Detecting harmful and non-inclusive terminology in technical contexts is critical for fostering inclusive environments in computing. This study explores the impact of model architecture on harmful language detection by evaluating a curated database of technical terms, each paired with specific use cases. We tested a range of encoder, decoder, and encoder-decoder language models, including BERT-base-uncased, RoBERTa large-mnli, Gemini Flash 1.5 and 2.0, GPT-4, Claude AI Sonnet 3.5, T5-large, and BART-large-mnli. Each model was presented with a standardized prompt to identify harmful and non-inclusive language across 64 terms. Results reveal that decoder models, particularly Gemini Flash 2.0 and Claude AI, excel in nuanced contextual analysis, while encoder models like BERT exhibit strong pattern recognition but struggle with classification certainty. We discuss the implications of these findings for improving automated detection tools and highlight model-specific strengths and limitations in fostering inclusive communication in technical domains.

Customers want your business to use Artificial Intelligence (AI) to improve their experience and make their life easier – even if they don't know what it is or what it does. They understand that they must enable AI-powered experiences to better serve customers and to keep up with competitors. But even with adoption and interest being as high as it is, we're just at the beginning of the AI journey. When you type anything into Google, you're met by a barrage of search results. I just typed "Artificial Intelligence" into the search engine and was met by a grand total of 330 million results.

Customers want your business to use Artificial Intelligence (AI) to improve their experience and make their life easier -- even if they don't know what it is or what it does. They understand that they must enable AI-powered experiences to better serve customers and to keep up with competitors. But even with adoption and interest being as high as it is, we're just at the beginning of the AI journey. In this article, we take a look at how the 6 evolutionary stages of AI are significantly shaping new customer experience expectations. When you type anything into Google, you're met by a barrage of search results.

Displaying a document in Middle Eastern languages requires contextual analysis due to different presentational forms for each character of the alphabet. The words of the document will be formed by the joining of the correct positional glyphs representing corresponding presentational forms of the characters. A set of rules defines the joining of the glyphs. As usual, these rules vary from language to language and are subject to interpretation by the software developers. In this paper, we propose a machine learning approach for contextual analysis based on the first order Hidden Markov Model. We will design and build a model for the Farsi language to exhibit this technology. The Farsi model achieves 94 \% accuracy with the training based on a short list of 89 Farsi vocabularies consisting of 2780 Farsi characters. The experiment can be easily extended to many languages including Arabic, Urdu, and Sindhi. Furthermore, the advantage of this approach is that the same software can be used to perform contextual analysis without coding complex rules for each specific language. Of particular interest is that the languages with fewer speakers can have greater representation on the web, since they are typically ignored by software developers due to lack of financial incentives.