Human workers behind AI training raise new privacy concerns

AI systems rely heavily on human labour to train and improve algorithms. Images and videos collected by AI-powered devices are often reviewed and labelled by human annotators so that systems can better recognise objects, environments, and context.

This work is frequently outsourced to data annotation companies such as Sama, which provides training data services for large technology firms, including Meta Platforms. Many of these tasks are carried out by contract workers in Nairobi, Kenya, where employees review large volumes of visual data under strict confidentiality agreements.

Recent investigations have raised concerns about privacy and data governance linked to AI wearables such as the Ray-Ban Meta smart glasses, developed in partnership with EssilorLuxottica. Some device features rely on cloud processing, meaning that captured images and voice inputs may be transmitted and analysed remotely.

Workers involved in the annotation process report regularly encountering sensitive material. Footage can include scenes recorded inside private homes, bedrooms, or bathrooms, as well as images that unintentionally reveal personal or financial information.

These practices raise broader questions about transparency and cross-border data transfers, particularly when data originating in Europe or the United States is processed in other countries. They also highlight the often-hidden human role behind AI systems that are frequently presented as fully automated technologies.

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Oracle launches AI system designed to predict construction safety risks

The US tech company Oracle has introduced a new AI platform to predict safety risks across construction projects.

A system called Advisor for Safety that aims to shift industry practices from reactive incident response to predictive risk prevention.

The AI model was trained using safety information equivalent to more than 10,000 project-years across multiple project types and locations.

By analysing historical patterns, the platform generates weekly forecasts that identify projects statistically most likely to experience safety incidents.

The solution also integrates structured safety observation tools through systems such as Oracle Aconex and Oracle Primavera Unifier, allowing field teams to collect consistent data on mobile devices or web platforms.

These inputs improve predictive accuracy while enabling organisations to track potential hazards earlier in the project lifecycle.

According to Oracle, the system combines data streams ranging from incident reports and payroll records to project schedules and operational metrics.

Early adopters reportedly reduced workplace incidents by up to 50 percent and workers’ compensation costs by as much as 75 percent during the first year of use.

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Privacy lawsuit targets Meta AI glasses after reports of footage review

Meta is facing a new lawsuit in the US over privacy concerns tied to its AI smart glasses.

The legal complaint follows investigative reporting indicating that contractors working for a Kenya-based subcontractor reviewed footage captured by users’ devices, including sensitive personal scenes.

The lawsuit alleges that some of the reviewed material included nudity and other intimate activities recorded by the glasses’ cameras.

According to the complaint, the footage formed part of a data review process designed to improve the AI system integrated into the wearable device.

Plaintiffs claim Meta marketed the product as prioritising user privacy, citing advertisements suggesting that the glasses were ‘designed for privacy’ and that users remained in control of their personal data.

The complaint argues that such messaging could mislead consumers if the footage were subject to human review without clear disclosure.

A legal action that also names eyewear manufacturer Luxottica, which partnered with Meta to produce the glasses.

Meanwhile, the UK’s Information Commissioner’s Office has begun examining the issue after reports that face-blurring safeguards may not have consistently protected individuals captured in the recordings.

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Gemini leads latest ORCA benchmark on AI maths accuracy

A new round of the ORCA (Omni Research on Calculation in AI) benchmark reveals significant progress in how leading AI chatbots handle real-world mathematical problems, while also highlighting persistent limitations in reliability and consistency.

The latest results show Google’s Gemini 3 Flash moving clearly ahead of competing systems, correctly answering nearly three-quarters of the 500 practical questions used in the benchmark.

Our readers may recall that the platform previously analysed the first edition of the ORCA benchmark, examining how AI chatbots performed on everyday quantitative tasks rather than purely academic problems. The earlier analysis already showed notable gaps between systems and raised questions about the reliability of AI models for calculations people might encounter in daily life.

The second benchmark compares four widely accessible models: ChatGPT-5.2, Gemini 3 Flash, Grok-4.1 and DeepSeek V3.2. Gemini recorded the largest improvement, decisively outpacing the others. ChatGPT and DeepSeek posted smaller but steady gains, while Grok’s results declined slightly in several subject areas.

Performance improvements were uneven across domains, with Gemini showing particularly strong gains in fields such as biology, chemistry, physics and health-related calculations.

Closer examination of the errors reveals why AI still struggles with mathematical accuracy. Calculation mistakes have increased as a share of total errors, while rounding and formatting problems have decreased.

Researchers explain that large language models do not actually compute numbers in the same way that calculators do. Instead, they predict likely sequences of words and numbers, which can lead to small shortcuts during multi-step reasoning that eventually produce incorrect results.

The benchmark also highlights another challenge: instability. The same question can produce different answers when asked multiple times, even when the model initially responded correctly. Such variation reflects the probabilistic nature of AI systems.

As a result, the benchmark concludes that AI chatbots can assist with calculations but cannot yet match the consistency of traditional calculators, which always return the same answer for the same input.

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EU competition scrutiny pushes Meta to reopen WhatsApp AI access

Meta has announced that third-party AI chatbots will again be allowed to operate through WhatsApp in Europe, reversing restrictions introduced earlier this year.

The decision follows pressure from the European Commission, which had warned it could impose interim competition measures.

