Published May 30, 2024 | Version CC-BY-NC-ND 4.0
Journal article Open

Bias in Text Generative Open AI

Creators

  • 1. Department of Computer Science and Engineering International Institute of Information Technology Hyderabad (Telangana), India.

Description

Abstract: The rise of text generation models, especially those powered by advanced deep learning architectures like Open AI’s GPT-3, has unquestionably transformed various natural language processing applications. However, these models have recently faced examination due to their inherent biases, often evident in the generated text. This paper critically examines the issue of bias in text generation models, exploring the challenges posed, the ethical implications it entails, and the potential strategies to mitigate bias. Firstly, we go through the causes of the origin of the bias, ways to minimize it, and mathematical representation of Bias.

Files

B108404020224.pdf

Files (429.3 kB)

Name Size Download all
md5:10c8fefc38a00ec9c8911800d7400199
429.3 kB Preview Download

Additional details

Identifiers

DOI
10.54105/ijainn.B1084.04020224
EISSN
2582-7626

Dates

Accepted
2024-02-15
Manuscript received on 08 January 2024 | Revised Manuscript received on 17 January 2024 | Manuscript Accepted on 15 February 2024 | Manuscript published on 30 May 2024

References

  • On the Apparent Conflict Between Individual and Group Fairness.
  • Understanding Bias and Fairness in AI Systems.
  • Managing Bias in Machine Learning.
  • Confirmation Bias: Roles of Search Engines and Search Contexts.
  • Search Engine Bias and the Demise of Search Engine Utopianism.
  • Bias in Search Engines and Algorithms.
  • Evaluation Metrics for Measuring Bias in Search Engine Results.
  • Kanani, P., & Padole, Dr. M. (2019). Deep Learning to Detect Skin Cancer using Google Colab. In International Journal of Engineering and Advanced Technology (Vol. 8, Issue 6, pp. 2176–2183). https://doi.org/10.35940/ijeat.f8587.088619
  • Text Generation using Neural Models. (2019). In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 2S, pp. 19–23). https://doi.org/10.35940/ijitee.b1006.1292s19
  • Chellatamilan, T., Valarmathi, B., & Santhi, K. (2020). Research Trends on Deep Transformation Neural Models for Text Analysis in NLP Applications. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 9, Issue 2, pp. 750–758). https://doi.org/10.35940/ijrte.b3838.079220
  • Mathew, S. (2024). An Overview of Text to Visual Generation Using GAN. In Indian Journal of Image Processing and Recognition (Vol. 4, Issue 3, pp. 1–9). https://doi.org/10.54105/ijipr.a8041.04030424
  • Radhamani, V., & Dalin, G. (2019). Significance of Artificial Intelligence and Machine Learning Techniques in Smart Cloud Computing: A Review. In International Journal of Soft Computing and Engineering (Vol. 9, Issue 3, pp. 1–7). https://doi.org/10.35940/ijsce.c3265.099319