Taught by the Internet, Exploring Bias in OpenAIs GPT3
Ali Ayaz Aditya Nawalgaria Ruilian Yin
This research delves into the current literature on bias in Natural Language
Processing Models and the techniques proposed to mitigate the problem of bias,
including why it is important to tackle bias in the first place. Additionally,
these techniques are further analysed in the light of newly developed models
that tower in size over past editions. To achieve those aims, the authors of
this paper conducted their research on GPT3 by OpenAI, the largest NLP model
available to consumers today. With 175 billion parameters in contrast to BERTs
340 million, GPT3 is the perfect model to test the common pitfalls of NLP
models. Tests were conducted through the development of an Applicant Tracking
System using GPT3. For the sake of feasibility and time constraints, the tests
primarily focused on gender bias, rather than all or multiple types of bias.
Finally, current mitigation techniques are considered and tested to measure
their degree of functionality.