Online harms are a growing problem in digital spaces, putting user safety at risk and reducing trust in social media platforms. One of the most persistent forms of harm is hate speech. To address this, we need tools that combine the speed and scale of automated systems with the judgment and insight of human moderators. These tools should not only find harmful content but also explain their decisions clearly, helping to build trust and understanding. In this paper, we present WATCHED, a chatbot designed to support content moderators in tackling hate speech. The chatbot is built as an Artificial Intelligence Agent system that uses Large Language Models along with several specialised tools. It compares new posts with real examples of hate speech and neutral content, uses a BERT-based classifier to help flag harmful messages, looks up slang and informal language using sources like Urban Dictionary, generates chain-of-thought reasoning, and checks platform guidelines to explain and support its decisions. This combination allows the chatbot not only to detect hate speech but to explain why content is considered harmful, grounded in both precedent and policy. Experimental results show that our proposed method surpasses existing state-of-the-art methods, reaching a macro F1 score of 0.91. Designed for moderators, safety teams, and researchers, the tool helps reduce online harms by supporting collaboration between AI and human oversight.

A language arts teacher has shared the bizarre conspiracy theories her sixth grade students believe in and what fostered that beliefs. The teacher, who goes by the name Ms Alexanderr, said was amazed by her students' ideas and wanted to compile a list of the top five most she felt were the most bizarre. While the teacher said she wasn't surprised by one conspiracy theory that birds aren't real, she was shocked and couldn't understand others. Among them was the theory that Bill Nye the science guy is a Russian spy while another claimed Michael Jackson was still alive. The pop-star conspiracy was particularly perplexing, because her students were born after he died in 2009.

Considering the large amount of content created online by the minute, slang-aware automatic tools are critically needed to promote social good, and assist policymakers and moderators in restricting the spread of offensive language, abuse, and hate speech. Despite the success of large language models and the spontaneous emergence of slang dictionaries, it is unclear how far their combination goes in terms of slang understanding for downstream social good tasks. In this paper, we provide a framework to study different combinations of representation learning models and knowledge resources for a variety of downstream tasks that rely on slang understanding. Our experiments show the superiority of models that have been pre-trained on social media data, while the impact of dictionaries is positive only for static word embeddings. Our error analysis identifies core challenges for slang representation learning, including out-of-vocabulary words, polysemy, variance, and annotation disagreements, which can be traced to characteristics of slang as a quickly evolving and highly subjective language.

When considering beginning your AI project, you're likely inclined to have a blend of excitement and concern. Stunning, this can be astonishing. All the examples of success stories, the number of sales grow, income development etc. In any case, on the other hand, imagine a scenario where it turns out badly. How might you alleviate the risk of wasting money and time on something that simply isn't practical in any way?

What is the acceptable failure rate of an airplane? Well, it is not zero… no matter what how hard we want to believe otherwise. There is a number, and it is a very low number. When it comes to machines, computers, artificial intelligence, etc., they are perfectly imperfect. AI will never be perfect.

In services, businesses and even our personal lives, artificially intelligent bots are becoming commonplace. But with every technological advancement comes concerns, and the growing use of artificial intelligence is an especially hot topic. Part of the fear around AI is the mystery behind what its capabilities are. How does a bot make a decision? Can it have likes or dislikes?

In recent weeks, Tesla chief executive Elon Musk has said artificial intelligence (AI) is riskier than North Korea and he has called on the United Nations to ban autonomous weapons before they turn into killer robots. Musk has also reportedly called for AI regulation, saying AI is a "fundamental risk to the existence of human civilization" and he said Facebook chief executive Mark Zuckerberg's rosier spin proves "his understanding of the subject is limited." It is a decidedly grim view of a technology infiltrating the marketing industry via channels like customer service, search and data analysis. So how worried are marketers exactly? For her part, Jenna Niven, creative director at ad agency R/GA, noted AI has been around for a long time and is already integrated into processes we use every day, like Gmail sorting spam or Netflix recommending content, but only now are we starting to see AI in the forefront of user experiences like Alexa and self-driving cars, which means it is assuming a more obvious role in our lives.

An IBM supercomputer had to have its memory wiped because its programmers could find no other way to stop him swearing. Artificial intelligence Watson, which famously won Jeopardy! The website is a repository of English-language slang, and inevitably includes a range of profanities and insults completely inappropriate for polite conversation. How the mighty have fallen: Ken Jennings and Brad Rutter compete against IBM's Watson AI in a special edition of Jeopardy! in 2011. The machine had to have its memory wiped because it wouldn't stop swearing But Watson proved incapable of mastering the subleties of good mannered repartee and, after he began uttering obscenities, his masters were forced to delete the taboo vocabulary.

Much has been said about artificial intelligence (AI) and machine learning, which learn from their interactions so that they can work better in the future. But a number of incidents have pointed out how easy it is for AI systems to go astray, either by accident or because people deliberately set out to corrupt them. That computer programs respond based on the data they're given isn't new; "Garbage in, garbage out" dates back to the 1950s. As computers and software grow increasingly sophisticated in the data sources they accept and the responses they can provide, however, it's not always as easy to detect what might be "garbage." One of the oldest examples of such manipulated AI systems is the "Google bomb," where people created a particular catchphrase, associated it with something else, and posted the connection around the Internet.

On Wednesday March 23, Microsoft unleashed its brand new AI on Twitter. Her name was Tay, and she was programmed to tweet like a teenage girl. Microsoft didn't intend for that to happen, of course. It wanted to test and improve its algorithm for conversational language. According to Microsoft, Tay was built by "mining relevant [anonymous] public data" which was "modeled, cleaned, and filtered" to create her personality.