As artificial intelligence (AI) challenges us to reimagine new ways of doing and being, Dr Marcel O'Gorman, professor of English Language and Literature, embraces emerging technologies and applies them to his pedagogy in the classroom. O'Gorman has published widely about the impacts of technology, and his most recent research focuses on how critical and inclusive design methods might help tackle some of the moral and ethical issues faced by contemporary technoculture. O'Gorman recently wrapped up teaching a fourth-year undergraduate course on techno-critical writing and design that focused on key issues around responsible innovation, such as algorithmic bias, conflict minerals and the colonial practices of big tech on the global stage. Students applied what they learned by writing and designing projects throughout the course. "They wrote stories in ChatGPT that tested the AI for gender bias. They generated images in DALL-E 2 that traced a racist history in the AI's training data," O'Gorman says.

A day earlier than teased, Rockstar has released the first official trailer of Grand Theft Auto VI, the next installment in arguably the biggest AAA game series. As indicated by a recent teaser image, GTA VI will be set in Leonida, Rockstar's take on Florida, and largely centered on Vice City, the series' stand in for Miami. The game will have a playable female character for the first time in the modern incarnation of the franchise, and we get swampy areas, inspired by Florida's National Park, and almost as swampy strip clubs. It is GTA, after all. The game will launch in 2025.

The approaches are very secure, however, the computational complexity of decryption is generally Deep neural networks (DNNs) show prominent superiority in not less than O(n). Even in the best case, the computational a large variety of fields, including self-driving cars [1], facial overhead increases linearly with DNN size, which recognition authorization [2], object detection [3], etc. is not suitable for buyers with limited computing resources. Training neural network models is expensive, which relies on Some researchers apply the selective encryption to reduce extensive datasets and computing resources. However, the resources the computational complexity of encryption and decryption of ordinary institutions or individuals are not always [8, 9]. In [8], the model can only be executed on trusted hardware.

Training and deploying machine learning models that meet fairness criteria for protected groups are fundamental in modern artificial intelligence. While numerous constraints and regularization terms have been proposed in the literature to promote fairness in machine learning tasks, most of these methods are not amenable to stochastic optimization due to the complex and nonlinear structure of constraints and regularizers. Here, the term "stochastic" refers to the ability of the algorithm to work with small mini-batches of data. Motivated by the limitation of existing literature, this paper presents a unified stochastic optimization framework for fair empirical risk minimization based on f-divergence measures (f-FERM). The proposed stochastic algorithm enjoys theoretical convergence guarantees. In addition, our experiments demonstrate the superiority of fairness-accuracy tradeoffs offered by f-FERM for almost all batch sizes (ranging from full-batch to batch size of one). Moreover, we show that our framework can be extended to the case where there is a distribution shift from training to the test data. Our extension is based on a distributionally robust optimization reformulation of f-FERM objective under $L_p$ norms as uncertainty sets. Again, in this distributionally robust setting, f-FERM not only enjoys theoretical convergence guarantees but also outperforms other baselines in the literature in the tasks involving distribution shifts. An efficient stochastic implementation of $f$-FERM is publicly available.

Technology Assisted Review (TAR) stopping rules aim to reduce the cost of manually assessing documents for relevance by minimising the number of documents that need to be examined to ensure a desired level of recall. This paper extends an effective stopping rule using information derived from a text classifier that can be trained without the need for any additional annotation. Experiments on multiple data sets (CLEF e-Health, TREC Total Recall, TREC Legal and RCV1) showed that the proposed approach consistently improves performance and outperforms several alternative methods.

Can we design artificial intelligence (AI) systems that rank our social media feeds to consider democratic values such as mitigating partisan animosity as part of their objective functions? We introduce a method for translating established, vetted social scientific constructs into AI objective functions, which we term societal objective functions, and demonstrate the method with application to the political science construct of anti-democratic attitudes. Traditionally, we have lacked observable outcomes to use to train such models, however, the social sciences have developed survey instruments and qualitative codebooks for these constructs, and their precision facilitates translation into detailed prompts for large language models. We apply this method to create a democratic attitude model that estimates the extent to which a social media post promotes anti-democratic attitudes, and test this democratic attitude model across three studies. In Study 1, we first test the attitudinal and behavioral effectiveness of the intervention among US partisans (N=1,380) by manually annotating (alpha=.895) social media posts with anti-democratic attitude scores and testing several feed ranking conditions based on these scores. Removal (d=.20) and downranking feeds (d=.25) reduced participants' partisan animosity without compromising their experience and engagement. In Study 2, we scale up the manual labels by creating the democratic attitude model, finding strong agreement with manual labels (rho=.75). Finally, in Study 3, we replicate Study 1 using the democratic attitude model instead of manual labels to test its attitudinal and behavioral impact (N=558), and again find that the feed downranking using the societal objective function reduced partisan animosity (d=.25). This method presents a novel strategy to draw on social science theory and methods to mitigate societal harms in social media AIs.

