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The opportunities of using AI
AI presents numerous opportunities for knowledge work and impact on productivity of individuals, and evenmore so for organisations. AI has the ability to really change the ways in which work can be done such as sifting through large volumes of data far more quickly with potential benefits of enhanced efficiency, improved decision-making and reducing time on burdening administrative tasks. What is seen repeatedly in studies and research is that AI is a tool that changes people’s cognitions and expertise, which changes their skills leading to a massive shift of improvement in performance and productivity. But what is important is to note that AI is not autonomous and it needs human prompts to create information that is correct. It is about getting the prompts right and asking the right questions for the AI tool to help you.
AI as a tool for accessibility
AI can support individuals with learning difficulties, dyslexia and visual impairments alongside non-native English speakers. For example, in the field of education, platforms like ReadSpeaker can convert text to audio, making learning materials accessible to visually impaired students. Additionally, AI can analyse student performance and tailor learning content to individual needs and learning styles. Whereas AI in workplaces can help with real-time captioning in meetings, voice recognition software and AI can give cognitive assistant through chatbots that provide support for individuals with hearing or mobility impairments.
Diversity crisis and ethical challenges
Whilst AI has many opportunities to decrease workload and enhance work performance, the fast pace of AI also comes with challenges and some barriers that makes AI still far from being inclusive. Statistics from Women in Tech found that in 2023, women held just 26.7% of technology jobs overall, with leadership representation even lower at 10.9%. About 80 percent of AI professors are men, while women make up just 15 percent of AI research staff (West et al., 2019). Workforce gender imbalance at major tech companies such as Google, Facebook and Microsoft is helping perpetuate bias within artificial intelligence. To give further specifics, within the Microsoft’s core workforce (2023), there are 31.6% of women, whilst only 27.2% women are in technical roles. Why this is an important factor to consider is because research such as Baron-Cohen (2002) identifies that there are human sex differences in which the brains function, where the male brain is significantly better at ‘systemising’ whilst the female brain is significantly better at ‘empathising’. This is not saying that one gender is better than the other but identifying the need to combine the different skills to make a strong field of technology.
The lack of diversity doesn’t just relate to gender. According to the sixth annual Diversity in Tech report from Tech Talent Charter (2024), only 28% of the UK’s technology workers are gender minorities and 35% are from global majority backgrounds. Insufficient representation of large portions of our society will result in knowledge gaps and human bias, creating a “diversity crisis” within the use of AI tools and representation of the human voice.
Furthermore, Coyle (2019) identifies, there is a dominance of white males in AI coding, creating a diversity crisis. Picchi (2019) states that 80% of AI professors are men which allows bias and reinforces this narrow idea of the ‘normal’ person that could cause algorithmic discrimination. On the other hand, the technical errors within AI can be optimised, but the care and empathy cannot be resolved or generated by AI (Montemayor et al, 2021) when working with diverse needs of service users which is fundamental in relational practice. Hence, whilst AI promises efficiency and faster results, it generates specific risks that may be overlooked and justifies the necessity of human monitoring and emotional intervention in everyday practice by individuals through more diverse teams in IT.
Women and young girls in STEM
The foundation to this bias comes from the opportunities and inclusion of women/young girls in STEM programmes.
There is a big emphasis of young girls and women to be encouraged into STEM programmes as a way into technology due to the rising gap. Although, the foundation of this gap starts from early years education where we, as educators and practitioners, need to change the narrative of what is gender specific play or interests and give children the choice and voice to make their own decisions.
Whose voice is heard? Algorithmic bias
The study by Chen (2023) indicates that algorithmic bias stems from limited raw data sets and biased algorithm designers. The knowledge gap based on gender and global majority is presenting false or misleading information as fact. Most importantly it is compromising the authenticity of individual ‘human voice’ in the AI data. For example, meeting notes with children or families can be summarised but does this reflect the voice of the child or family within those notes? This calls for regulation of AI, along with a national framework of ethical principles for its use, to ensure accountability to service users whether children, families, practitioners, educators or anyone else, and to uphold human rights.
Key takeaways
- AI needs to be embraced with an ethical understanding – it is about data security but enhanced with social and ethical values whilst reflecting the voice of the diverse society
- The contribution of women and different ethnicities in AI teams is essential to ensure a variety of perspectives and outcomes within the algorithms
- There is a need for cross-functional teams that specialise in identifying bias in both humans and machines. These teams need to be best equipped to holistically tackle challenges and ensure more equitable and inclusive AI systems
- We need to be creating continuous open opportunities for a diverse AI generating workforce that is inclusive and equitable
Critical considerations
- It is vital to advocate for appropriate professional development and support. Ensure that you and your colleagues receive the necessary training and support to effectively integrate AI into practice.
- Continuously reflect on the impact of AI on your practice but also reflect on your unconscious bias of practice (language) when encouraging play
- Prioritise human interaction and social-emotional development. Ensure that AI and technology does not come at the expense of crucial opportunities for children to interact with others and develop essential social and emotional skills.
References and further reading
Baron-Cohen, S. (2002). The Extreme Male Brain Theory of Autism. Trends in Cognitive Sciences, 6(6), pp.248–254. Available at: https://doi.org/10.1016/s1364-6613(02)01904-6 Accessed: 27th July 2025
Daws, R. (2019) Lack of STEM diversity in causing AI to have a ‘white male’ bias. Available at: https://www.artificialintelligence-news.com/news/stem-diversity-ai-white-male-bias/ Accessed: 27th July 2025
Montemayor, C., Halpern, J. and Fairweather, A. (2021) In principle obstacles for empathic AI: Why we can’t replace human empathy in healthcare. AI & Society, [online] 37(4), pp.1353–1359. Available at: https://doi.org/10.1007/s00146-021-01230-z. Accessed 27th July 2025
Picchi, A. (2019) How tech’s white male workforce feeds bias into AI. Available at: https://www.cbsnews.com/news/ai-bias-problem-techs-white-male-workforce/ Accessed: 27th July 2025
Tech Talent Charter (2024) Diversity in Tech: An annual report tracking diversity in technology across the UK. Available on: https://www.techtalentcharter.co.uk/wp-content/uploads/diversity-in-tech-report-2024.pdf Accessed: 27th July 2025
West, S.M., Whittaker, M. and Crawford, K. (2019). Discriminating Systems: Gender, Race and Power in AI. AI Now Institute. Available at: https://ainowinstitute.org/publications/discriminating-systems-gender-race-and-power-in-ai-2 Accessed: 27th July 2025
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