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@article{180542,
        author = {Satya Sudha S and Praneeth Naga Sai Narayan Janjanam and Nera Manideep and Mahasamudram Naveen},
        title = {Ensuring Inclusivity and fairness in AI-generated content.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {1464-1467},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180542},
        abstract = {Bias and fairness in NLP models are of the 
highest priority to ensure AI-generated text stays 
balanced and impartial. NLP models, having been 
trained on large datasets to receive social biases which 
may result in fair or unfair results. This project is 
about searching, researching, and mitigation of text 
biases 
generated by AI using fairness-aware 
algorithms. The method involves choosing well 
balanced datasets, employing involving training, and 
adding fairness rules to reduce gender and culture 
biases. The project includes a fairness inspection 
process to identify potential weaknesses before it is 
launched. By following responsible AI guidelines and 
regulations, this approach seeks to develop NLP that 
is easier to understand, more ethical, and socially 
responsible. AI fairness models that generate fair text 
content. Fairness inspection help identify unfairness, 
weaknesses, and security risks in a system and while 
pure fairness is hard to achieve, continuous efforts can 
reduce harm and promote Equality in AI.},
        keywords = {Bias, Fairness, Hugging Face model,  NLP, Fairness-aware Algorithms (AIF360).},
        month = {June},
        }