Despite our best intentions, UX practitioners are subject to hidden biases and barriers as any of our fellow humans. It’s more important than ever to understand our own biases to make sure we can be most effective in our communication and our design work. Increasing application of AI and machine learning as well as ever increasing amounts of data on people particularly are areas where hidden and unmitigated biases can create bad and even harmful outcomes. We explore ways to discover and discuss biases constructively before they undermine work, look at case studies of products that suffered from hidden biases, and consider pragmatic approaches to manage their influence in our projects
Presented by Karen Bachmann
Know Thyself, and to thine users be true: Understanding and Managing Biases that can influence UX work
1. @karenbachmann Know Thyself: Managing Bias #UXPA2018 1
Know thyself and to thine users be true:
Understanding and managing biases that can
influence UX work
Karen Bachmann
karen@designforcontext.com
@karenbachmann
UXPA 2018
Puerto Rico
#UXPA18
2. @karenbachmann Know Thyself: Managing Bias #UXPA2018
● How to have non-threatening and productive discussions
● Tools for identifying our biases
● Tools to manage biases
● Bringing our new awareness into our design practice
What we will discuss
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Awareness, ourselves and our colleagues
Exposure to other perspectives
Connection with our own humanity
What do we need most
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An approachable definition of bias
● How should we talk about biases to ensure a non-
threatening and productive discussion?
A LITTLE BACKGROUND
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Bias: A Mental Shortcut
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● Part of being
human
● Kahneman’s
System 1
● Not inherently
bad, may be
misapplied
● Implicit = invisible
● Not necessarily in
sync with our
conscious beliefs
● Can be managed
and changed
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Do you see what I see?
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How do you describe this
… without any presuppositions?
Objectivity is more elusive than we
want – or like – to admit to ourselves.
Image: https://www.flickr.com/photos/horiavarlan/4273846588/
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Addressing bias is not uniquely a UX challenge
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Healthcare
Business Government
Law enforcement
Safety
Data science
Technology
Journalism
Entertainment
UX Research & Design
Science
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Building awareness and knowing ourselves
● How can we best identify the biases within ourselves and our
teams?
● What tools and processes to control for bias we can
introduce into our processes?
● Where do biases emerge in our work?
KNOWING OURSELVES
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Start with understanding your own bias
Don’t fall into the illusion of complete
objectivity
Don’t be afraid to acknowledge bias
Know thyself
13. @karenbachmann Know Thyself: Managing Bias #UXPA2018
● Provide the tools for mindfulness and self-awareness as well as
building team awareness
● Acknowledge your own biases and be open to learning more
● Establish clear goals to understand biases and why it matters to the
team and to your work
● Establish communication ground rules
● Use the ability to see the biases of others with empathy and
compassion
Discussions with the team
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12 steps from #uxchat
●Encourage diversity
●Step back from your ego
●Always be talking to others
●Self-examination
●Beware of “groupthink”
●Uncover biases as early as
possible
●Bring in a moderator
●Be transparent
●Be inclusive
●Focus on the data
●Encourage safe spaces and secure
channels
●Have empathy, always
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http://whatusersdo.com/blog/how-to-fight-bias-in-your-organisation-or-team/ (now part of UserZoom)
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Detection Tools
●Changing decision making
approach
● Pre-mortems
● Worst case scenario
● Multiple estimations -> designs
● Seek outside view
●Implicit Association Test
●Flip it to test it
●Invite examination and input from
people who are not like “us”
●Appoint a Devil’s Advocate
●Slow down and reflect
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● What's your default mode for judgments and decisions? To find out,
take this (very short) cognitive-reflection test, which was created by
Shane Frederick at Yale and originally appeared in The Journal of
Economic Perspectives. At the end, you’ll receive feedback on your
answers and gain insight into how you arrived at them.
● A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball.
How much does the ball cost?
● If it takes five machines five minutes to make five widgets, how many
minutes would it take 100 machines to make 100 widgets?
● In a lake, there is a patch of lily pads. Every day, the patch doubles in size.
If it takes 48 days for the patch to cover the entire lake, how many days
would it take for the patch to cover half the lake?
Are you being tricked by intuition?
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WHICH BIASES SHOULD WE LOOK FOR FIRST?
