Since its establishment in 1914, the US Federal Trade Commission has stood as a bulwark against the fraud, deception, and shady dealings that American consumers face every day -- fining brands that "review hijack" Amazon listings, making it easier to cancel magazine subscriptions and blocking exploitative ad targeting. On Monday, Michael Atleson, Attorney, FTC Division of Advertising Practices, laid out both the commission's reasoning for how emerging generative AI systems like ChatGPT, Dall-E 2 could be used to violate the FTC Act's spirit of unfairness, and what it would do to companies found in violation. "Under the FTC Act, a practice is unfair if it causes more harm than good," Atleson said. "It's unfair if it causes or is likely to cause substantial injury to consumers that is not reasonably avoidable by consumers and not outweighed by countervailing benefits to consumers or to competition." He notes that the new generation of chatbots like Bing, Bard and ChatGPT can be used to influence the user's, "beliefs, emotions, and behavior."

More than 150 workers whose labor underpins the AI systems of Facebook, TikTok and ChatGPT gathered in Nairobi on Monday and pledged to establish the first African Content Moderators Union, in a move that could have significant consequences for the businesses of some of the world's biggest tech companies. The current and former workers, all employed by third party outsourcing companies, have provided content moderation services for AI tools used by Meta, Bytedance, and OpenAI--the respective owners of Facebook, TikTok and the breakout AI chatbot ChatGPT. Despite the mental toll of the work, which has left many content moderators suffering from PTSD, their jobs are some of the lowest-paid in the global tech industry, with some workers earning as little as $1.50 per hour. As news of the successful vote to register the union was read out, the packed room of workers at the Mövenpick Hotel in Nairobi burst into cheers and applause, a video from the event seen by TIME shows. Confetti fell onto the stage, and jubilant music began to play as the crowd continued to cheer.

Paedophiles are using a popular new artificial intelligence (AI) platform to transform real photos of children into sexualised images, it has been revealed. It has led to warnings to parents to be careful about the pictures of their children they're posting online. The images were found on the US AI image generator Midjourney, which much like ChatGPT uses prompts to deliver an output, although these usually consist of pictures rather than words. The platform is used by millions and has churned out such realistic images that people across the world have been fooled by them, including users on Twitter. An image of Pope Francis donning a huge white puffer jacket with a cross hanging from his neck sent social media users into a frenzy earlier this year.

Modern machine learning increasingly supports paradigms that are multi-institutional (using data from multiple institutions during training) or cross-institutional (using models from multiple institutions for inference), but the empirical effects of these paradigms are not well understood. This study investigates cross-institutional learning via an empirical case study in higher education. We propose a framework and metrics for assessing the utility and fairness of student dropout prediction models that are transferred across institutions. We examine the feasibility of cross-institutional transfer under real-world data- and model-sharing constraints, quantifying model biases for intersectional student identities, characterizing potential disparate impact due to these biases, and investigating the impact of various cross-institutional ensembling approaches on fairness and overall model performance. We perform this analysis on data representing over 200,000 enrolled students annually from four universities without sharing training data between institutions. We find that a simple zero-shot cross-institutional transfer procedure can achieve similar performance to locally-trained models for all institutions in our study, without sacrificing model fairness. We also find that stacked ensembling provides no additional benefits to overall performance or fairness compared to either a local model or the zero-shot transfer procedure we tested. We find no evidence of a fairness-accuracy tradeoff across dozens of models and transfer schemes evaluated. Our auditing procedure also highlights the importance of intersectional fairness analysis, revealing performance disparities at the intersection of sensitive identity groups that are concealed under one-dimensional analysis.

In populous countries, pending legal cases have been growing exponentially. There is a need for developing NLP-based techniques for processing and automatically understanding legal documents. To promote research in the area of Legal NLP we organized the shared task LegalEval - Understanding Legal Texts at SemEval 2023. LegalEval task has three sub-tasks: Task-A (Rhetorical Roles Labeling) is about automatically structuring legal documents into semantically coherent units, Task-B (Legal Named Entity Recognition) deals with identifying relevant entities in a legal document and Task-C (Court Judgement Prediction with Explanation) explores the possibility of automatically predicting the outcome of a legal case along with providing an explanation for the prediction. In total 26 teams (approx. 100 participants spread across the world) submitted systems paper. In each of the sub-tasks, the proposed systems outperformed the baselines; however, there is a lot of scope for improvement. This paper describes the tasks, and analyzes techniques proposed by various teams.

