AI tool put to test sifting public views on botox and fillers
Chris Vallance
Senior Technology Reporter Getty Images
An artificial intelligence (AI) tool has been used to sort through comments about botox and lip fillers, that were submitted as part of a public consultation, in what officials said was the first use of this kind in the UK.
Officials set the tool to work sifting responses to a Scottish government consultation on regulating non-surgical cosmetic procedures.
They found it came up with 'nearly identical' results, when compared to humans set the same task.
It is hoped the tool, dubbed 'Consult', will spare civil servants from similar time-consuming tasks in future, and save taxpayers an estimated £20m.
Consult is one of a planned set of government AI-powered tools that have collectively been dubbed 'Humphrey' after the wily senior civil servant, Sir Humphrey Appleby, from the classic 1980s sitcom Yes, Minister. The series often took aim at excessive bureaucracy in government.
In this trial the AI tool examined 2,000 submissions. But public consultations, which gather the views of UK citizens on issues under consideration by ministers, can generate tens of thousands of responses.
It was able to identify themes amongst the responses, and counted and catalogued answers accordingly – with human experts checking its work at both stages.
Consult's findings were then examined to see how they compared to a team of human experts working in parallel.
Technology secretary Peter Kyle said the initial success of the trial meant that Consult would be used across government 'very soon'.
'After demonstrating such promising results, Humphrey will help us cut the costs of governing and make it easier to collect and comprehensively review what experts and the public are telling us on a range of crucial issues,' he wrote.
The government hopes £45bn can be saved by wider public sector use of AI technology. 'Humans in the loop'
The government says that Consult is currently still in its trial stage and more evaluation will take place before any final decision to roll it out more widely.
There would always be 'humans in the loop' checking Consult's work, the government added.
Officials have also sought to address some of the persistent concerns about AI systems.
One is that they sometimes invent information – a failing known as 'hallucinating'.
Because the AI was only being asked to carry out a relatively limited task, officials said hallucination would not be a major problem.
Such AI tools, built using what are known as 'large language models' have also displayed bias, as they adsorb the prejudices inherent in the human-generated data on which they are trained.
But experts who worked with Consult had found it reduced bias overall, the government said, by removing opportunities for individual human analysts to 'project their own preconceived ideas'.
Consult has also been tested to check it can handle language containing spelling mistakes and other errors.
However, for now it only works in English, and responses in other languages spoken within the UK such as Welsh would need to be translated into English first.
READ SOURCE
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Yahoo
14 minutes ago
- Yahoo
Trump wants to 'liberate' Los Angeles, residents say 'no thanks'
By Brad Brooks (Reuters) -U.S. President Donald Trump says he sent in the National Guard and Marines to "liberate" Los Angeles from the violence of protesters, but some residents of Little Tokyo, a neighborhood hit hardest by the unrest say "no thanks" Mr President. A dozen people who live, work or frequent the neighborhood, where Japanese is heard spoken as frequently as English in shops and restaurants, on Tuesday told Reuters that Trump's use of the military was inflaming the protests against recent immigration raids in Los Angeles. "The president sending in the National Guard and Marines has only made things worse, it's made the protesters go crazy," said Sulieti Havili, who lives nearby and helps run a Pokemon club with over 6,000 members that routinely plays in Little Tokyo. "They are doing nothing to protect this community." Havili, 25, who was out picking up trash in Little Tokyo with her Pokemon club partner Nolberto Aguilar, 42, said it was clear to her that Trump sending in thousands of military personnel had "brought out the worst in the protesters" and only served to aggravate the situation. Aguilar added that Trump's deployment of troops was thwarting the will of most Los Angeles citizens and local leaders, fanning the flames created by ICE immigration raids, that he said targeted law-abiding immigrants. The Trump administration says its immigration raids are rounding up de facto criminals for lacking proper documents to stay in the United States. California Governor Gavin Newsom has said the use of military personnel to combat the protests had "inflamed a combustible situation" and warned that "democracy is under assault." Los Angeles Mayor Karen Bass has said that Trump's use of troops was a deliberate effort to create "chaos." The Trump administration strongly rejects the accusations and says their actions were needed in the face of local and state leaders inability to get the situation under control. 'INSTIGATING' ACTION Little Tokyo, a charming neighborhood with shops selling Japanese goods and restaurants serving up some of the city's finest sushi, abuts the federal buildings where protesters have gathered for five nights in a row. For the past two nights, when police and National Guard troops have forcibly dispersed protesters in the early evening, demonstrators scatter into smaller groups into Little Tokyo, which has been covered with anti-ICE and anti-Trump graffiti. Running skirmishes well into the night in the neighborhood has seen police use booming flash-bangs and firing other "less lethal" munitions at protesters. Several of those interviewed in Little Tokyo asked that they not be named, saying they feared reprisals - from the federal government. Many were immigrants themselves or had family members who were not born in the U.S., and feared making themselves or their loved ones targets of ICE. One of those was Anthony, who works at a tea shop in the neighborhood. "There is no question that the president sending in thousands of National Guard and 700 Marines has done nothing but make the protesters more aggressive," he said. "It's instigating the protesters and making things worse." Samantha Lopez, a descendent of Filipino immigrants who works at a Korean ice cream parlor in Little Tokyo, said she felt empathy for the demonstrators but rejected any acts of vandalism or violence they carry out. Still, she blamed the use of military personnel for creating the unrest. "It's just poor handling of protests that stay peaceful until they're confronted by officers," Lopez said. "It's bad for business, and it's bad for this neighborhood."
