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The Critical Importance of Inclusivity in AI

For AI to deliver genuine progress, inclusion must be a core priority and guiding principle of AI development and adoption.

5 min readBy Megha Kumar
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5 min read
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Megha Kumar

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The Critical Importance of Inclusivity in AI
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Executive summary

Board briefing at a glance
Distilled for CISO, CTO, and board audiences.

Key takeaways

  • The AI field remains stubbornly homogeneous. This is not in the long-term interest of anyone, vendors, deployers or users.
  • Organisations known for non-inclusive AI practices will struggle to attract and retain top talent, particularly younger professionals who often prioritise DEI.

Business impact

Businesses that deploy faulty AI systems risk investor criticism, potential penalties, and loss of market trust.

Recommendation

Improve diversity within AI teams, test the model throughout its deployment life-cycle, and ensure robust governance and human oversight.

M

Megha Kumar

CEO

Dr Megha Kumar is a reputed domain expert on geopolitics, AI and digital regulation.

LinkedIn5 published articles

Few topics in 2025 are as hot as generative AI.

 Today’s models are underpinned by advanced reasoning capabilities, enabling them to solve complex problems with logical steps, resembling the way humans think through and respond to challenging questions. 

 Such capabilities have massive potential for fields like science, maths, medicine and law. However, the success of generative AI in these fields doesn’t rely on reasoning algorithms and huge datasets alone. For AI to deliver genuine progress, inclusion must be a core priority and guiding principle of AI development and adoption. 

 That idea has come under fire in recent times, with DEI programmes often maligned as so-called ‘woke capitalism’. However, an unwillingness to engage with the issue of socio-economic inclusion is particularly perilous for companies counting on AI solutions to deliver business growth.

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