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Confirmation bias
Anchoring
Implicit bias
Ingroup/Outgroup
Pro-Innovation
Recency effect
Overconfidence
Backfire effect
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Biases can emerge anywhere
Research
● Recruiting
● Questions we ask
● Specific data we collect
● Conclusions and priority
Design
● Target markets
● Types of interactions
● Visual design selections
● Technology selection
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Controlling our biases in our work
● How do we monitor for regressing thinking and new biases
throughout the design process?
MINDFULNESS & MANAGEMENT
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● Make the detection tools part of the design process – not once and
done
● Define goals and checks around identified biases
● Set objectives, check objectives
● Trip wires
● Watch for emergent “excuses” for regressing to System 1 thinking
Integrating the tools for awareness
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● Establish a culture of questioning
● Listen for discord and regression
● Bias buddies
● Use technological aids where possible
● Communicate shared understanding continuously
● Be kind to each other
Enabling communication
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Awareness, ourselves and our colleagues
Exposure to other perspectives
Connection with our own humanity
What do we need most
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Cited resources
These resources were directly incorporated in the presentation.
● B. Benson. “Cognitive bias cheat sheet.” (2016): https://betterhumans.coach.me/cognitive-bias-cheat-sheet-55a472476b18
● J. Beshears, S. Frederick, and F. Gino “Test Yourself: Are You Being Tricked by Intuition?” Harvard Business Review. (2015):
https://hbr.org/2015/04/test-yourself-are-you-being-tricked-by-intuition
● T. Bradberry. “13 Cognitive Biases That Really Screw Things Up For You.” (2018): https://www.linkedin.com/pulse/13-cognitive-biases-
really-screw-things-up-you-dr-travis-bradberry/
● M. Funchess. “Implicit Bias -- how it effects us and how we push through.” TEDxFlourCity. (2014):
https://www.youtube.com/watch?v=Fr8G7MtRNlk
● E. Hall. “The 9 Rules of Design Research.” (2018): https://medium.com/mule-design/the-9-rules-of-design-research-1a273fdd1d3b
● H.G. Halvorson. “How To Recognize (And Overcome) Your Unconscious Biases In Hiring.” Fast Company. (2015):
https://www.fastcompany.com/3043074/how-to-thwart-your-unconscious-biases-when-hiring-a-diverse-team
● S. Judd. “Superfan.” (2018): http://www.sachajudd.com/superfan
● D. Kahneman. Thinking, Fast and Slow. (2011)
● T. Keereepart. “3 design principles to help us overcome everyday bias.” TEDxPortland. (2016):
https://www.youtube.com/watch?v=K6dstCUWsFY
● S. Lawrence-Lightfoot and J. Hoffmann Davis. The Art and Science of Portraiture. (1997):
http://www.saralawrencelightfoot.com/portraiture1.html
● A.C. Madrigal. “Disposable America.” The Atlantic. (2018): https://www.theatlantic.com/technology/archive/2018/06/disposable-
america/563204/
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Cited resources (cont’d)
● P. McIntosh. "White Privilege: Unpacking the Invisible Knapsack" Peace and Freedom Magazine. (1989):
https://nationalseedproject.org/white-privilege-unpacking-the-invisible-knapsack
● K. Pressner. ”Are you biased? I am.” TEDxBasel. (2016): https://www.youtube.com/watch?v=Bq_xYSOZrgU
● Project Implicit. (2011): https://implicit.harvard.edu/implicit/index.jsp
● C. Ratcliff. “12 ways to fight bias in your team” Synopsys of #uxchat on Twitter, hosted by K. Bachmann. (2018):
http://whatusersdo.com/blog/how-to-fight-bias-in-your-organisation-or-team/
● S.E. Smith. “Why philosophy is so important in science education.” Quartz. (2017): https://qz.com/1132948/why-philosophy-is-so-
important-in-science-education/ (Inspiration for the “Do you see” exercise on Slide 9)
● J.B. Soll, K.L. Milkman, and J.W. Payne. “Outsmart your own biases.” Harvard Business Review. (2015):
https://hbr.org/2015/05/outsmart-your-own-biases
● S. Watcher-Boettcher. Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech. (2017):
http://www.sarawb.com/technically-wrong/
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These resources were not quoted or directly use for my presentation, but present ideas and examples that influenced my thinking and
offer valuable, interesting insights.
