Weitere ähnliche Inhalte Ähnlich wie Building trust and accountability - the role User Experience design can play in Artificial Intelligence (20) Mehr von Pistoia Alliance (20) Kürzlich hochgeladen (20) Building trust and accountability - the role User Experience design can play in Artificial Intelligence1. 18 September, 2019
BUILDING TRUST AND ACCOUNTABILITY:
THE ROLE USER EXPERIENCE DESIGN CAN
PLAY IN ARTIFICIAL INTELLIGENCE
Speakers:
• Simon Fortenbacher, Director of User Experience and Design, GSK
• Gergely Szabo, Senior UX/UI Designer, Elsevier
• Kirk Brote, Kirk Brote Consulting
Moderator: Paula de Matos, Project Manager and UX Consultant, UXLS
4. ©PistoiaAlliance
UXLS 2020 Conference
4
Location: Boston, USA
Dates: Spring 2020 (TBC)
Wish to be on the mailing list
email:
paula.dematos@pistoiaalliance.org
https://www.pistoiaalliance.org/news/inspirational-2019-ux-conference-for-the-life-sciences/
5. ©PistoiaAlliance
Coming up: 22.10.2019 Boston Pistoia Alliance FAIR
Implementation and AI/ML Workshop
5
• Morning workshops:
o FAIR Implementation
o AI Best Practices Project Team
o Assay FAIR Data Annotation Project Team
• Plenary talk by Dr. John Overington, CIO, Medicines Discovery Catapult,
UK
• Panel discussion with Dr. Overington; Lihua Yu, PhD, President, H3
Biomedicine Inc; and Al Wang, Head, IT Business Partner, Translational,
Bristol-Myers Squibb
• Evening reception, posters, launch of datathon #2
Register today! : https://www.eventbrite.co.uk/e/pistoia-alliance-fair-implementation-and-
aiml-workshop-tickets-62986729002
6. ©PistoiaAlliance
Learn how
leading industry
experts are
today achieving
practical
benefits from
data integration
and predictive
analytics.
• Learn how techniques and data are
being used to deliver results and
return on investment
• Gain a broad understanding of
how machine learning and
semantic data integration fit
together in the context of drug
discovery
• Put the buzzwords into context
and understand their true meaning
and relevance to your work
https://contechlive.com/product/contech-pharma-delegate-tickets/
• Special offer for Pistoia Alliance members – enter code PISTOIA for
an exclusive discount on delegate tickets.
8. ©PistoiaAlliance
Introduction to the speakers
818 September, 2019
Simon Fortenbacher
Director of User Experience
and Design, GSK
• Leads a team of UX
Designers
• One of the project
founders of UXLS
Gergely Szabo
Senior UX Designer, Entellect
• Responsible for the UX
Research, User Experience
and UI of a series of
Life Science-related ML-
projects at Entellect.
• Formerly of Springer Nature
Kirk Brote
Director of Kirk Brote
Consulting
• UX consulting and design in
genomics, AI, healthcare,
life science, banking &
payments, and retail.
• Formerly of WuxiNextCode
12. ©PistoiaAlliance
What is UX Design?
1218 September, 2019
• User Experience (UX) is about focusing on the core needs,
goals and expectations of the end users of any product
• UX Design is the process of ensuring a product is specified,
designed, built and tested with end users in mind
• UX Design is not the same as UI Design
– UX Design covers the whole product specification and creation
process, including ensuring the right problems are being solved, and
the users’ needs are being accurately researched, captured and
evaluated
– A UI design might be a potential output from a UX Design activity
• Usability is a measure of the efficiency, effectiveness and
satisfaction of any product
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What is UX Design?
1318 September, 2019
• The UX Design process covers user research, specification
and design, and testing
• It best starts early with recognising a problem to be solved
Adopted from Stanford d.school model
Empathise Ideate
Define Prototype
Test
14. ©PistoiaAlliance
What is UX Design?
1418 September, 2019
• UX Design emphasises the need for iterative usability testing
as way to verify the effectiveness of any product during
development
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What is UX Design?
1518 September, 2019
• User Research helps understand what users actually need,
not what they say they need…
“Your local
B&Q closes
at five o’clock”
3:49pm
“OK Google. What time does my local B&Q* close today?”
“Your local B&Q closes
in one hour. With current
traffic conditions you
should be able to get
there thirty minutes
before closing time.”
