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AI and data
The human work behind AI: annotation, evaluation, moderation and automation support.
Latest posts
6 posts
AI and dataSpeech data for African languages and accents: why it matters and how it is collected
Speech models still struggle with African languages and accents. Here is how diverse, consent-tracked speech data is recorded, transcribed and checked.
2 min read
AI and dataThe human exception team behind your automation
Why automation projects stall on exceptions, and how a human team that reviews low-confidence outputs, monitors bots and categorises failures keeps workflows moving.
2 min read
AI and dataContent moderation done right: consistency, context and moderator wellbeing
What good content moderation looks like in practice, from policies and enforcement matrices to appeals, reporting and the safeguards that protect moderators.
3 min read
AI and dataHuman-in-the-loop chatbots: when the bot should hand over
How to design the hand-off between a chatbot and a human team, so customers never repeat themselves and every escalation makes the bot better.
3 min read
AI and dataRLHF explained: how human feedback trains AI models
A clear explanation of reinforcement learning from human feedback, what evaluators actually do, how consistency is maintained and how to run an evaluation programme.
3 min read
AI and dataWhy AI still needs trained human data annotators
As models improve, human judgement moves up the stack rather than disappearing. Here is where trained annotators still matter, and what separates good training data from bad.
3 min read
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