Prioritise Fairness

Fairness is not a zero-sum game. Prioritizing fairness for specific groups who are most likely to be harmed does not mean taking away from others. However, we often lack data resolution for groups categorized as “other,” making it difficult to investigate their representation and potential harms. Every “checkbox” on a form represents a prioritization of a specific attribute, and those not represented face unique challenges.

To address these issues, we must adopt a counterfactual thinking, prioritizing those who have been historically marginalized and underrepresented in technology.

The Gender Shades project in this context, defined as having practical differences in gender classification error rates between groups, highlighted the lower quality of service experienced by Black women in facial recognition compared to white men. This demonstrates the importance of intersectionality: examining overlapping factors like gender and race together reveals unique harms faced by specific groups.

Building Inclusive Technology

Creating technology that includes as many people as possible is essential for building robust and equitable AI systems. Excluding diverse identities from data leads to non-robust models that may perpetuate biases and harms. Questioning the need for tools that predict sensitive attributes like gender is also crucial, as AI systems rely on observable features rather than inherent qualities. Additionally, historical biases in technology development, such as camera optimization for lighter skin tones, need to be acknowledged and addressed.

Inclusivity goes beyond representation; it’s about whose voices are heard and how their feedback is incorporated.

By actively engaging with diverse communities and incorporating their perspectives throughout the AI development process, we can create more inclusive and equitable systems.

Moving Forward

To create more just and equitable AI systems, we must actively consider fairness and inclusivity throughout the development process. This requires ongoing effort, critical reflection, and a commitment to prioritizing the needs of those most likely to be harmed by biased systems.

Fairness and inclusivity are essential considerations in the development of AI systems. By understanding the potential harms, adopting a counterfactual mindset, and prioritizing the needs of historically marginalized groups, we can create more equitable and just technological solutions. It is our responsibility as AI practitioners and stakeholders to actively work towards building inclusive AI systems that benefit all members of society.

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