● American Alliance of Museums. “Unconscious Bias and Personal Work.” (2018): https://www.aam-us.org/programs/diversity-equity-
accessibility-and-inclusion/facing-change-unconscious-bias-and-personal-work/
● @BienSur_JeTaime. “One of the reasons for more ethnic diversity in tech. Devices can't be biased, but if the creators don't account for
their own biases it shows up in things like Asian women being indistinguishable to iPhones and black hands not triggering sensors in
soap machines.” (2017): https://twitter.com/BienSur_JeTaime/status/941866665746235397
● S. Bradley. “All the creepy, crazy and amazing things that happened in AI in 2017.” Wired. (2017):
http://www.wired.co.uk/article/what-happened-in-ai-in-2017
● G.L. Ciampaglia and F. Menczer. “Misinformation and biases infect social media, both intentionally and accidentally.” The
Conversation. (2018): http://theconversation.com/misinformation-and-biases-infect-social-media-both-intentionally-and-accidentally-
97148
● J. Clark. “Design in the Era of the Algorithm.” Presentation at Mind the Product, synopsis by J. Gadsby Peet. (2017):
https://www.mindtheproduct.com/2017/11/product-design-era-algorithm-josh-clark/
● D. Chopra. “Does the Human Mind Need a Fresh Start?” (2017): https://www.choprafoundation.org/science-consciousness/does-the-
human-mind-need-a-fresh-start/
● R. Courtland. “Bias detectives: the researchers striving to make algorithms fair.” Nature. (2018):
https://www.nature.com/articles/d41586-018-05469-3
● D. Gray. Liminal Thinking: Create the change you want by changing the way you think. (2016): http://liminalthinking.com/
Additional resources
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● M. Hartmann. “Unpacking the biases that shape our beliefs.” TEDxStJohns. (2015):
https://www.youtube.com/watch?v=dU7Mhne4CzU
● N.A. Heflick. “We Are Blind to Our Own Biases.” Psychology Today. (2011): https://www.psychologytoday.com/blog/the-big-
questions/201102/we-are-blind-our-own-biases
● D. Hernandez. “Unpacking and Transforming Your Biases For A Better Community.” TEDxSanAntonio. (2016):
https://www.youtube.com/watch?v=FU4CDFFy77g
● D. Hockett. “We all have implicit biases. So what can we do about it?” TEDxMidAtlanticSalon. (2017):
https://www.youtube.com/watch?v=kKHSJHkPeLY
● C. Jager. “24 Cognitive Biases You Need To Stop Making [Infographic]” Lifehacker. (2018):
https://www.lifehacker.com.au/2018/03/find-out-which-cognitive-biases-alter-your-perspective/
● T. Laurinavicius. “Cognitive Biases You Need to Master to Design Better Websites.” (2018):
https://webdesign.tutsplus.com/articles/cognitive-biases-you-need-to-master-to-design-better-websites--cms-30742
● J. Lindzon. “This Brain Hack Will Help Reframe Your Interpretation Of Reality.” Fast Company. (2017):
https://www.fastcompany.com/40500514/this-brain-hack-will-help-reframe-your-interpretation-of-reality
● E. Livni. “The philosophy that could have stopped Silicon Valley’s crisis of conscience before it started.” Quartz. (2017):
https://qz.com/1161704/silicon-valley-elite-from-companies-like-facebook-and-google-are-soothing-their-consciences-at-californias-
esalen-institute/
● Microsoft. “Keeping an Eye on AI with Dr. Kate Crawford.” Microsoft Research Podcast. (2018): https://www.microsoft.com/en-
us/research/blog/keeping-an-eye-on-ai-with-dr-kate-crawford/?OCID=msr_podcast_kcrawford_tw
Additional resources (cont’d)
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● Mind Tools. “Avoiding Psychological Bias in Decision Making: How to Make Objective Decisions.” (2018):
https://www.mindtools.com/pages/article/avoiding-psychological-bias.htm
● Partnership on AI. (2016-1018): https://www.partnershiponai.org/
● J. Powell. “It's About Time We Challenge Our Unconscious Biases.” TEDxStLouisWomen. (2016):
https://www.youtube.com/watch?v=thkmVv54e6M
● J. Powles. “New York City’s Bold, Flawed Attempt to Make Algorithms Accountable.” The New Yorker. (2017):
https://www.newyorker.com/tech/elements/new-york-citys-bold-flawed-attempt-to-make-algorithms-accountable
● M.J. Socolow. “How to Prevent Smart People From Spreading Dumb Ideas.” The New York Times. (2018):
https://www.nytimes.com/2018/03/22/opinion/facebook-spreading-ideas.html
