This article explores the ethical problems arising from the use of ChatGPT as a kind of generative AI and suggests responses based on the Human-Centered Artificial Intelligence (HCAI) framework. The HCAI framework is appropriate because it understands technology above all as a tool to empower, augment, and enhance human agency while referring to human wellbeing as a grand challenge, thus perfectly aligning itself with ethics, the science of human flourishing. Further, HCAI provides objectives, principles, procedures, and structures for reliable, safe, and trustworthy AI which we apply to our ChatGPT assessments. The main danger ChatGPT presents is the propensity to be used as a weapon of mass deception (WMD) and an enabler of criminal activities involving deceit. We review technical specifications to better comprehend its potentials and limitations. We then suggest both technical (watermarking, styleme, detectors, and fact-checkers) and non-technical measures (terms of use, transparency, educator considerations, HITL) to mitigate ChatGPT misuse or abuse and recommend best uses (creative writing, non-creative writing, teaching and learning). We conclude with considerations regarding the role of humans in ensuring the proper use of ChatGPT for individual and social wellbeing.

In the current context where online platforms have been effectively weaponized in a variety of geo-political events and social issues, Internet memes make fair content moderation at scale even more difficult. Existing work on meme classification and tracking has focused on black-box methods that do not explicitly consider the semantics of the memes or the context of their creation. In this paper, we pursue a modular and explainable architecture for Internet meme understanding. We design and implement multimodal classification methods that perform example- and prototype-based reasoning over training cases, while leveraging both textual and visual SOTA models to represent the individual cases. We study the relevance of our modular and explainable models in detecting harmful memes on two existing tasks: Hate Speech Detection and Misogyny Classification. We compare the performance between example- and prototype-based methods, and between text, vision, and multimodal models, across different categories of harmfulness (e.g., stereotype and objectification). We devise a user-friendly interface that facilitates the comparative analysis of examples retrieved by all of our models for any given meme, informing the community about the strengths and limitations of these explainable methods.

Detecting misinformation threads is crucial to guarantee a healthy environment on social media. We address the problem using the data set created during the COVID-19 pandemic. It contains cascades of tweets discussing information weakly labeled as reliable or unreliable, based on a previous evaluation of the information source. The models identifying unreliable threads usually rely on textual features. But reliability is not just what is said, but by whom and to whom. We additionally leverage on network information. Following the homophily principle, we hypothesize that users who interact are generally interested in similar topics and spreading similar kind of news, which in turn is generally reliable or not. We test several methods to learn representations of the social interactions within the cascades, combining them with deep neural language models in a Multi-Input (MI) framework. Keeping track of the sequence of the interactions during the time, we improve over previous state-of-the-art models.

Keyword spotting systems continuously process audio streams to detect keywords. One of the most challenging tasks in designing such systems is to reduce False Alarm (FA) which happens when the system falsely registers a keyword despite the keyword not being uttered. In this paper, we propose a simple yet elegant solution to this problem that follows from the law of total probability. We show that existing deep keyword spotting mechanisms can be improved by Successive Refinement, where the system first classifies whether the input audio is speech or not, followed by whether the input is keyword-like or not, and finally classifies which keyword was uttered. We show across multiple models with size ranging from 13K parameters to 2.41M parameters, the successive refinement technique reduces FA by up to a factor of 8 on in-domain held-out FA data, and up to a factor of 7 on out-of-domain (OOD) FA data. Further, our proposed approach is "plug-and-play" and can be applied to any deep keyword spotting model.

One of the new developments in chit-chat bots is a long-term memory mechanism that remembers information from past conversations for increasing engagement and consistency of responses. The bot is designed to extract knowledge of personal nature from their conversation partner, e.g., stating preference for a particular color. In this paper, we show that this memory mechanism can result in unintended behavior. In particular, we found that one can combine a personal statement with an informative statement that would lead the bot to remember the informative statement alongside personal knowledge in its long term memory. This means that the bot can be tricked into remembering misinformation which it would regurgitate as statements of fact when recalling information relevant to the topic of conversation. We demonstrate this vulnerability on the BlenderBot 2 framework implemented on the ParlAI platform and provide examples on the more recent and significantly larger BlenderBot 3 model. We generate 150 examples of misinformation, of which 114 (76%) were remembered by BlenderBot 2 when combined with a personal statement. We further assessed the risk of this misinformation being recalled after intervening innocuous conversation and in response to multiple questions relevant to the injected memory. Our evaluation was performed on both the memory-only and the combination of memory and internet search modes of BlenderBot 2. From the combinations of these variables, we generated 12,890 conversations and analyzed recalled misinformation in the responses. We found that when the chat bot is questioned on the misinformation topic, it was 328% more likely to respond with the misinformation as fact when the misinformation was in the long-term memory.

