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Transparent AI


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A brief overview of the principles, practices and risks of AI governance and ethics transparency, from a reputational and communications perspective

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Transparent AI

  2. 2. • Independent reputation and communications advisor, trainer, speaker focused on AI, cyber, and social media • Technology & telecoms; healthcare, pharma & life sciences; business & professional services; start-up & early stage • UK-based, with Europe, Middle- east and Asia-Pacific experience and footprint About Charlie Pownall • Author, Managing Online Reputation (Palgrave Macmillan, 2015) • Fellow, Royal Society of Arts • Faculty, Center for Leadership & Learning, Johnson & Johnson • Chairman, Communications & Marketing Committee, American Chamber of Commerce Hong Kong, 2012-2015 • Formerly: European Commission, Reuters, SYZYGY AG, WPP plc, Burson-Marsteller 2
  3. 3. 3 Perceptions of AI • Widely divergent views on AI between different stakeholders, notably industry and general public/consumers • High general public/consumer awareness, low understanding • Widespread concerns about privacy, cyber attacks, manipulation, dis/misinformation, equality and human rights, (un)employment
  4. 4. General public AI concerns (USA) 4
  5. 5. 5 Why trust in AI is low • ‘Black box’ AI/algorithmic systems and opaque research • Inadequate understanding of AI functionality, competence, risks, limitations • Sensationalist/alarmist media coverage on (un)employment, surveillance, killer robots, geo-politics, etc • Many myths and misconceptions
  6. 6. 6
  7. 7. 7 Changing AI landscape • AI use is fully embedded in everyday life • AI/algorithms are regular headline news • More general public/consumer/end user backlashes • Plethora of active NGOs/civil society organisations • Pressure on AI developer/manager and academic/researcher responsibility and accountability • Prospect of government intervention • Broader, deeper understanding of AI limitations
  8. 8. 8 Components of trustworthy AI are emerging • Traceability/verifiability and explainability/interpretability help experts make AI systems safer and fairer, and understand AI decision-making • Strong governance and ethics help organisations develop and manage more appropriate AI systems and be more accountable • AI/algorithms remain fundamentally opaque and confusing to the general public/consumers, and perceived accountability remains low
  9. 9. 9 Principles of AI transparency • Put values, ethics, and transparency at the centre of AI governance • Involve directly impacted stakeholders in AI design and testing • Monitor and strengthen AI governance and technology continuously • Communicate clearly, honestly, consistently, regularly, from the start • Think laterally about unexpected consequences
  10. 10. 10 AI transparency in practice – communications • Governance communication – People, policies, protocols, process, strategy, etc – Stakeholder involvement, incl. suppliers – Datasets, toolkits, tools • Product/service communication – Purpose, objectives, context, technology, data processing, outcomes – Risks/limitations, privacy, human oversight, ownership • Incident and crisis management – Preparation, response, recovery – Learning, and acting upon lessons learned
  11. 11. 11 The dangers of AI transparency • Loss of IP • Loss of competitive advantage • Failure to meet higher expectations • Reputational risk
  12. 12. Useful resources 12 • AI and robotics perception research Recent research studies on perceptions on and trust in AI and robotics amongst the general public, consumers, patients, politicians, business, employees and other stakeholders • AI and algorithmic incident and controversy repositry An open registry of AI-driven incidents and controversies used by journalists, researchers, NGOs, businesses and others for reference, research and product/service development
  13. 13. THANK YOU.