● J. Temperton. “DeepMind's new AI ethics unit is the company's next big move.” Wired UK. (2017):
http://www.wired.co.uk/article/deepmind-ethics-and-society-artificial-intelligence
● A. Thompson. “Google’s Sentiment Analyzer Thinks Being Gay Is Bad.” Motherboard. (2017):
https://motherboard.vice.com/en_us/article/j5jmj8/google-artificial-intelligence-bias
● @sara_ann_marie. “the line from "look at our cool new facial-recognition app" to "wow this thing only works for white people" is a
fucking millimeter long and clearly marked. why can't a single tech co seem to be able to see it?” (2018):
https://twitter.com/sara_ann_marie/status/953783812315602944
● M. Simmons. “Studies Show That People Who Have High ‘Integrative Complexity’ Are More Likely To Be Successful.” (2018):
https://medium.com/the-mission/studies-show-that-people-who-have-high-integrative-complexity-are-more-likely-to-be-successful-
443480e8930c
Additional resources (cont’d)
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Know thyself and to thine users be true
Understanding and managing biases that can
influence UX work
UXPA • June 2018 slides will be posted at: www.designforcontext.com/insights
Karen Bachmann
karen@designforcontext.com
@karenbachmann
UXPA 2018
Puerto Rico
#UXPA18
Notes de l'éditeur
Listening talk: filters and barriers
NASA project – beliefs as a key element of project success, morphed into biases
“It is useless to attempt to reason a man out of a thing he was never reasoned into.”
― Jonathan Swift
“It's not at all hard to understand a person; it's only hard to listen without bias.”
― Criss Jami, Killosophy
“The eye sees only what the mind is prepared to comprehend.”
― Robertson Davies, Tempest-Tost
“I have yet to see a piece of writing, political or non-political, that does not have a slant. All writing slants the way a writer leans, and no man is born perpendicular.”
― E.B. White
How should we talk about biases to ensure a non-threatening and productive discussion?
How can we best identify the biases within our teams, including ourselves?
What tools and processes to control for bias we can introduce into our processes?
Where do biases emerge in our work?
How can we best incorporate our understanding within our design and research process?
How do we monitor for regressing thinking and new biases throughout the design process?
Humanity: “Umbutu - I am who I am because of who we all are, and we are who we are because of who I am. I see you, I see myself.” ~Melanie Funchess, TEDx on Implicit Bias
https://www.linkedin.com/pulse/13-cognitive-biases-really-screw-things-up-you-dr-travis-bradberry/
Awareness is the best way to beat these biases, so pay careful attention to how they influence you.
Technically Wrong Watcher-Boettcher [walk-ter Bet-cher]
“…exposure to difference changes perspective, and increases tolerance.” ~Sara Watcher-Boettcher
Implicit Bias -- how it effects us and how we push through | Melanie Funchess | TEDxFlourCity
Implicit biases can make us incapable of seeing the truth before our eyes
"We all have them. Even people who have avowed commitments to impartiality. Like judges."
"What has been done can be undone…. Our brains have tremendous capacity for growth and change."
Call yourself on your actions. Takes being extremely self-aware.
"Transformational activism" - do your work
Connect with people who don't look like you
When you have privilege, use it to create equity
Intentionally and deliberately engage in non-biasing activities - join groups not like you and learn and then teach others
Former diversity trainer at DOJ
Tact is the art of making a point without making an enemy -- Isaac Newton
Algorithms and training data
Data collection
Data use
Limited perspectives informing the choices we make
Echo chambers and social bubbles
https://www.theatlantic.com/technology/archive/2017/07/the-internet-is-one-big-personality-test/531861/
When algorithms can be trained to accurately infer your personality based on anything you do, the internet is a personality quiz—or, at least, it can be, so long as each page visit, web search, and “like” can be gathered and correlated. Online, before you even click on a quiz, you’re already filling something out.
