Presenter: Clare Corthell, summer.ai Artificial intelligence (AI) now predicts an incredible amount of an individual human's experience. AI software can pick your stocks, score a customer's churn risk, and find a date who's your type. These inferential technologies enable users to make better choices among a massive number of options. But as software makes more decisions on the user’s behalf, how do we, the creators of this software, expand the user’s world instead of constraining it? How do we ensure that individuals are not equated with the collective, and treated as people with individual needs, desires, and the agency to choose? Spanning design, neuroscience, artificial intelligence, and ethics, this talk illustrates how creators can elevate a user's experience while leveraging the power of AI.

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Designing Artificial
Intelligence for Humans
Clare Corthell
summer.ai

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We build algorithms that make
inferences about the real
world.

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Potential to make life better

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Potential to do harm,
in the real world

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Let’s get
really uncomfortable

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Goal
how to mitigate harm when
using data about the real world

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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

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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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Bias & prescription
(not everyone gets their
cornucopia of rainbows)
Where
examples as defined by

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Bias
holding prejudicial favor,
usually learned implicitly and
socially
Where

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Bias in Data
skews that we can’t
directly observe
Where

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Bias, scale, harm
inference 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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Prescription
when inferences are used
to decide what should
happens in the real world
Where

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Prescription is power
possible 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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• 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

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loan assessment The long history of
bias
Where

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beyond criticism to action

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the world isn’t perfect,
but you could make it better

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The future is unwritten —
yet sometimes we forget
that.

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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
Finding Bias

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working against biaS
1. Talk to users, impacted people, and
stakeholders
2. Construct Fairness
ACtion

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talk to people
Seek to understand:
who they are
what they value
what they need
what potential harm can affect them
ACtion

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Construct fairness
for example:
to mitigate gender bias, include
gender so you can actively
enforce fairness
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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you have agency
and a market price

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we’re at the forefront of a new age
governed by algorithms

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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, you’re designing
their future.
Make it a future you want to live in.

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Thank you.
@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