  Existing debiasing techniques are typically training-based or require access
to the model's internals and output distributions, so they are inaccessible to
end-users looking to adapt LLM outputs for their particular needs. In this
study, we examine whether structured prompting techniques can offer
opportunities for fair text generation. We evaluate a comprehensive
end-user-focused iterative framework of debiasing that applies System 2
thinking processes for prompts to induce logical, reflective, and critical text
generation, with single, multi-step, instruction, and role-based variants. By
systematically evaluating many LLMs across many datasets and different
prompting strategies, we show that the more complex System 2-based Implicative
Prompts significantly improve over other techniques demonstrating lower mean
bias in the outputs with competitive performance on the downstream tasks. Our
work offers research directions for the design and the potential of
end-user-focused evaluative frameworks for LLM use.
