A former Marine from Orange County pleaded guilty Monday to a federal stalking charge after he ran a lengthy harassment campaign against a professional gamer from Canada, according to authorities. Evan Baltierra, 29, of Trabuco Canyon faces up to five years in prison, according to the U.S. attorney's office for the Central District of California. Baltierra moderated the gamer's online channel, where she streamed live video of herself playing the popular multiplayer online role-playing game "World of Warcraft," after she and Baltierra met online, according to charging documents filed in federal court. They first met in person at the BlizzCon convention in Anaheim in November 2019, where the victim held a meet-and-greet event with her fans, according to the documents. Baltierra asked the woman to be his "valentine" online, but she refused because she was in a relationship.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Rob Reich wears many hats: political philosopher, director of the McCoy Family Center for Ethics in Society, and associate director of the Stanford Institute for Human-Centered Artificial Intelligence. In recent years, Reich has delved deeply into the ethical and political issues posed by revolutionary technological advances in artificial intelligence (AI). His work is not always easy for technologists to hear. In his book, System Error: Where Big Tech Went Wrong and How We Can Reboot, Reich and his co-authors (computer scientist Mehran Sahami and social scientist Jeremy M. Weinstein) argued that tech companies and developers are so fixated on "optimization" that they often trample on human values.

Recent work highlights the role of causality in designing equitable decision-making algorithms. It is not immediately clear, however, how existing causal conceptions of fairness relate to one another, or what the consequences are of using these definitions as design principles. Here, we first assemble and categorize popular causal definitions of algorithmic fairness into two broad families: (1) those that constrain the effects of decisions on counterfactual disparities; and (2) those that constrain the effects of legally protected characteristics, like race and gender, on decisions. We then show, analytically and empirically, that both families of definitions \emph{almost always} -- in a measure theoretic sense -- result in strongly Pareto dominated decision policies, meaning there is an alternative, unconstrained policy favored by every stakeholder with preferences drawn from a large, natural class. For example, in the case of college admissions decisions, policies constrained to satisfy causal fairness definitions would be disfavored by every stakeholder with neutral or positive preferences for both academic preparedness and diversity. Indeed, under a prominent definition of causal fairness, we prove the resulting policies require admitting all students with the same probability, regardless of academic qualifications or group membership. Our results highlight formal limitations and potential adverse consequences of common mathematical notions of causal fairness.

The popularity of machine learning has increased the risk of unfair models getting deployed in high-stake applications, such as justice system, drug/vaccination design, and medical diagnosis. Although there are effective methods to train fair models from scratch, how to automatically reveal and explain the unfairness of a trained model remains a challenging task. Revealing unfairness of machine learning models in interpretable fashion is a critical step towards fair and trustworthy AI. In this paper, we systematically tackle the novel task of revealing unfair models by mining interpretable evidence (RUMIE). The key idea is to find solid evidence in the form of a group of data instances discriminated most by the model. To make the evidence interpretable, we also find a set of human-understandable key attributes and decision rules that characterize the discriminated data instances and distinguish them from the other non-discriminated data. As demonstrated by extensive experiments on many real-world data sets, our method finds highly interpretable and solid evidence to effectively reveal the unfairness of trained models. Moreover, it is much more scalable than all of the baseline methods.

As AI systems become increasingly powerful and pervasive, there are growing concerns about machines' morality or a lack thereof. Yet, teaching morality to machines is a formidable task, as morality remains among the most intensely debated questions in humanity, let alone for AI. Existing AI systems deployed to millions of users, however, are already making decisions loaded with moral implications, which poses a seemingly impossible challenge: teaching machines moral sense, while humanity continues to grapple with it. To explore this challenge, we introduce Delphi, an experimental framework based on deep neural networks trained directly to reason about descriptive ethical judgments, e.g., "helping a friend" is generally good, while "helping a friend spread fake news" is not. Empirical results shed novel insights on the promises and limits of machine ethics; Delphi demonstrates strong generalization capabilities in the face of novel ethical situations, while off-the-shelf neural network models exhibit markedly poor judgment including unjust biases, confirming the need for explicitly teaching machines moral sense. Yet, Delphi is not perfect, exhibiting susceptibility to pervasive biases and inconsistencies. Despite that, we demonstrate positive use cases of imperfect Delphi, including using it as a component model within other imperfect AI systems. Importantly, we interpret the operationalization of Delphi in light of prominent ethical theories, which leads us to important future research questions.

Shortly after the assassination, trolls on 4chan posted a series of racist jokes "identifying" the shooter using photos of Kojima, the legendary 58-year-old game designer of "Metal Gear Solid" and "Death Stranding" fame, as reported by Kotaku. He eventually deleted his posts, but not before they were retweeted by far-right French politician Damien Rieu. Rieu then made another tweet using pictures of Kojima accompanied by a phrase translating to "the far-left kills." Rieu eventually deleted the tweet and issued an apology.

The Bridge: As your kids learn and grow in the world of technology, they aren't the only ones doing so. Meet LaMDA -- the supposedly sentient AI who is shaking the foundations of technology as we know it. Take a deep breath, because it's about to get really weird. We mentioned LaMDA (Google's "Language Model for Dialogue Applications") in last week's issue on the AI art bot, but only briefly. LaMDA is an AI program developed by Google that no one had heard of until a couple weeks ago, when one of the engineers on the project went public with the information that the AI was sentient, and had the cognitive functions of a 7–8 year old child.

The question has arisen with escalating frequency in recent years, a sort of journalistic thought bubble emerging from the collective consciousness of writers. Will artificial intelligence (AI) save humanity, or supplant us? On the one hand, we are told that AI holds the potential to solve some of the world's biggest problems -- challenges like poverty, food insecurity, inequality and climate change. On the other hand, some very smart people have issued warnings. Stephen Hawking said the technology could "spell the end of the human race."

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Alternative rock band Rage Against the Machine returned to the stage for their first performance in 11 years and did not mince words when expressing their anger about the Supreme Court overturning Roe v. Wade. During a show Saturday at Alpine Valley Music Theatre in Wisconsin, the band broadcasted several captions on a screen on stage blasting the high court over its decision to reverse the 1973 ruling on abortion rights – with one caption going as far as to suggest an elimination of the court, the Milwaukee Journal Sentinel reported. In addition to speaking out in favor of abortion rights, the captions touched on a number of other hot-button issues, including a reference to women as "birth-givers" and highlighting the rate of child gun violence victims.

In the late summer of 2020, when much of normal social life was suspended, a relationship that I had been in for several years abruptly collapsed. I was thirty-nine and scared by the idea that I would not be reproducing the kind of heteronormative nuclear family I had grown up in. I wandered the sidewalks of my Brooklyn neighborhood, where discarded masks littered the gutters, with a sense of having been exiled from my own life. My apartment, with its cat and its plants, still existed but was no longer my home; I could get a glass of cold prosecco at my favorite bar, but the people I used to see there seemed to have vanished. In Haruki Murakami's novel "1Q84," a character climbs down a ladder into a parallel existence in which things appear to be the same but nothing really is.