Earlier in 2026, Meta limited access to rival chatbot services on the messaging platform, prompting regulators to examine whether the move unfairly restricted competition in the rapidly expanding AI market.

WhatsApp remains one of the most widely used messaging applications across European countries, making platform access critical for emerging AI services.

Under the new arrangement, companies will be able to distribute general-purpose AI chatbots via the WhatsApp Business API for 12 months.

The change is intended to give European regulators time to complete their investigation while allowing competing AI services to operate within the platform ecosystem.

Meta has also indicated that businesses offering chatbots through WhatsApp will be required to pay fees to access the system.

The European Commission is now assessing whether these adjustments sufficiently address competition concerns surrounding the integration of AI services inside major digital platforms.

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OpenAI explains 5 AI value models transforming enterprise strategy

AI is beginning to reshape corporate strategy as organisations shift from isolated technology experiments to broader operational transformation.

According to OpenAI, businesses that treat AI as a collection of disconnected pilots risk missing the bigger structural change that the technology enables.

A new framework describes five value models through which AI can gradually reshape companies. The first stage focuses on workforce empowerment, where tools such as ChatGPT spread AI capabilities across teams and improve everyday productivity.

Once employees develop fluency, organisations can introduce AI-native distribution models that transform how customers discover products and interact with digital services.

More advanced stages involve specialised systems. Expert capability integrates AI into research, creative production, and domain-specific analysis, allowing professionals to explore a wider range of ideas and experiments.

Meanwhile, systems and dependency management introduce AI tools capable of safely updating interconnected digital environments, including codebases, documentation, and operational processes.

The final stage involves full process re-engineering through autonomous agents. In such environments, AI systems coordinate complex workflows across departments while maintaining governance, accountability, and auditability.

Organisations that successfully progress through these stages may eventually redesign their business models rather than merely improving efficiency within existing structures.

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Sovereign AI becomes a strategic question for governments

Governments across the world are increasingly treating AI as a strategic capability that shapes economic development, public services and national security. Momentum behind the idea of ‘sovereign AI’ is growing as countries reassess who controls the chips, cloud infrastructure, data and models powering modern technology.

Complete control over the entire AI stack remains unrealistic for most economies because of the enormous financial and technological costs involved. Global infrastructure continues to rely heavily on US technology firms, which still operate a large share of data centres and AI systems worldwide.

Policy makers are therefore exploring different approaches to sovereignty across the AI ecosystem rather than pursuing total independence. Strategies range from building domestic computing capacity to adapting global AI models for national languages, regulations and public services.

Several countries already illustrate different approaches. The EU is investing billions in AI infrastructure, Canada protects sensitive computing resources while using global models, and India prioritises applications that serve its multilingual population through public digital systems.

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AI adoption and jobs debated at India summit

Governments, companies and international organisations gathered in India in February for the AI Impact Summit to discuss the future of AI governance and adoption. Participants in India focused on economic impacts, labour market changes and sector specific uses of AI.

Delegates in India also highlighted growing interest in international cooperation on AI governance. Ninety one countries endorsed a declaration supporting shared tools, global collaboration and people centred development of AI.

Language diversity became a central topic during discussions in India. India’s government announced eight foundation AI models designed to support generative AI across the country’s 22 recognised languages.

Debate in India also reflected the growing influence of the Global South in AI policy discussions. Policymakers and experts in India emphasised infrastructure gaps, language diversity and local economic realities shaping AI adoption.

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ECB reports minor impact of AI on employment

AI has so far had only a small effect on employment across Europe, according to economists at the European Central Bank. A comparison of 5,000 firms- both AI users and non-users- showed no significant difference in job creation or reduction.

Some firms that use AI intensively were even four percent more likely to hire new staff than average.

Economists noted that AI investment has not replaced existing jobs. In some cases, firms are hiring additional employees to develop and implement AI systems or to scale up operations more efficiently.

Only a minority of firms, around 15 percent, reported reducing labour costs as a motivation for AI adoption.

Despite limited impacts so far, the ECB cautioned that AI could have more significant effects as technology matures. Firms that specifically invest in AI to cut jobs may indeed reduce employment, and the long-term consequences for production processes and labour markets remain uncertain.

The findings come amid rising concern over AI-driven job losses, with companies such as Amazon and Allianz citing AI as a reason for recent cuts. Markets reacted negatively last week after a viral post predicted widespread layoffs, though current evidence shows only minor effects.

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Growing risks from AI meeting transcription tools

Businesses across the US and Europe are confronting new privacy risks as AI transcription tools spread through workplaces. Tools that automatically record and transcribe meetings increasingly capture sensitive conversations without clear consent.

Privacy specialists warn that organisations in the US and Europe previously focused on rules controlling what employees upload into AI systems. Governance efforts now shift towards monitoring what AI tools record during daily work.

AI services such as Otter, Zoom transcription and Microsoft Copilot can record discussions involving performance reviews, health information and legal matters. Companies in the US and Europe face legal exposure when third-party platforms store recordings without strict controls.

Governance teams in the US and Europe are being urged to introduce clear rules on meeting recordings and retention of transcripts. Stronger policies may include consent requirements, limits on recording sensitive meetings and stricter data storage oversight.

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