Fairness in AI, including tools like GitHub Copilot, is fundamentally about preventing systems from perpetuating or amplifying societal biases. The most direct way to integrate fairness is by addressing the root cause of bias during the development and training phases. This involves carefully curating and pre-processing training data to remove or mitigate existing biases and designing algorithms that do not create or exacerbate unfair outcomes for different demographic groups. This proactive approach is central to building responsible and equitable AI systems.

A. Focusing on accessibility will ensure fairness.

Accessibility ensures a tool is usable by people with diverse abilities, but it does not address whether the AI's outputs or suggestions are biased or discriminatory.

B. Focusing on collecting large datasets for training will ensure fairness.

Large datasets can still contain significant societal biases. Without careful curation, a larger dataset may simply amplify existing unfairness rather than eliminate it.

C. Regularly monitoring the AI tool's performance will ensure fairness in its outputs.

Monitoring is a crucial reactive measure for detecting fairness issues post-deployment, but it does not integrate fairness into the core operational process. The foundational integration happens during training.

1. Microsoft Responsible AI Standard, v2. (Official Vendor Documentation). Microsoft defines "Fairness" as a key principle, stating, "AI systems should treat all people fairly." The standard emphasizes the need to "understand and control for different types of bias, such as allocation and quality-of-service harms, that can affect system performance and, consequently, people’s lives." This directly supports mitigating bias in data and models.

Source: Microsoft, "The Microsoft Responsible AI Standard, v2," General Tenets, Principle 2: Fairness, page 10.

2. GitHub, "Building with responsible AI." (Official Vendor Documentation). GitHub, as a Microsoft company, adheres to these principles. Their approach involves "measuring for potential harms like bias" and implementing "mitigations to make our models safer." This points to the proactive process of addressing bias during development.

Source: GitHub Blog, "How GitHub is building for responsible AI," Published November 8, 2023.

3. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜" (Peer-Reviewed Academic Publication). This paper extensively discusses how large language models, the technology behind Copilot, learn and perpetuate biases from their vast training data. It argues that the source of unfairness is the biased data the models are trained on.

Source: FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pages 610–623. DOI: https://doi.org/10.1145/3442188.3445922