Learning games therefore involves, at best, enforcement of local consistency constraints as recently advocated [4].

Drone operators from the private sector who volunteered to fly unmanned vehicles to the disaster-hit Noto Peninsula in Ishikawa Prefecture following January's magnitude 7.6 earthquake returned to the region last week after record-breaking rainfall caused severe floods and cut off access to remote communities. The Japan UAS Industrial Development Association (JUIDA), an industry body of drone companies, was commissioned by the Self-Defense Forces to carry out multiple missions to transport bread, vegetable juice, milk, and cooked and dry-packed rice to people in areas that had been cut off. Other operators that took part in the effort included KDDI SmartDrone, which was directly commissioned by the prefecture to fly drones over wrecked roads to snap images that would help assess the damage caused by landslides.