“We’re being trained by algorithms that they’re always right,” Kosinski says. … If people’s faith in algorithms continues to grow, it might not be long before I trust a computer to tell me about my personality more than I trust friends or family—or more than I trust myself.
http://www.wired.co.uk/article/mustafa-suleyman-deepmind-ai-morals-ethics
[AI technologies] could shine a light on damaging human biases and help society address them, or entrench patterns of discrimination and perpetuate them.
We need to do the hard, practical and messy work of finding out what ethical AI really means.
https://www.buzzfeed.com/tedchiang/the-real-danger-to-civilization-isnt-ai-its-runaway
Silicon Valley has unconsciously created a devil in their own image, a boogeyman whose excesses are precisely their own. [AI isn’t the problem. It’s exposing the underlying problem.]
https://www.mindtheproduct.com/2017/11/product-design-era-algorithm-josh-clark/
We must be cognisant of the fact that we could easily code our historical biases into the machines of the future. People who have been persecuted in the past are not outliers, they must be integrated into the fabric of our societies and we can help make that happen with technology.
https://meanjin.com.au/essays/the-last-days-of-reality/
Over the last two years, that capacity to manage mood has been monetised through the sharing of fake news and political feeds atuned to reader preference: you can also make people happy by confirming their biases.We all like to believe we’re in the right, and when we get some sign from the universe at large that we are correct, we feel better about ourselves. That’s how the curated newsfeed became wedded to the world of profitable propaganda.
http://www.sachajudd.com/superfan
Biases are not inherently good or bad, but “actually a cognitive tool. It’s all the tiny assumptions that we make as we navigate the world because we’re bombarded with too much information and our brains sort it quicker than even we can recognise.”
https://www.fastcompany.com/3043074/how-to-thwart-your-unconscious-biases-when-hiring-a-diverse-team
If you have a brain, you’re automatically biased. End of story.
https://medium.com/the-mission/studies-show-that-people-who-have-high-integrative-complexity-are-more-likely-to-be-successful-443480e8930c
Many were instrumental to our survival in an ancient world, but can lead to irrational decisions in the modern world.
https://medium.com/mule-design/the-9-rules-of-design-research-1a273fdd1d3b
This has nothing to do with how smart or how well-informed you are. Once you accept this, and as long as you work in a team that evinces psychological safety and mutual respect, it can be a fun game to identify biases and call them out.
Melanie Funchess
"What has been done can be undone…. Our brains have tremendous capacity for growth and change."
Call yourself on your actions. Takes being extremely self-aware.
3 design principles to help us overcome everyday bias | Thaniya Keereepart | TEDxPortland
"The biases baked into us aren't going away overnight."
Already changing behavior, but we need to keep at it and do things a tiny step at a time
“It is useless to attempt to reason a man out of a thing he was never reasoned into.”
― Jonathan Swift
“It's not at all hard to understand a person; it's only hard to listen without bias.”
― Criss Jami, Killosophy
“The eye sees only what the mind is prepared to comprehend.”
― Robertson Davies, Tempest-Tost
“I have yet to see a piece of writing, political or non-political, that does not have a slant. All writing slants the way a writer leans, and no man is born perpendicular.”
― E.B. White
Laura: It’s a wonder we get anything done. KB addendum: It’s a wonder we get any of the right things done.
What should we remember?
Unreliability of memory
Generalizing instinctively or selective facts
Context of memory
Need to act fast
Overconfidence
Immediacy > measured, long-term
Inertia
Risk aversion
Oversimplification
Too much information
Reliance on familiar
Incorrectly boosting significance
Focus on change (anchoring!)
Confirmation based on existing mindset (confirmation bias)
Blind spots to our own biases
Not enough meaning
Invent meaning
Fill gaps from past patterns
Oversimplification
Overconfidence
Projecting our thoughts and assumptions
https://betterhumans.coach.me/cognitive-bias-cheat-sheet-55a472476b18
Buster Benson is writing a book on cognitive biases and arguments (Patreon). Keeps a book of his beliefs that he updates regularly.
https://aeon.co/videos/models-are-always-imperfect-and-the-ones-we-choose-greatly-shape-our-experience
https://qz.com/1132948/why-philosophy-is-so-important-in-science-education/
To approach it, I invite students to look at something nearby without any presuppositions. I then ask them to tell me what they see. They pause… and then recognize that they can’t interpret their experiences without drawing on prior ideas. Once they notice this, the idea that it can be appropriate to ask questions about objectivity in science ceases to be so strange.