Forbes
20 minutes ago
- Forbes
Why Multilingual AI Isn't The Same As Global-Ready
Alessa Cross is on the founding team at Ventrilo AI. getty Despite advances in multilingual modeling, most language AI systems remain anchored in English. Earlier generations, such as GPT-3, drew over 90% of their pretraining data from English sources, and even newer models inherit structural and linguistic biases shaped by English-dominant internet content. The result is a persistent gap between models that can speak many languages and those that can function across linguistic contexts. Yet in enterprise settings, it remains common to treat these systems as "global-ready" out of the box. When we ask these models to serve users around the world, we're regularly asking them to operate beyond their training context. The results may still be grammatically correct, but they're often structurally or culturally misaligned. If we want AI to be more than an English-speaking assistant with a multilingual dictionary, we must move past the illusion that translation equals localization. That begins with recognizing how deeply English-centric assumptions shape model behavior, and what it takes to build systems that scale across different languages and cultural domains. Language models perform best in the languages they see most during training. As linguistic distance from English increases, so does performance degradation, especially in languages with different syntactic rules or limited training representation. In lower-resource languages, models often misread tone or intent. A polite inquiry in Hindi or Japanese may be interpreted as vague or indecisive. Still, many benchmarks, evaluation datasets, annotation protocols and UX assumptions are also designed around American English. Early voice assistants illustrate this well. Early versions of Siri struggled significantly with Arabic and regional dialects despite strong performance across the board. The issue was that the model failed to reflect how speakers naturally structure requests. In many cases, users were forced to adopt English-like phrasing just to be understood, which is a complete reversal of the human-computer relationship. In human-centered design, technology is meant to adapt to the user, not the other way around. Instead of augmenting communication, the system becomes another barrier to it. Why Localization Is Not Translation Translating prompts into other languages does not localize a product. Localization is a systems-level redesign that touches the underlying model assumptions, UX patterns and even architecture. Consider, for example: • Design Accommodations: Sentence length and structure vary significantly by language. German UI text can be up to three times longer than its English equivalent, which often requires layout redesigns to prevent truncation or overlap. • Tone And Register Calibration: What reads as efficient in American English might come across as curt or even disrespectful in Japanese. Nuanced tone calibration requires cultural familiarity. • Contextual Expectations: A summarization tool for U.S. hospitals must parse SOAP notes and colloquial shorthand. The same tool in France will need to work with structured, coded documentation and adhere to GDPR constraints. When these factors are ignored, linguistic gaps can grow into usability failures, and eventually into market rejection. Why Domain Matters As Much As Language Language is just one dimension. For enterprise users, especially in regulated sectors, functional alignment often matters more than linguistic fidelity. In the U.S., enterprise AI may be expected to integrate with Salesforce and Slack. In China, workflows depend on WeChat Work or regional CRMs. In India, workflows might span WhatsApp and legacy accounting platforms. When AI ignores these distinctions, it becomes a tool that teams work around, rather than one that augments their productivity. In healthcare or finance, a mismatch can be far more costly. Even low-level decisions, such as whether your retrieval system applies a single global index or language-specific pipelines, shape usability. How does it prioritize query routing when users include partial translations? These decisions impact latency and quality, even before any output is generated. Designing for global use means understanding not just language, but the lived context of work. Accepting The World's Inputs Most language models are trained on relatively clean, curated data. Real-world inputs are anything but. Especially in multilingual markets, users frequently mix languages and use non-standard grammar, local idioms, creative spelling and hybrid sentence structures. Systems need to account for: • Robust normalization without flattening distinctions essential to meaning • Fast, on-device language detection to route inputs to the right models quickly and accurately • Region-specific