Who Is Accountable When Anyone Can Build AI?
As building with AI gets easier, more people take consequential actions with it. Four questions to answer before you build or deploy anything with AI in it.
I have attacked retrieval-augmented language models in research and now work in AI regulatory research. My essays ask who is accountable when AI fails, what consent means when nobody reads the terms, and when human oversight exists only on paper.
If AI saves us time, what are we doing with the time it saves?
We are trying to govern a technology while our relationship with it is still being negotiated.
A person can stay formally in control of a decision while losing the confidence to judge it.
As building with AI gets easier, more people take consequential actions with it. Four questions to answer before you build or deploy anything with AI in it.
One email when a new essay is published. Nothing else.
Solving overlooked problems.
Most AI systems used in Nigeria are built elsewhere. I am working on what a country needs in order to test those systems itself, before and after deployment, instead of relying on the developer's own claims.
Under way.
I built a hybrid retrieval pipeline (FAISS, TF-IDF, cross-encoder reranking, FLAN-T5), attacked it with synonym substitution and word deletion, and scored the damage with an Adversarial Risk Index I developed.
Completed as my undergraduate thesis.
Read the projectA study of public discussion of AI in Nigeria, using a data collection service I built for Bluesky that can be extended to news and video platforms.
Under way.
I build products for markets that are usually served last. Quotla handles business paperwork for owners without technical staff, and Testifi gives people control over how their personal stories are shared.
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I study how AI systems can be tested and held to account after they leave the lab, especially where they are bought rather than built. My work draws on security evaluation and on frameworks such as the NIST AI RMF and the EU AI Act.
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I design brand systems and visual explanations, so that technical work can be understood by the people who have to act on it.
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I have held leadership roles in organisations of different sizes, in agriculture, confectionery and technology. I speak about what those roles taught me, and about AI for audiences who have to make decisions about it.
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