Listwise rerankers based on large language models (LLM) are the zero-shot state-of-the-art. However, current works in this direction all depend on the GPT models, making it a single point of failure in scientific reproducibility. Moreover, it raises the concern that the current research findings only hold for GPT models but not LLM in general. In this work, we lift this pre-condition and build for the first time effective listwise rerankers without any form of dependency on GPT. Our passage retrieval experiments show that our best list se reranker surpasses the listwise rerankers based on GPT-3.5 by 13% and achieves 97% effectiveness of the ones built on GPT-4. Our results also show that the existing training datasets, which were expressly constructed for pointwise ranking, are insufficient for building such listwise rerankers. Instead, high-quality listwise ranking data is required and crucial, calling for further work on building human-annotated listwise data resources.

Then, I address the identified research problems Hate speech on social media has severe negative in hate speech detection, by investigating the impacts, not only on its victims (Sticca et al., impact of bias in NLP models on hate speech 2013) but also on the moderators of social detection models from three perspectives: 1) the media platforms (Roberts, 2019). This is why explainability perspective ( 4), where I address the it is crucial to develop tools for automated hate first research problem and investigate the impact speech detection. These tools should provide of bias in NLP models on their performance of a safer environment for individuals, especially hate speech detection and whether the bias in for members of marginalized groups, to express NLP models explains their performance on hate themselves online. However, recent research shows speech detection; 2) the offensive stereotyping that current hate speech detection models falsely bias perspective ( 5), where I address the second flag content written by members of marginalized research problem and investigate the impact of communities, as hateful (Sap et al., 2019; Dixon imbalanced representations and co-occurrences of et al., 2018; Mchangama et al., 2021). Similarly, hateful content with marginalized identity groups recent research indicates that there are social biases on the bias of NLP models; and 3) the fairness in natural language processing (NLP) models (Garg perspective ( 6), where I address the third research et al., 2018; Nangia et al., 2020; Kurita et al., 2019; problem and investigate the impact of bias in Ousidhoum et al., 2021; Nozza et al., 2021, 2022). NLP models on the fairness of the task of hate Yet, the impact of these biases on the task of speech detection. For each research problem, I hate speech detection has been understudied. In summarize the work done to highlight its main my thesis, I identify and study three research findings, contributions, and limitations. Thereafter, problems: 1) the impact of bias in NLP models on I discuss the general takeaways from my thesis and the performance and explainability of hate speech how it can benefit the NLP community at large ( 7).

One of the most stunning twists in the recent five-day crisis at ChatGPT creator OpenAI came when some 95 percent of the company's hundreds of employees threatened to quit. The staff planned to follow CEO Sam Altman to develop successors to ChatGPT at Microsoft instead. The threat appeared to mark a turning point in Altman's ultimately successful attempt to return to OpenAI--it was also a scenario that businesses have the legal power to block in most US states. California, home to OpenAI's San Francisco HQ, is one of a handful states that bar the enforcement of noncompete agreements in employment contracts, which can forbid employees from hopping jobs to a competitor, often for years. That picture is now set to change, as a raft of new legislation aims to make more places like California.

In October, Francesca Mani was one of reportedly more than 30 girls at Westfield High School in New Jersey who were victims of deepfake pornography. Boys at the school had taken photos of Francesca and her classmates and manipulated them with artificial intelligence to create sexually explicit images of them without their consent. The practice is actually stunningly commonplace, but we rarely hear such stories--at least in part because many victims of sexual harassment very understandably don't want to talk publicly about incidents that are so private. But within just a day of learning about the violation, which she calls "shocking," 15-year-old Francesca started speaking out and calling on lawmakers to do something about the broader problem. Her efforts are already starting to pay off with new momentum behind proposals for state and federal legislation, which I wrote about in a story published this morning.