preprocessing, because what counts as 'clean' input in one market might erase meaning in another For many users and global contexts, how well your AI handles edge cases is a prerequisite to usability. The Feedback Loop As Core Architecture No global AI product will be perfectly localized on day one. But systems that can learn from localized usage improve quickly if the infrastructure supports it. When translation models are built using user-generated content and a corpus which closely mirrors real-world usage, they consistently outperform models trained on broad, general-domain data. These results show the value of tuning your translations to what your users are saying, and suggest that adding direct feedback loops could improve quality even further, and more quickly. Enabling that learning requires infrastructure that can: • Capture user edits and corrections • Cluster user abandonments and rephrased queries • Feed localized usage patterns back into training • Surface divergence between user expectations and model output Often, what separates a brittle AI language product from a scalable platform is how effectively it can learn and improve from real usage. Redefining 'Global-Ready' Global readiness is a question of whether your AI product feels intelligent and usable to users who don't share your default assumptions. That requires investment across four distinct axes: • Language: Beyond the translation layer, design systems that feel native in syntax, tone and usage. • Domain: Align with local workflows, documentation styles, data structures and regulatory standards. • Input: Engineer systems to understand how users actually speak and write. • Feedback: Treat user interactions as training data. Build infrastructure that allows products to learn from localized usage, especially when they deviate from expected patterns. AI products that ignore how global teams actually work tend to stall at the edge of familiar markets. Whether they scale into global workflows or stay locked in English-first regions often comes down to investments made early in design. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Axios
32 minutes ago
- Axios
Pella marks 100 years with new, diverse workforce
As Pella Corporation celebrates its centennial this year, the Iowa-based window and door manufacturer is embracing a very different workforce than the one it started with. Why it matters: Founded in a predominantly Dutch and majority-white community, Pella Corp. has adapted to a changing America and diversified its manufacturing workforce in response to ongoing labor shortages. Zoom out: The U.S. manufacturing industry is facing a historic labor shortage, with an estimated gap of 4 million jobs by 2030, says Karmen Gardner, spokesperson for Pella Corp. State of play: Over 30% of workers at Pella's manufacturing plants in places like Carroll and Sioux Center are Hispanic, and the company is investing in making its workplace more inclusive for those who speak English as a second language, Gardner says. Pella has rolled out bilingual training for English and Spanish speakers, Spanish-translated instructions, and handheld translation devices called Pocketalks on the factory floor. It also launched a new internal platform, Pella Connect, which allows employees to read company messages in their preferred language. Zoom in: Between 2017 and 2022, the number of Pella's ELL employees at the Sioux Center plant grew from none to about a third of its roughly 400 employees, per the N'West Iowa Review. Before the changes, non-English speakers would not have been considered for employment, a Sioux Center production manager told the Review. However, the changes have occurred as the company seeks to expand its market share nationwide. By the numbers: The company employs 10,000 people and operates 18 manufacturing plants, with growth plans targeted in the South and West, where populations are growing, Gardner says. Most of the company's recent growth has occurred outside of Pella, according to the Business Record. They've expanded their product offerings to include hurricane-impact-rated windows and doors, and have increased their manufacturing footprint in locations such as Miami and California. In Iowa, labor shortages are consistently listed as a top concern by business leaders. The intrigue: The company was founded in 1925 by Pete Kuyper, a Dutch businessman who invented a window screen that rolled up and down like a shade. Pella, a "city of refuge" mentioned in the Bible, was named that after hundreds of Dutch immigrants came to Iowa seeking religious freedom. And that name still holds meaning with the company's diversifying workforce, Gardner says. Yes, but: With the Trump administration's immigration crackdown, Gardner notes that immigrant workers have been important for America as a whole "... and certainly our businesses as well."
