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Bami Iroko

Bami is marketing and communications strategist, talent manager, author, and creative consultant whose work spans globally. She helps brands, organizations, and artists tell impactful stories that influence decisions, drive growth, and create meaningful connections across cultures.

Her career bridges marketing, entertainment, and technology—bringing a unique blend of strategy, creativity, and purpose to every project. Additionally, she manages and consults for creative talent across industries, supporting brand development, partnerships, and audience engagement.

A champion for inclusive tech and AI responsibility, Bami is passionate about shaping a future where innovation, creativity, and equity coexist.

Marketing Manager @ XWP

Social Media Strategist | Consultant

Author

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The Bias in Motion

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When technology doesn’t see you,

it can’t serve you.

Technology mirrors its makers. Every dataset, every algorithm, every interface tells a story about who is seen, and who isn’t. When representation is missing, the systems we build start to forget entire communities. But visibility is power — and the future depends on how we choose to design it.

Visibility is power, and invisibility is a design choice.

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Seeing ourselves in the Machine

Technology reflects the people who build it — their values, perspectives, and blind spots.�
When representation is missing, systems can’t serve everyone equally.�
One of our clients, iOne Digital, partnered with a global image platform became a catalyst for redefining representation in digital media.

It reflects the people who build it — their choices, their biases, their blind spots.

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The Bias in the Image

AI image tools depict white faces with higher accuracy than Black or Asian ones (SpringerLink, 2023)�
Facial recognition systems misidentify darker-skinned women 35% more often than white men (MIT Media Lab, Joy Buolamwini study)�
AI-generated visuals replicate narrow beauty norms — lighter skin, straighter features, Eurocentric standards (MDPI, 2024)

AI is only as inclusive as its training data.

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Algorithmic Beauty & Identity

2025 Journalism & Media study: AI-generated “professionals” were 75% white and male.�
Non-normative bodies, skin tones, and gender identities were underrepresented or sexualized.�
Beauty and professionalism were algorithmically coded — bias disguised as aesthetics.�
This shapes audience perception of credibility and worth.

Generative AI amplifies normative visuals unless disrupted.

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Representation as Infrastructure

Getty’s Inclusive Visual Language Project rebuilt tagging systems for accessibility.�
Diverse images became discoverable — not hidden behind generic search terms.�
Inclusion embedded from concept to curation, not added post-production.�
Real change happens at the system level, not in marketing campaigns.

Representation isn’t a side project. It’s the structure itself.

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The Bigger Picture

70% of global AI training data originates from the U.S. and Western Europe. (Stanford AI Index, 2024)�
Limited geographic diversity → limited imagination.�
MIT (2025): Global “content homogenization” flattens culture into sameness.�
Innovation suffers when only one worldview trains the world’s machines.

Bias in the dataset becomes bias in the world.

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Editorial Strategies That Prioritize Mission Over Metrics

Metrics measure reach; mission measures resonance.�
Editorial purpose must guide what AI assists — not the reverse.�
Storytelling grounded in community values sustains trust.�
Virality fades; integrity lasts.

Lead with purpose, not performance.

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Integrating AI Without Compromising Identity

AI can mimic tone but not culture.�
Transparency in process ensures accountability in output.�
Protect voice, context, and nuance — teach your tools your ethics.�
Review AI content as critically as human drafts.�
AI can mimic style, but only humans can protect identity.

Use the tool, don’t become it.

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Advocating for Better Training Data and Platform Change

Data governance is the new diversity frontier.�
Ask: Who built this dataset? Who benefits from it? Who’s missing?�
Push for transparency and inclusive sourcing in AI models.�
The future of ethical AI depends on collective advocacy.

Representation is a responsibility.

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