Amusement for children
Useful tool for anyone
Scourge on the environment
Metaphor for disposable culture
Unreliable and faulty
Essential tool for people with motor skill challenges and other disabilities
https://www.theatlantic.com/technology/archive/2018/06/disposable-america/563204/
Liminal Thinking Principle: “Assume [Know] you are not objective. If you’re part of the system, you’re part of the problem.”
Horia Varlan. https://www.flickr.com/photos/horiavarlan/4273846588/
https://www.aam-us.org/programs/diversity-equity-accessibility-and-inclusion/facing-change-unconscious-bias-and-personal-work/
Many industries and professions are having a similar discussion and trying to solve the problem
https://qz.com/1161704/silicon-valley-elite-from-companies-like-facebook-and-google-are-soothing-their-consciences-at-californias-esalen-institute/
“Better late than never, at Esalen, techies are now studying compassion and connection with the help of specialists.”
https://motherboard.vice.com/en_us/article/j5jmj8/google-artificial-intelligence-bias
"It's easy to get around [the bias] for each individual problem," Ernest Davis, a professor of computer science at New York University told me over the phone, "But getting around it systematically is very difficult."
https://qz.com/1132948/why-philosophy-is-so-important-in-science-education/
I propose an explicit division of labour. Our scientist colleagues should continue to teach the fundamentals of science, but they can help by making clear to their students that science brims with important conceptual, interpretative, methodological, and ethical issues that philosophers are uniquely situated to address, and that far from being irrelevant to science, philosophical matters lie at its heart.
http://www.wired.co.uk/article/deepmind-ethics-and-society-artificial-intelligence
Suleyman is bullish about his company’s efforts to not just break new frontiers in artificial intelligence technology, but also keep a grip on the ethical implications. … "putting sensitive issues, proactively, up-front, on the table, for public discussion."
https://www.nytimes.com/2018/01/17/technology/apple-addiction-iphone.html
“I do think this is their time to step up,” said Tristan Harris, a former design ethicist at Google who now runs Time Well Spent, an organization working to improve technology’s impact on society.
https://finance.yahoo.com/news/mec-challenges-industry-brave-bias-130000133.html: Brave your bias challenge
The Art and Science of Portraiture lessons
Sara Lawrence-Lightfoot and Jessica Hoffmann Davis
Dan Klyn at WIAD Tampa 2018
Dan Klyn advocates "Please Yourself" in design rather than trying to neutralize ourselves as designer, despite the idea that this would be a hard sell
Embracing, not suppressing, our humanity in the design process increasingly seems like a good idea.
https://qz.com/1132948/why-philosophy-is-so-important-in-science-education/
“But all of us are ‘biased’ and our biases fuel the creative work of science. This issue can be difficult to address, because a naive conception of objectivity is so ingrained in the popular image of what science is. “
https://associationsnow.com/2017/12/tech-conference-2017-curiosity-based-decision-making/
But there’s a role for introspection as well. Leaders need to get to know themselves better and learn to love their vulnerabilities, Steltzer said. Often, the biggest inhibitor to curiosity is not someone else, but your own fears.
https://www.microsoft.com/en-us/research/blog/keeping-an-eye-on-ai-with-dr-kate-crawford/?OCID=msr_podcast_kcrawford_tw
There is no quick technical fix to bias. It’s really tempting to want to think that there’s going to be some type of silver-bullet solution that we can just tweak our algorithms or, you know, use different sorts of training data sets, or try to boost signal in particular ways. The problem of this is that it really doesn’t look to the deep social and historical issues that human data is made from.
Well let’s go back to FATE, because that’s kind of a big interest of yours right now. Fairness, accountability, transparency and ethics.
Also WEIRD
https://complexityandmanagement.wordpress.com/2012/11/23/trust-in-organisations/
http://whatusersdo.com/blog/how-to-fight-bias-in-your-organisation-or-team/
Talking: If we strive for a culture where we’re having open and honest conversations, then we’ll always be mindful of everyone’s point-of-view and recognise when our own biases are surfacing.
Self-exam: Do we need to add a specific time in the diary for regular self-reflection? If it doesn’t come naturally, then maybe we do.
Groupthink: As Psychologists for Social Responsibility states, “A group is especially vulnerable to groupthink when its members are similar in background.”
