Information seeking users often pose questions with false presuppositions, especially when asking about unfamiliar topics. Most existing question answering (QA) datasets, in contrast, assume all questions have well defined answers. We introduce CREPE, a QA dataset containing a natural distribution of presupposition failures from online information-seeking forums. We find that 25% of questions contain false presuppositions, and provide annotations for these presuppositions and their corrections. Through extensive baseline experiments, we show that adaptations of existing open-domain QA models can find presuppositions moderately well, but struggle when predicting whether a presupposition is factually correct. This is in large part due to difficulty in retrieving relevant evidence passages from a large text corpus. CREPE provides a benchmark to study question answering in the wild, and our analyses provide avenues for future work in better modeling and further studying the task.

This is not Beamery's first audit of its AI tools. It conducted internal audits to test for compliance with General Data Protection Regulation, the 2016 European Union law that protects consumer identity and privacy. For AI anti-bias audits that fall under the New York City law, Beamery sought to test how its talent acquisition tools handle a potential job candidate's gender and ethnicity during the recruitment process. The first audit took place in the summer followed by a month-long audit in October.

A deepfake version of Meta CEO Mark Zuckerberg thanked Congressional Democrats for their'service and inaction' on antitrust legislation. The eerie, convincing clip is the work of advocacy group Demand Progress Action, which used deepfake technology to turn an actor into Zuckerberg - who thanks Democratic leaders Nancy Pelosi and Chuck Schumer for holding up two major pieces of antitrust legislation this year. 'Over the past five years, Congress has held over 30 hearings designed to hold Big Tech accountable,' fake Zuckerberg says in the ad, which the liberal group plans to use for television ads in New York and Washington, D.C. 'Sometimes you land a punch.' 'Most of the time, it felt like playing paddy cake,' fake Zuckerberg boasts. 'So I'd like to propose a toast to Speaker of the House Nancy Pelosi and Senate Majority Leader Chuck Schumer (above),' fake Zuckerberg says, holding a glass of champagne We then see a clip of Republican Sen. Hatch asking him how he sustains a business model where users don't pay for services. A grinning Zuckerberg says: 'Senator, we run ads.' 'Either way, it looks like the most consequential action that Congress is poised to take, a bipartisan bill to prevent companies like mine from self-dealing, is about to fade away like so many efforts to rein in big tech in the past.'

Generally, Stable Diffusion 1 is trained on LAION-2B (en), subsets of laion-high-resolution and laion-improved-aesthetics. The datasets is then filtered for explicit pornographic material, using the LAION-NSFW classifier with punsafe 0.1 and an aesthetic score 4.5. In other words, Stable Diffusion 2 used a larger, NSFW-filtered datasets for training. Also, the second phase of training is based on images with resolution higher or equal to 512x512. The new release ensures that StabilityAI resolves the long-standing legal problems related to child pornography and deep fakes. Also, the new model generates better images in certain areas.

The Bureau of Multiversal Arbitration is an unusual workplace. Maude Fletcher's alright, though she needs to learn how to turn off caps lock in the company chat. But trying to deal with Byron G Snodgrass is like handling an energetic poodle, and Phil is a bit stiff. Byron G Snodgrass is an energetic poodle. A peace lily, I think.

"Without understanding the cause and effect of interactions within the world, no AI model, algorithm, technique, application, or technology is real and true", be it: Natural language generation converting structured data into the native language; Speech recognition converting human speech into a useful and understandable format by computers; Virtual agents, computer applications that interact with humans to answer their queries, from Google Assistant to the Watson; Biometrics, to identify individuals based on their biological characteristics or behaviors, with fingerprints and faces, hand veins, irises, or voices biometric modalities; Decision management systems for data conversion and interpretation into predictive models; Machine learning empowering machine to make sense from data sets without being actually programmed, to make informed decisions with data analytics and statistical models; Robotic process automation configuring a robot (software application) to interpret, communicate and analyze data; Peer-to-peer network connecting between different systems and computers for data sharing without the data transmitting via server; Deep learning platforms based on ANNs teaching computers and machines to learn by example just the way humans do; Generative AI (GANs, Transformers, Autoencoders) referring to unsupervised and semi-supervised machine learning algorithms that enable computers to use existing content like text, audio and video files, images, or code to create new possible content as completely original artifacts. It leverages AI and ML algorithms to generate artificial content such as text, images, audio and video content based on its training data to trick the user into believing the content is real, facing legal challenges concerning data privacy; Generative AI models with image generation algorithms generating photographs of human faces, objects and scenes, image-to-image conversion, text-to-image translation, film restoration, semantic-image-to-photo translation, face frontal view generation, photos to emojis, face aging, media and entertainment: deep fake technology; AI optimized hardware support artificial intelligence models, as #neuralnetworks, #deeplearning, and #computervision, including CPUs, GPUs, TPUs, OPUs to handle scalable workloads, special purpose built-in silicon for neural networks, neuromorphic chips, etc.; Real AI is NOT about representing computational models of intelligence, described as structures, models, and operational functions that can be programmed for problem-solving, inferences, language processing, etc. Real AI is about the computational models of reality and mentality, described as causal structures, models, and operational functions that can be programmed for problem-solving and inferences for a wide range of goals in a wide range of environments.