In prediction-based decision-making systems, different perspectives can be at odds: The short-term business goals of the decision makers are often in conflict with the decision subjects' wish to be treated fairly. Balancing these two perspectives is a question of values. However, these values are often hidden in the technicalities of the implementation of the decision-making system. In this paper, we propose a framework to make these value-laden choices clearly visible. We focus on a setting in which we want to find decision rules that balance the perspective of the decision maker and of the decision subjects. We provide an approach to formalize both perspectives, i.e., to assess the utility of the decision maker and the fairness towards the decision subjects. In both cases, the idea is to elicit values from decision makers and decision subjects that are then turned into something measurable. For the fairness evaluation, we build on well-known theories of distributive justice and on the algorithmic literature to ask what a fair distribution of utility (or welfare) looks like. This allows us to derive a fairness score that we then compare to the decision maker's utility. As we focus on a setting in which we are given a trained model and have to choose a decision rule, we use the concept of Pareto efficiency to compare decision rules. Our proposed framework can both guide the implementation of a decision-making system and help with audits, as it allows us to resurface the values implemented in a decision-making system.

Our work advances an approach for predicting hate speech in social media, drawing out the critical need to consider the discussions that follow a post to successfully detect when hateful discourse may arise. Using graph transformer networks, coupled with modelling attention and BERT-level natural language processing, our approach can capture context and anticipate upcoming anti-social behaviour. In this paper, we offer a detailed qualitative analysis of this solution for hate speech detection in social networks, leading to insights into where the method has the most impressive outcomes in comparison with competitors and identifying scenarios where there are challenges to achieving ideal performance. Included is an exploration of the kinds of posts that permeate social media today, including the use of hateful images. This suggests avenues for extending our model to be more comprehensive. A key insight is that the focus on reasoning about the concept of context positions us well to be able to support multi-modal analysis of online posts. We conclude with a reflection on how the problem we are addressing relates especially well to the theme of dynamic change, a critical concern for all AI solutions for social impact. We also comment briefly on how mental health well-being can be advanced with our work, through curated content attuned to the extent of hate in posts.

It took just the month of March this year to fell an area of forest in Triunfo do Xingu equivalent to 700 football pitches. At more than 16,000 sq km, this Environmental Protection Area (APA) in the south-eastern corner of the Brazilian Amazon, in the state of Pará, is one of the largest conservation areas in the world. And according to a new tool that predicts where deforestation will happen next, it's also the APA at highest risk of even more destruction. The tool, PrevisIA, is an artificial intelligence platform created by researchers at environmental nonprofit Imazon. Instead of trying to repair damage done by deforestation after the fact, they wanted to find a way to prevent it from happening at all.

LONDON/STOCKHOLM – As recently as February, generative AI did not feature prominently in EU lawmakers' plans for regulating generative artificial intelligence technologies such as ChatGPT. The bloc's 108-page proposal for the AI Act, published two years earlier, included only one mention of the word "chatbot." References to AI-generated content largely referred to deepfakes: images or audio designed to impersonate human beings. By mid-April, however, members of European Parliament (MEPs) were racing to update those rules to catch up with an explosion of interest in generative AI, which has provoked awe and anxiety since OpenAI unveiled ChatGPT six months ago. This could be due to a conflict with your ad-blocking or security software.

Meta won't say how many content moderators it employs or contracts in Horizon Worlds, or whether the company intends to increase that number with the new age policy. But the change puts a spotlight on those tasked with enforcement in these new online spaces--people like Yekkanti--and how they go about their jobs. Yekkanti has worked as a moderator and training manager in virtual reality since 2020 and came to the job after doing traditional moderation work on text and images. He is employed by WebPurify, a company that provides content moderation services to internet companies such as Microsoft and Play Lab, and works with a team based in India. His work is mostly done in mainstream platforms, including those owned by Meta, although WebPurify declined to confirm which ones specifically citing client confidentiality agreements.