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TRANSPARENT AI
OpenEthics.ai
November 2020charliepownall.com
CHARLIE POWNALL

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• 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
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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

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General public AI concerns (USA)
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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

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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

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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

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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

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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

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The dangers of AI transparency
• Loss of IP
• Loss of competitive advantage
• Failure to meet higher expectations
• Reputational risk

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Useful resources
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• 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

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THANK YOU.
cp@charliepownall.com
linkedin.com/in/charliepownall
charliepownall.com