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Wrangle 2015
Designing Artificial Intelligence for Humans
Clare Corthell summer.ai
Wrangle 2015
Wrangle 2015
We build algorithms that make inferences about the real world.
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Potential to make life better• trade stocks • find you a date • score customer churn
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Potential to do harm, in the real world
(not the super intelligence — in the real world, to real people, now)
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Let’s get really uncomfortable
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Goal think about how to mitigate harm
when using data about the real world
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“ruthless, dictatorial, biased, & harmfully revealing”
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This isn’t hysterical - it’s Reasonable Fear
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How harm came about Where harm occurs Action we can take
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How does harm come about?
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Digital Technology and Scale
How
repeatable tasks are automated humans decide which tasks computer does and how
productivity is limited by how fast people make decisions
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Inference Technology and Scale
How
human-like decisions humans being “automated away”
decisions faster, at scale, and without humans
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computers do everything ∴
Rainbows & Cornucopias — right?
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Yes, in theory…
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Bias & prescription(not everyone gets their cornucopia of rainbows)
Where
examples as defined by
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Biasholding prejudicial favor,
usually learned implicitly and socially. every one of us is biased,
and people can’t observe their own biases
Where
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Bias in Databias in human thought leaves bias in data,
skews that we can’t directly observe
Where
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Bias, scale, harminference technology scales human decisions — any flawed decisions
or biases it is built on is scaled, too.
Where
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Descriptive Predictive
Prescriptive
What happened? What will happen? What should happen?
Where
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Prescriptionwhen inferences are used to decide what should happens
in the real world
Where
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Prescription is powerpossible and likely to
reinforce biases that existed in the real world before
Where
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Examples online Dating
future Founders loan assessment
Where
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Dating Subtle Bias and Race
Where
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Dating Subtle Bias and Race
Where
We can say that building a matching algorithm based on scores would reinforce a racial bias
Ratings men typically gave to women The effect is apparent in aggregate
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• College Education • Computer Science major • Years of experience • Last position title • Approximate age • Work experience in venture backed company
future startup Founders Institutional Bias
Where
Decision Tree with inputs:
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institutional bias comes through the data — though it seemed meritocratic at the outset The features say nothing about gender! Yet in literally pattern matching founders, we see bias.
future startup Founders Institutional Bias
Where
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The problem is not that this doesn’t reflect the real world — but rather that it doesn’t reflect
the world we want to live in.
future startup Founders Institutional Bias
Where
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loan assessment The long history of bias
Where
long history of loan officers issuing loans based on measurable values such as income, assets, education, and zip code Problem: in aggregate, loan officers are historically biased So loan algorithms perpetuate and reinforce an unfair past in the real world today
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let’s go beyond criticism to action
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the world isn’t perfect, so it’s worth exploring potential worlds
corrected for biases like racism and sexism
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You sit in the captain’s chair; you move the levers
and the knobs
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The future is unwritten — yet sometimes we forget that
we could make it better.
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Design for the world you want to live in.
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Bias is difficult to understand because it lives deep within your data
and deep within the context of the real world
Finding Bias
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1. Talk to people 2. Construct Fairness
ACtion
Two ways to Combat biaS
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Seek to understand: who they are
what they value what they need
what potential harm can affect them
ACtion
talk to people
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Construct fairness for example:
to mitigate gender bias, include gender so you can actively enforce fairness
(what doesn’t get measured can’t be managed)
ACtion
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designing algorithms is creative
(data does not speak for itself)
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We should value what we do not simply by the accuracy of our models
but the the benefit for humans and lack of negative impact
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a facial recognition thought experiment
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if you don’t build it maybe no one will
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a facial recognition thought experiment
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you have agency and a market price
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If an algorithm makes something cheaper for the majority but harmful for a minority —
are you comfortable with that impact?
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we’re at the forefront of a new age governed by algorithms
We must be deliberate in managing them ethically, strategically, and tactically
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remember the people on the other side of the algorithm
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because whether it’s their next song, their next date,
or their next loan,
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you’re designing their future. Make it a future you want to live in.
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huge thanks to
@clarecorthell clare@summer.ai
sources of note• “Fairness Through Awareness” Dwork, et al • Fortune-Tellers, Step Aside: Big Data Looks For
Future Entrepreneurs NPR • Harvard Implicit Bias Test
Manuel Ebert Cynthia Dwork Wade Vaughn Marta Hanson
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