News sources undergo the process of selecting newsworthy information when covering a certain topic. The process inevitably exhibits selection biases, i.e. news sources' typical patterns of choosing what information to include in news coverage, due to their agenda differences. To understand the magnitude and implications of selection biases, one must first discover (1) on what topics do sources typically have diverging definitions of "newsworthy" information, and (2) do the content selection patterns correlate with certain attributes of the news sources, e.g. ideological leaning, etc. The goal of the paper is to investigate and discuss the challenges of building scalable NLP systems for discovering patterns of media selection biases directly from news content in massive-scale news corpora, without relying on labeled data. To facilitate research in this domain, we propose and study a conceptual framework, where we compare how sources typically mention certain controversial entities, and use such as indicators for the sources' content selection preferences. We empirically show the capabilities of the framework through a case study on NELA-2020, a corpus of 1.8M news articles in English from 519 news sources worldwide. We demonstrate an unsupervised representation learning method to capture the selection preferences for how sources typically mention controversial entities. Our experiments show that that distributional divergence of such representations, when studied collectively across entities and news sources, serve as good indicators for an individual source's ideological leaning. We hope our findings will provide insights for future research on media selection biases.

Americans share whether they're concerned by recent expert predictions that unregulated AI could turn on their human inventors and wipe out our species. AUSTIN, Texas – Americans who spoke with Fox News questioned tech titans' recent warnings that AI's unregulated and rapid advancement could eventually kill humanity. But one man said he feared a Terminator-like ending. "I'm not concerned," Zachary, of Austin, told Fox News. "Even if it mimics the human brain, I don't believe that it is capable of manipulating us in order to overthrow us."

Barbie fans around the world are counting down to the release of the Barbie movie, which is set to land in theatres on July 21, 2023. Posters for the film dropped yesterday, showing stars including Margot Robbie, 32, and Ryan Gosling, 42 in their iconic roles. But have you ever wondered what your character might look like in Barbieland? Well now you can find out, thanks to a new tool dubbed the Barbie Selfie Generator. The tool uses artificial intelligence (AI) to transform your photos into Barbie movie posters - here's how to try it yourself.

The short videos give the impression of a flipbook, jumping shakily from one surreal frame to the next. They're the result of internet meme-makers playing with the first widely available text-to-video AI generators, and they depict impossible scenarios like Dwayne "The Rock" Johnson eating rocks and French president Emmanuel Macron sifting through and chewing on garbage, or warped versions of the mundane, like Paris Hilton taking a selfie. This new wave of AI-generated videos has definite echoes of Dall-E, which swept the internet last summer when it performed the same trick with still images. Less than a year later, those wonky Dall-E images are almost indistinguishable from reality, raising two questions: Will AI-generated video advance as quickly, and will it have a place in Hollywood? ModelScope, a video generator hosted by AI firm Hugging Face, allows people to type a few words and receive a startling, wonky video in return. Runway, the AI company that cocreated the image generator Stable Diffusion, announced a text-to-video generator in late March, but it has not made it widely available to the public.

FOX Business correspondent Lydia Hu has the latest on jobs at risk as AI further develops on "America's Newsroom." The Federal Trade Commission (FTC) is making a play to be a key regulator of artificial intelligence (AI) systems, just as technology heavyweights and policymakers are clamoring for federal government oversight of AI applications. Last week's call for a moratorium on new AI development from tech giants like Elon Musk and Steve Wozniak kick-started a discussion about whether and how the government should step in and put guardrails up around potentially dangerous AI systems. Several lawmakers responded by saying a moratorium would be difficult to impose, leaving a huge gap between calls for action and the realities of how quickly Congress can act. However, the FTC has made it clear over the last week that it is prepared to bridge that gap and take a stab at regulating emerging AI systems. The federal agency tasked with policing "deceptive or unfair business practices" says it has a dog in this fight and is building up a capacity to take on the threats that AI poses to wary consumers.