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Blog Posts (16)

  • We sent our Founder, Simon Greenman, to Number 10 to Talk AI

    Very proud that our firm Best Practice AI was invited to participate in a roundtable discussion at Number 10 on AI and digital trade with George Hollingbery, Minister of State for Trade Policy.

  • Best Practice AI at World Economic Forum's Global AI Council Meeting with DCMS minster Jeremy Wright

    Honoured that Simon Greenman, Co-Founder and Partner at Best Practice AI, is on the Global AI Council of the World Economic Forum. Minister Jeremy Wright of the UK Department for Digital, Culture, Media and Sport chaired the council's meeting on Tuesday the 11th of June to discuss its directions and priorities.

  • Healthily and Best Practice AI publish world’s first AI Explainability Statement reviewed by the ICO

    LONDON, Fri 17th Sep, 2021 - One of the world’s leading AI smart symptom checkers has taken the groundbreaking decision to publish a statement explaining how it works. Healthily, supported by Best Practice AI together with Simmons & Simmons and Jacob Turner of Fountain Court Chambers today publish the first AI Explainability Statement to have been reviewed by the UK Information Commissioner’s Office (ICO). The Healthily AI Explainability Statement explains how Healthily uses AI in its app including why AI is being used, how the AI system was designed and how it operates. The statement, which can be viewed here, provides a non-technical explanation of the Healthily AI to its customers, regulators and the wider public. Around the world, there is a growing regulatory focus and consensus around the need for transparent and understandable AI. AI Explainability Statements are public-facing documents intended to provide transparency, particularly so as to comply with global best practices and AI ethical principles, as well as binding legislation. AI Explainability Statements such as this are intended to facilitate compliance with Articles 13, 14, 15 and 22 of the GDPR for organisations using AI to process personal data. The lack of such transparency has been at the heart of recent EU court cases and regulatory decisions, involving Uber and Ola in the Netherlands and Foodinho in Italy. Healthily, a leading consumer digital healthcare company, worked with a team from the AI advisory firm, Best Practice AI, the international law firm Simmons & Simmons, and Jacob Turner from Fountain Court Chambers to create the first AI Explainability Statement in the sector. They also engaged with the ICO. A spokesperson for the ICO confirmed: “In preparing its Explainability Statement, Healthily received feedback from the UK’s data protection regulator, the Information Commissioner’s Office (ICO) and the published Statement reflects that input. It is the first AI Explainability Statement which has had consideration from a regulator. The ICO has welcomed Healthily publication of its Explainability Statement as an example of how organisations can practically apply the guidance on Explaining Decisions Made With AI”. Matteo Berlucchi, CEO of Healthily said: “We are proud to continue our effort to be at the forefront of transparency and ethical AI use for our global consumer base. It was great to work with Best Practice AI on this valuable exercise.” Simon Greenman, Partner at Best Practice AI, said: “Businesses need to understand that AI Explainability Statements will be a critical part of rolling out AI systems that retain the necessary levels of public trust. We are proud to have worked with Healthily and the ICO to have started this journey.” To learn more about how Best Practice AI, Simmons & Simmons LLP, and Jacob Turner from Fountain Court Chambers built the AI Explainability Statement, please contact us below.

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  • AI Use Case | Automate syncing of sales process data such as calendars and contact reports

    < back AI Use Case Automate syncing of sales process data such as calendars and contact reports Automatically sync calendar, address book, emails, phone calls and messages to the salesforce so as to optimise time focus. Function Sales Sales Operations Benefits Operational - Activity (eg calendar) syncing Case Studies ASOS.com~ASOS eliminates the risk of inaccurate data entering finance systems by implementing a machine learning solution for invoice handling,"Acxiom~Acxiom improves opportunity-to-close problems for their sales team, claiming near-100 percent renewal rate, with machine learning " Industry ​ ​ Data Sets Structured / Semi-structured AI Technologies ​ Potential Vendors Celaton,Clari

  • AI Use Case | Identify fraud, waste, and abuse patterns from clinical and operations data

    < back AI Use Case Identify fraud, waste, and abuse patterns from clinical and operations data Identify fraud, waste, and abuse patterns from a variety of clinical operations data. This would typically occur at a cross-functional level, but could be at an individual, operating unit or institutional level. Function ​ ​ Benefits Cost - Reduce wastage,Cost - Fraud reduction Case Studies Sheba Medical Center~Sheba Medical Center ensures accuracy of prescriptions and eliminates human error using machine learning Industry Healthcare Healthcare Providers And Services Data Sets Structured / Semi-structured,Images AI Technologies ML Task - Grouping - Anomaly Detection,Machine Learning (ML) Potential Vendors ​

  • AI Case Study | SignAll_translates_spoken_English_into_sign_language_enabling_better_communication_for_hearing_impaired_using_vision_processing_and_natural_language_processing

    < back AI Case Study SignAll translates spoken English into sign language enabling better communication for hearing impaired using vision processing and natural language processing Using vision processing to understand hand movements and body language, machine learning and natural language processing SignAll has developed a proprietary platform to translate spoken language into written messages or hand signals enabling easier communication for people who have hearing trouble. Industry Public And Social Sector Ngo Project Overview "Sign Language is a complex and nuanced language that consists of different elements. To translate accurately, it is imperative to be able to detect: Body movement, facial expressions, hand shapes. SignAll has developed innovative patent-pending technology combining: Computer vision, Machine learning, Natural-language processing algorithms. Reported Results POC; Results not yet available Technol ogy ​ Function R And D Product Development Background "Over 100 million people are unable to hear. Being deaf from birth or childhood, many of these people use sign language as their primary form of communication." Benefits ​ Data ​

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