Transparency: Portraiture lessons
Inclusive: Make sure a diverse team is effective by making sure all voices have a chance to speak and be heard
https://www.fastcompany.com/3043074/how-to-thwart-your-unconscious-biases-when-hiring-a-diverse-team
Taken together with results from hundreds of other studies, it’s clear that where raising awareness alone can fail, simple strategies–like taking a moment to focus on similarities and common identities, or slowing down to weigh all the evidence–can go a long way to increasing not only the diversity of hires in organizations, but also to creating the kind of inclusive environment that will make those hires feel like it’s worth sticking around.
Data: Data doesn’t have bias. Researchers have bias. Challenge the questions, provide the context and background, provide raw data even
https://hbr.org/2015/05/outsmart-your-own-biases
Are you biased? I am | Kristen Pressner | TEDxBasel Aug 30, 2016
Two conversations and her reactions juxtaposed to expose a bias
Mentally, "flip it to test it." Reverse a description or perception to the opposite (man/woman, Caucasian/Indian - If it is weird, you might have a problem
Otherwise, You may be missing an opportunity to see the world differently.
https://webdesign.tutsplus.com/articles/cognitive-biases-you-need-to-master-to-design-better-websites--cms-30742
Confirmation bias: tendency to search for, interpret, favor, and recall information in a way that confirms one's preexisting beliefs or hypotheses
Anchoring: the tendency for an individual to rely too heavily on an initial piece of information offered (known as the "anchor") when making decisions.
Pro-Innovation: Belief that an innovation should be adopted by whole society without the need of its alteration
Recency effect: Our tendency to generalize memory means we give priority to things we most recently learned
Overconfidence: a person's subjective confidence in his or her judgements is reliably greater than the objective accuracy of those judgements,
Backfire: given evidence against their beliefs, people can reject the evidence and believe even more strongly
Implicit bias: the unconscious attribution of particular qualities to a member of a certain social group
Ingroup/Outgroup: a pattern of favoring members of one's in-group over out-group members.
http://behavioralscientist.org/behavioral-immune-system-influences-attitudes-toward-immigration/: behavioral immune system
https://www.mindtools.com/pages/article/avoiding-psychological-bias.htm
Psychological bias is the opposite of common sense and clear, measured judgment. It can lead to missed opportunities and poor decision making.
Confirmation bias
Look for ways to challenge what you think you see. Seek out information from a range of sources, and use an approach such as the Six Thinking Hats technique to consider situations from multiple perspectives.
Alternatively, discuss your thoughts with others. Surround yourself with a diverse group of people, and don't be afraid to listen to dissenting views.
Anchoring - "first impression" bias
reflect on your decision-making history, and think about whether you've rushed to judgment in the past.
make time to make decisions slowly
Overconfidence
If you suspect that you might be depending on potentially unreliable information, think about what you can do to gather comprehensive, objective data.
Gambler's Fallacy
make sure that you look at trends from a number of angles
look for trends in your environment
Fundamental Attribution Error
It's essential to look at situations, and the people involved in them, non-judgmentally. Use empathy and (if appropriate) cultural intelligence, to understand why people behave in the ways that they do.
Also, build emotional intelligence, so that you can reflect accurately on your own behavior.
For too long we’ve focused on managing user bias. Can we manage our own?
https://www.theatlantic.com/technology/archive/2017/07/the-internet-is-one-big-personality-test/531861/
People tend to have a sense of their own character, but this sense is never complete. We know ourselves, but we don’t.
Emergent excuses
Time
Budget
Overconfidence
Ambient understanding from AEL collaboration approach
https://medium.com/mule-design/the-9-rules-of-design-research-1a273fdd1d3b Erika Hall
9. Find your bias buddies: …When it comes to interpreting the results of research, collaboration becomes particularly critical. Everyone with a human brain is burdened by human biases. And there is no way to sense one’s own. We all see what best fits our existing beliefs. So, we have to refer to an external standard (including the pre-established goals and questions) and work together to check each other
Tech tools: sentiment analysis on team communication, present consolidated findings
Ambient understanding from AEL collaboration approach
How to have non-threatening and productive discussions
Tools for identifying our biases
Tools to manage biases
Bringing our new awareness into our design practice
Are you biased? I am | Kristen Pressner | TEDxBasel Aug 30, 2016
Worth the journey: “Otherwise, You may be missing an opportunity to see the world differently.”