Introducing SAFERAI: Safety Advancing Federated Estimation of Risk using Artificial Intelligence.

In an era where safeguarding data privacy and security are paramount, the convergence of privacy preserving machine learning and IoT technology presents both immense opportunities and daunting challenges. The need to prioritise trustworthy AI through development that is robust, explainable and transparent is an urgent priority.

Recognising this pivotal moment, OctaiPipe, a pioneer in federated edge AI innovation, has joined forces with industry leaders P&G, SME partners Site Assist, Raiven and Digital Catapult and InnovateUK to deliver a groundbreaking SaferAI concept. groundbreaking concept for safer AI

“Bringing together SMEs, large organisations and research organisations, these novel solutions will demonstrate how trusted AI and machine learning technologies can aid and be incorporated into many of the UK’s industries and sectors.”

Dr Kedar Pandya, UKRI Technology Missions Fund Senior Responsible Owner

“Federated Learning with OctaiPipe is unlocking potential not yet realized in industry. It’s enabling competing business to collaborate on the use of technology and AI for good. Specifically, within SaferAI, I can see significant value in utilizing federated learning to share model results for predicting safety incidents and risks. I can see this changing approaches in industrial safety. Before, we could only cooperate on best practices, technology or processes, but now we can actually prevent incidents with the use of AI and shared data.”

Christoph Wagner-Gillen, P&G Product Supply HSE Governance

SAFER AI

SaferAI, or as the projects catchier title suggests, Safety advancing federated estimation of risk using AI, is a 12-month, £1.64m project focused on the development of a highly comprehensive federated learning model for predicting incident occurrences and risk.

SaferAI is an industry first, enabling world leading consumer goods manufacturer, P&G, to collaborate with other interested Consumer Goods Companies to forecast safety incident rates to reduce Health & Safety (H&S) incidents.

Until now, with low event occurrence in any single setting, there has been insufficient data availability to predict future incident events or near-misses. However, using federated learning with OctaiPipe, multiple large-scale parties with similar Health, Safety and Environment (HSE) situations can now collaborate to pool intelligence to achieve scale.

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HSE in the workplace

Many organisations already collect high-level operational and HSE incident data intelligence through various Industrial IoT devices and cameras to successfully predict safety incidents. However, analytics based on this is not meaningfully actionable to drive changes that preventatively reduce risks.

For predictions to be meaningfully actionable, they must be made at a sufficient level of granularity within a factory or workgroup.

Whilst we’re fortunate that HSE critical events are rare within single sites or even organisations, it means insufficient data exists to employ ML models capable of predicting when and why H&S incidents might occur so they can be prevented.

To date, progress towards an AI-enabled solution has been impeded by:

    a) a requirement for more observations than one organisation can generate alone, so it is imperative to share data, and

     b) barriers to sharing data across organisations that, until now, have not been overcome.

Federated learning solves this. SaferAI will enable organisations to combine data to facilitate actionable incident predictions for small work groups.

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‍Federated Learning

Federated Learning is a privacy preserving machine learning technique that allows for machine learning models to be trained across multiple distributed edge devices without the need to see or move the data.

It is a key technology in the convergence of AI, IOT and connectivity as it enables edge intelligence whilst safeguarding sensitive or private data.

Federated Learning will be used the project to establish a viable on-device (Edge) AI system that pairs privacy-focused telemetry and computer vision (CV) systems. It will also enable continual collaborative learning in edge environments, combining model results provided by collaborating organisations to yield a highly comprehensive HSE model.

During SaferAI, OctaiPipe are further developing and validating as a high-trustworthiness FL-for-IoT system along with a high-accuracy model that uses observed use case data generated by AIoT camera systems. This solves the challenge of predicting safety critical incidents, near misses and hazardous/non-compliant conditions in industrial settings, enabling organisations to pre-emptively take remedial/mitigating actions to reduce H&S risks.

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