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How can we take into account "human diversity"?
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Human Centricity

Accounting for Human Diversity in AI Systems 
To ensure that artificial intelligence (AI) systems are inclusive and fair, it is crucial to account for the diversity of human stakeholders. This involves recognising and addressing variations in needs, perspectives, and vulnerabilities across cultural, social, and economic contexts. Human diversity must be a central consideration throughout the AI lifecycle, from design and development to deployment and monitoring. This approach not only upholds ethical principles but also enhances the effectiveness and trustworthiness of AI systems. 
One essential aspect is addressing diverse cultural, social, economic contexts. AI systems often operate globally, interacting with individuals from different backgrounds, belief systems, and socioeconomic statuses. To respect this diversity, developers must: 

  1.  Incorporate contextual understanding into AI models 
  2.  AI systems must ensure equitable access to benefits across demographic groups 
  3.  Developers must implement safeguards against biases 
  4.  AI systems should undergo regular audits 
  
 
1.     Incorporate contextual understanding into AI models, tailoring their functionality and outputs to suit various settings. 
For example, language models should account for regional dialects and linguistic nuances, and healthcare AI should consider local health conditions, resources, and cultural attitudes toward treatment. Failure to do so risks marginalising underrepresented groups or delivering solutions that are irrelevant or harmful in certain contexts (Mehrabi et al., 2021). 
 
 
2.     AI systems must ensure equitable access to benefits across demographic groups, avoiding scenarios where certain populations are disproportionately excluded from the advantages of AI technologies. 
This is particularly relevant in sectors like healthcare, education, and finance, where unequal access can exacerbate existing social inequalities. For example, AI-driven financial tools should cater to individuals without extensive credit histories, enabling fairer access to loans and financial services. Policies that promote accessibility, such as offering AI systems in multiple languages or designing interfaces for users with disabilities, are key steps in achieving equitable inclusion (UNESCO, 2021). 
 
 
3.     Developers must implement safeguards against biases during the creation and deployment of AI systems to further uphold human diversity. 
Bias in AI often arises from biased training data or from algorithms that amplify societal stereotypes. These biases can disproportionately harm marginalised communities by perpetuating discrimination or unfair treatment. Strategies such as diversifying datasets, using fairness-aware algorithms, and involving diverse teams in AI development help mitigate these risks. For instance, facial recognition systems have historically shown higher error rates for individuals with darker skin tones, highlighting the importance of rigorous testing across diverse populations (Buolamwini & Gebru, 2018). 
  
 
4.     AI systems should undergo regular audits to identify and prevent unintended disparities. 
Monitoring outcomes and impacts ensures that AI continues to align with its ethical objectives and adapts to evolving societal contexts. Audits can help uncover hidden biases, measure equitable performance, and assess the inclusivity of AI tools. Transparent reporting and accountability mechanisms also build public trust, enabling stakeholders to understand how AI decisions are made and how they affect different groups. This iterative approach fosters continuous improvement and ensures AI systems remain responsive to human diversity (Raji et al., 2020). 
By addressing these considerations, AI systems can better reflect and respect human diversity, promoting inclusivity and fairness. Such efforts not only prevent harm but also enhance the societal and economic benefits of AI by ensuring that all individuals can participate in and benefit from its advancements. 
 

 
1. Buolamwini, J., & Gebru, T. (2018). "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification." Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT). 
2. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). "A Survey on Bias and Fairness in Machine Learning." ACM Computing Surveys, 54(6), 1–35. 
3. Raji, I. D., Binns, R., Mittelstadt, B., Wachter, S., & Gebru, T. (2020). "Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing." Conference on Fairness, Accountability, and Transparency (FAT). 
4. UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence. Retrieved from [UNESCO website](https://www.unesco.org). 

Disclaimer

This tool has been created in collaboration with Dstl as part of an AI Research project. The intent is for this tool to help generate discussion between project teams that are involved in the development of AI tools and techniques within MOD. It is hoped that this will result in an increased awareness of the MOD’s AI ethical principles (as set out in the Ambitious, Safe and Responsible policy paper) and ensure that these are considered and discussed at the earliest stages of a project’s lifecycle and throughout. This tool has not been designed to be used outside of this context. 
The use of this information does not negate the need for an ethical risk assessment, or other processes set out in the Dependable AI JSP 936 part 1, the MODs’ policy on responsible AI use and development. This training tool has been published to encourage more discussion and awareness of AI ethics across MOD science and technology and development teams within academia and industry and demonstrates our commitment to the practical implementation of our AI ethics principles.