To address the widespread problem of uncivil behavior, many online discussion platforms employ human moderators to take action against objectionable content, such as removing it or placing sanctions on its authors. This reactive paradigm of taking action against already-posted antisocial content is currently the most common form of moderation, and has accordingly underpinned many recent efforts at introducing automation into the moderation process. Comparatively less work has been done to understand other moderation paradigms -- such as proactively discouraging the emergence of antisocial behavior rather than reacting to it -- and the role algorithmic support can play in these paradigms. In this work, we investigate such a proactive framework for moderation in a case study of a collaborative setting: Wikipedia Talk Pages. We employ a mixed methods approach, combining qualitative and design components for a holistic analysis. Through interviews with moderators, we find that despite a lack of technical and social support, moderators already engage in a number of proactive moderation behaviors, such as preemptively intervening in conversations to keep them on track. Further, we explore how automation could assist with this existing proactive moderation workflow by building a prototype tool, presenting it to moderators, and examining how the assistance it provides might fit into their workflow. The resulting feedback uncovers both strengths and drawbacks of the prototype tool and suggests concrete steps towards further developing such assisting technology so it can most effectively support moderators in their existing proactive moderation workflow.

Building an accurate model of travel behaviour based on individuals' characteristics and built environment attributes is of importance for policy-making and transportation planning. Recent experiments with big data and Machine Learning (ML) algorithms toward a better travel behaviour analysis have mainly overlooked socially disadvantaged groups. Accordingly, in this study, we explore the travel behaviour responses of low-income individuals to transit investments in the Greater Toronto and Hamilton Area, Canada, using statistical and ML models. We first investigate how the model choice affects the prediction of transit use by the low-income group. This step includes comparing the predictive performance of traditional and ML algorithms and then evaluating a transit investment policy by contrasting the predicted activities and the spatial distribution of transit trips generated by vulnerable households after improving accessibility. We also empirically investigate the proposed transit investment by each algorithm and compare it with the city of Brampton's future transportation plan. While, unsurprisingly, the ML algorithms outperform classical models, there are still doubts about using them due to interpretability concerns. Hence, we adopt recent local and global model-agnostic interpretation tools to interpret how the model arrives at its predictions. Our findings reveal the great potential of ML algorithms for enhanced travel behaviour predictions for low-income strata without considerably sacrificing interpretability.

Facial analysis systems have been deployed by large companies and critiqued by scholars and activists for the past decade. Many existing algorithmic audits examine the performance of these systems on later stage elements of facial analysis systems like facial recognition and age, emotion, or perceived gender prediction; however, a core component to these systems has been vastly understudied from a fairness perspective: face detection, sometimes called face localization. Since face detection is a pre-requisite step in facial analysis systems, the bias we observe in face detection will flow downstream to the other components like facial recognition and emotion prediction. Additionally, no prior work has focused on the robustness of these systems under various perturbations and corruptions, which leaves open the question of how various people are impacted by these phenomena. We present the first of its kind detailed benchmark of face detection systems, specifically examining the robustness to noise of commercial and academic models. We use both standard and recently released academic facial datasets to quantitatively analyze trends in face detection robustness. Across all the datasets and systems, we generally find that photos of individuals who are $\textit{masculine presenting}$, $\textit{older}$, of $\textit{darker skin type}$, or have $\textit{dim lighting}$ are more susceptible to errors than their counterparts in other identities.

Emerging ethical approaches have attempted to filter pretraining material, but such approaches have been ad hoc and failed to take context into account. We offer an approach to filtering grounded in law, which has directly addressed the tradeoffs in filtering material. First, we gather and make available the Pile of Law, a 256GB (and growing) dataset of open-source English-language legal and administrative data, covering court opinions, contracts, administrative rules, and legislative records. Pretraining on the Pile of Law may help with legal tasks that have the promise to improve access to justice. Second, we distill the legal norms that governments have developed to constrain the inclusion of toxic or private content into actionable lessons for researchers and discuss how our dataset reflects these norms. Third, we show how the Pile of Law offers researchers the opportunity to learn such filtering rules directly from the data, providing an exciting new research direction in model-based processing. Warning: this paper contains quotations that may be offensive or upsetting.