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Content type: Explainer
Behind every machine is a human person who makes the cogs in that machine turn - there's the developer who builds (codes) the machine, the human evaluators who assess the basic machine's performance, even the people who build the physical parts for the machine. In the case of large language models (LLMs) powering your AI systems, this 'human person' is the invisible data labellers from all over the world who are manually annotating datasets that train the machine to recognise what is the colour…
Content type: Advocacy
AI-powered employment practices: PI's response to the ICO's draft recruitment and selection guidance
The volume of data collected and the methods to automate recruitment with AI poses challenges for the privacy and data protection rights of candidates going through the recruitment process.Recruitment is a complex and multi-layered process, and so is the AI technology intended to service this process at one or all stages of it. For instance, an AI-powered CV-screening tool using natural language processing (NLP) methods might collect keyword data on candidates, while an AI-powered video…
Content type: Examples
In November 2018, worried American parents wishing to check out prospective babysitters and dissatisfied with criminal background checks began paying $24.99 for a scan from the online service Predictim, which claimed to use "advanced artificial intelligence" to offer an automated risk rating. Predictim based its scores in part on Facebook, Twitter, and Instagram posts - applicants were required to share broad access to their accounts - and offered no explanation of how it reached its risk…