3:49pm
* Or a home improvement store of your choice
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What is the User Experience
for Life Science Community?
Project members include
Steering committee
A project team of UX folk in the life science R&D sector
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2218 September, 2019
Who we are? Entellect is a new cloud-based data platform designed to
help life sciences companies overcome the challenges of
modern R&D by enriching and harmonizing proprietary and
external data and delivering it in an AI-ready environment.
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How do you help IA/ML workflows as a UX person?
2418 September, 2019
Special scenarios
• Dashboard / analytics experience
• Complicated search / API capabilities
• Text editor as main user interface
• Big Data, Slow UX
• CTA: a PDF, a report or data set
• Onboarding: check your hardware requirements
25. ©PistoiaAlliance
How do you help IA/ML workflows as a UX person?
2518 September, 2019
Special scenarios
• Dashboard / analytics experience
• Complicated search / API capabilities
• Text editor as main user interface
• Big Data, Slow UX
• CTA: a PDF, a report or data set
• Onboarding: check your hardware requirements
26. ©PistoiaAlliance
Some useful UX research methods
2618 September, 2019
Qualitative research comes first
• User interviews (ethnographic studies)
• Card sorting exercises
• Building personas / identifying user roles
• User testing
• Experience maps (a-ha’s and pain points)
• Reporting
27. ©PistoiaAlliance
Some useful UX research methods
2718 September, 2019
Qualitative research comes first
• User interviews (ethnographic studies)
• Card sorting exercises
• Building personas / identifying user roles
• User testing
• Experience maps (a-ha’s and pain points)
• Reporting
28. ©PistoiaAlliance
Understanding our users
2818 September, 2019
• 1on 1 card-sorting with a
dozen people
• Every level of experience
and knowledge
• Talking through our roles
too
• Rating persona skills
• Introducing a… UX ontology
:)
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Poll Question 3
3218 September, 2019
What do you believe are the biggest factors in engendering trust
in an AI application in R&D?
1. Verified sources of data ‘feeding’ an AI process
2. Better education on how AI works overall
3. Access to the AI model build and validation processes
4. Being able to review the steps & sources the AI process has
taken to reach a result
5. How the AI application presents its results
33. Building trust and accountability: the role User
Experience design can play in Artificial Intelligence
Kirk Brote, Kirk Brote Consulting
34. ©PistoiaAlliance
The Role of Trust and Accountability
The bottom line is that user’s won’t adopt what they don’t trust.
This is the single greatest challenge designing AI in any vertical
but especially in life sciences.
Consumers have not had great experiences with commercially
available AI experiences that colors their expectations on the
maturity of the tools.
• Siri, Alexa, and Google Assistant all have major, widely known
failure points.
• No digital assistant today does a good job anticipating
concerns like a real assistant would.
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Impact of Bias on AI Experiences
As designers and developers of AI systems, we are the lens
between data and experience, addressing fairness,
accountability, and long-term effects on society when designing
with data.
• All systems made by humans are intrinsically tied to human
bias
– Google photos racially bias tagging system
– Microsoft Tay’s offensive behavior on Twitter
– Tesla autopilot fatal accidents
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Unintended Consequences in AI
• Designers need to spend an inordinate amount of time
focusing on the potential for unintended consequences
• That is needed not only to avoid another PR nightmare for
large corporations; it is about designing AI that reflects and
evolves with our vision for a more ethical society
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Training Pitfalls in AI
• AI, ML, and many other related technologies need to be
trained in order to be effective and training requires data
• Most commonly the most widely available data is used for
training because of the volume required.
• Medical Data for training is best characterized how?
– Overwhelmingly ‘WHITE’ , with other races under represented
– Still overwhelmingly ‘MALE’
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Ethics and Consequences
In genomics AI and ML is informing us about actual as well as
potential health problems. Do the same decision frameworks still
hold in this model?
So what do we show users?
• Is there anything that can be done about the result?
• What certainty is there about the potential harm being
realized?
• What benefit is there to the patient in knowing the risk or
prediction?
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Ethics and Consequences
Individual decisions can have profound impact at scale. Using AI
to power decision making opportunities impacts the decision
making process itself. As scientists become more and more
comfortable trusting AI, we have to remain vigilant in
understanding how AI based decisions are being made to ensure
their validity.