Exploiting social media to spread hate has tremendously increased over the years. Lately, multi-modal hateful content such as memes has drawn relatively more traction than uni-modal content. Moreover, the availability of implicit content payloads makes them fairly challenging to be detected by existing hateful meme detection systems. In this paper, we present a use case study to analyze such systems' vulnerabilities against external adversarial attacks. We find that even very simple perturbations in uni-modal and multi-modal settings performed by humans with little knowledge about the model can make the existing detection models highly vulnerable. Empirically, we find a noticeable performance drop of as high as 10% in the macro-F1 score for certain attacks. As a remedy, we attempt to boost the model's robustness using contrastive learning as well as an adversarial training-based method - VILLA. Using an ensemble of the above two approaches, in two of our high resolution datasets, we are able to (re)gain back the performance to a large extent for certain attacks. We believe that ours is a first step toward addressing this crucial problem in an adversarial setting and would inspire more such investigations in the future.

Differentiable Neural Architecture Search (DARTS) is becoming more and more popular among Neural Architecture Search (NAS) methods because of its high search efficiency and low compute cost. However, the stability of DARTS is very inferior, especially skip connections aggregation that leads to performance collapse. Though existing methods leverage Hessian eigenvalues to alleviate skip connections aggregation, they make DARTS unable to explore architectures with better performance. In the paper, we propose operation-level progressive differentiable neural architecture search (OPP-DARTS) to avoid skip connections aggregation and explore better architectures simultaneously. We first divide the search process into several stages during the search phase and increase candidate operations into the search space progressively at the beginning of each stage. It can effectively alleviate the unfair competition between operations during the search phase of DARTS by offsetting the inherent unfair advantage of the skip connection over other operations. Besides, to keep the competition between operations relatively fair and select the operation from the candidate operations set that makes training loss of the supernet largest. The experiment results indicate that our method is effective and efficient. Our method's performance on CIFAR-10 is superior to the architecture found by standard DARTS, and the transferability of our method also surpasses standard DARTS. We further demonstrate the robustness of our method on three simple search spaces, i.e., S2, S3, S4, and the results show us that our method is more robust than standard DARTS. Our code is available at https://github.com/zxunyu/OPP-DARTS.

Algorithmic Recourse refers to the capability of an algorithm to provide explanations and make recommendations in response to an appeal or challenge raised by an individual who has been affected negatively by its decision Wachter et al. (2017); Ustun et al. (2019); Karimi et al. (2020); Venkatasubramanian and Alfano (2020). This concept is particularly important in areas such as finance, healthcare, and criminal justice where decisions made by algorithms can have significant impacts on people's lives Chou et al. (2022). Recently, there has been an explosion of proposals for counterfactual explainers in the emerging field of algorithmic recourse Guidotti (2022); Stepin et al. (2021); Karimi et al. (2021); Verma et al. (2020). Ensuring fairness and robustness in algorithmic decision-making processes is crucial to guarantee fair and just outcomes for all involved. In the context of algorithmic recourse, robustness refers to the ability of an algorithm to withstand unreliability, manipulation, or deception by malicious actors, while still providing fair and accurate recourse recommendations Slack et al. (2021); Upadhyay et al. (2021); Dominguez-Olmedo et al. (2022); Pawelczyk et al. (2022). There are four types of unreliabilities in counterfactual explanations Mishra et al. (2021): Robustness to input perturbations: Examining recourse behavior in response to slight input changes while the classifier remains unchanged Dominguez-Olmedo et al. (2022).

Word embeddings provide an unsupervised way to understand differences in word usage between discursive communities. A number of recent papers have focused on identifying words that are used differently by two or more communities. But word embeddings are complex, high-dimensional spaces and a focus on identifying differences only captures a fraction of their richness. Here, we take a step towards leveraging the richness of the full embedding space, by using word embeddings to map out how words are used differently. Specifically, we describe the construction of dialectograms, an unsupervised way to visually explore the characteristic ways in which each community use a focal word. Based on these dialectograms, we provide a new measure of the degree to which words are used differently that overcomes the tendency for existing measures to pick out low frequent or polysemous words. We apply our methods to explore the discourses of two US political subreddits and show how our methods identify stark affective polarisation of politicians and political entities, differences in the assessment of proper political action as well as disagreement about whether certain issues require political intervention at all.

This article presents a solution for gauging public opinion in tweets. The goal is to create a transformation pipeline that outputs clusters of comments and trending subjects. Apache, Apache NiFi, Apache Hadoop, Apache Hive, and Apache Airflow are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. No endorsement by The Apache Software Foundation is implied by the use of these marks. The Twitter ingestion pipeline consists of four stages.

Every year, an army of hackers takes aim at the tax code. The tax code is not computer code, but it is a series of rules--supposedly deterministic algorithms--that take data about your income and determine the amount of money you owe. This code has vulnerabilities, more commonly known as loopholes. It has exploits; those are tax avoidance strategies. There is an entire industry of black-hat hackers who exploit vulnerabilities in the tax code: We call them accountants and tax attorneys.

Various types of simplifications used for the purposes of advertising became the prerequisite for the introduction of regulations. There are situations where the options of Advanced Driver Assistance Systems (ADAS) and the benefits of their use are described in promotional materials in a way that suggests that they enable driving in autonomous mode, without the need for the driver's participation, while the currently offered vehicles do not exceed the L3 level according to the SAE J3016 standard, so they require the attention of the driver. In addition, the context of responsibility for a road accident, which according to legal regulations belongs to the driver, is not without significance.

Last year, a Tennessee-based artist named Kelly McKernan noticed that their name was being used with increasing frequency in A.I.-driven image generation. McKernan makes paintings that often feature nymphlike female figures in an acid-colored style that blends Art Nouveau and science fiction. A list published in August, by a Web site called Metaverse Post, suggested "Kelly McKernan" as a term to feed an A.I. generator in order to create "Lord of the Rings"-style art. Hundreds of other artists were similarly listed according to what their works evoked: anime, modernism, "Star Wars." On the Discord chat that runs an A.I. generator called Midjourney, McKernan discovered that users had included their name more than twelve thousand times in public prompts.

Remember Tay? That's what I immediately fixed upon when Microsoft's new Bing started spouting racist terms in front of my fifth-grader. I have two sons, and both of them are familiar with ChatGPT, OpenAI's AI-powered tool. When Bing launched its own AI-powered search engine and chatbot this week, my first thought upon returning home was to show them how it worked, and how it compared with a tool that they had seen before. As it happened, my youngest son was home sick, so he was the first person I began showing Bing to when he walked in my office. I started giving him a tour of the interface, as I had done in my hands-on with the new Bing, but with an emphasis on how Bing explains things at length, how it uses footnotes, and, most of all, includes safeguards to prevent users from tricking it into using hateful language like Tay had done.

Whether you are in manufacturing, retail, marketing, healthcare, or business, artificial intelligence and automation practices are starting to shift the way industries operate. A Deloitte study recently found that over 50% of organizations are planning on incorporating the use of AI and automation technologies in 2023. While many top executives are worried about the risks of AI usage, other high-achieving organizations are adopting new tech-savvy operational processes. A survey of Global 500 companies found that leaders choosing to invest in AI and automation business tools and software solutions expect to see significant growth within the next few years. How business practices are expected to change in the new year as artificial intelligence and ... [ ] automation continues to transform business operations.