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Work the way you liveDeloitte Shared Services Conference 2019

Human-centred AI: getting the most out of artificial intelligenceJim Guszcza, Deloitte

© 2019 Deloitte Development LLC. All rights reserved. Human-centred AI: getting the most out of artificial intelligence

“AI is the new electricity”

“Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.”

Andrew Ng

Former Baidu chief scientist, Coursera co-founder

Human-centred AI: getting the most out of artificial intelligence© 2019 Deloitte Development LLC. All rights reserved.

The AI “master narrative”: our new computer overlords

“Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it”

-- 1956 Dartmouth Conference

“About 47% of total US employment is at risk[of computerization]”

-- Oxford U study

“Google’s AlphaGo is demonstrating for the first time that machines can truly learn and think in a human way”

-- New York Times March, 2016

“Before the prospect of an intelligence explosion we humans are like small children playing with a bomb”

-- Nick Bostrom


AI as “automation”


The problem of “artificial stupidity”


Smart technologies are unlikely to engender smart outcomesunless they are designed to promote smart adoption

on the part of human end users.


Smart technologies are unlikely to engender smart outcomesunless they are designed to promote smart adoption

on the part of human end users.

Effective and Ethical AI needs human-centered design.


The problem with the designs of most engineers is that they are too logical.

We have to accept human behavior the way it is, not the way we would wish it to be.

— Don Norman, The Design of Everyday Things

The AI revolution needs a design revolution


AI and other data products will yield better outcomes if they are designed to go with the grain of human psychology.

“Human-centered AI” teams must think like designers …

… not just “engineers”.

Human-centricity: understanding the user


What is AI

From “smart systems”to “systems that make us smarter”


What is AI, really?

The development of computers that are able to do things normally done by people…

… any program can be considered AI if it does something that we would normally think of as intelligent in humans.

How the program does it is not the issue, just that is able to do it at all.

That is, it is AI if it is smart, but it doesn’t have to be smart like us. Kris Hammond

Northwestern University and Narrative Science


RPA: automating tasks involving explicit knowledge

Explicit (textbook) knowledge: can be verbalized and codified

Software “robots” can: • Open emails, attachments• Log onto websites• Move files and folders• Copy and paste data• Scrape data from the web• Reformat data into reports or dashboards• Connect to APIs• Follow “if/then” rules• Merge data from multiple sources• Perform calculations• Read and write to databases • Fill in forms


“The new AI”: automating tasks involving tacit knowledge

Tacit knowledge: “We know more than we can tell” (Polanyi)

Pattern recognition on big data enables algorithms to: • Recognize faces• Understand speech• Identify tumors in x-rays• Drive cars• Retrieve relevant information• Evaluate potential job candidates• Suggest medical diagnoses• Retrieve relevant information• Underwrite simple loans, insurance risks• Adjust simple insurance claims• Answer routine questions


Neural networks in the 1990s


Neural networks today


Deep learning – powerful …


Deep learning – powerful … but “shallow”


The AI paradox

One of the fascinating things about the search for AI is that it’s been so hard to predict which parts would be easy or hard.

At first, we thought that the quintessential preoccupations of the officially smart few, like playing chess or proving theorems—the corridas of nerd machismo—would prove to be hardest for computers.

In fact, they turn out to be easy. Things every dummy can do, like recognizing objects or picking them up, are much harder.

And it turns out to be much easier to simulate the reasoning of a highly trained adult expert than to mimic the ordinary learning of every baby.

Alison Gopnik, UC Berkeley


Human strengths:• Strategy• Causal understanding• Commonsense reasoning• Contextual awareness• Empathy• Ethical reasoning• “Imagination is more important than

knowledge”

Computer strengths:• Tactics • Pattern recognition• Consistency (avoid “noise”)• Rationality (avoid “bias”)• Brute force• Narrowly defined, repetitive

tasks• “Idiot savant”

Start with the assumption of human-machine partnerships.

(automation should not be the default mode of ideation)

“The hard problems are easy and the easy problems are hard”


Human-computer extended intelligence – a parable


Their skill at manipulating and “coaching” their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grandmaster opponents and the greater computational power of other participants.

Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.

— Garry Kasparov



Creating smart human-computer teams goes beyond statistics and computer science.

Also needed: psychology, human-centered design, behavioral economics, ethics, training/education, …


Their skill at manipulating and “coaching” their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grandmaster opponents and the greater computational power of other participants.

Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.

— Garry Kasparov


A change in perspective is worth 80 IQ points

AI = Deep Learning

AI = Machine Learning

AI = Smart Machines

AI = Automation

AI = human-computer extended intelligence

Thomas Malone (MIT): computers can serve as tools, assistants, peers, managers in human-computer teams


Case studies

From “humans in the loop”to “computers in the group”


ATMs and bank tellers

We see a whole number of occupations where

you might think that technology is going to

destroy jobs because it’s taking over tasks;

and the reverse happens.

– James Bessen

By complementing what humans can do, the introduction of AI technologies can increase the demand for human capabilities


Chatbots and call center operators

Chatbots can handle simple transactional calls

(“what is my balance”, “is my flight on time”)

This frees up humans to spend more time to answer

more important questions requiring common sense,

humor, and contextual awareness.

(“how should I invest my savings?”, “should I go

standby on the next flight?”)

AI technologies enable us to “humanize” otherwise de-humanizing jobsHappier, more engaged workers create value through more meaningful customer interactions


Automated or AI-assisted underwriting

Simple risk evaluations can be automated Underwriters have more time to manage portfolios of more complex risks


AI for next-generation child support

Predictive algorithms enable child support case workers shift from being “enforcers” to coaches and risk managers.


AI-assisted jurisprudence

The [Wisconsin Supreme] court ruled that while judges

could use these risk scores, the scores could not be a

“determinative” factor in whether a defendant was

jailed or placed on probation.

And… the court stipulated that a presentence report

submitted to the judge must include a warning about

the limits of the algorithm’s accuracy.

– Julia Angwin, NYT, 8/1/2016

The existence of human cognitive biases and “noise” often implies an ethical imperative to complement human judgment with algorithmic indications.

But the AI must be “explainable” to enable human-computer collaboration.


… Asked if artificial intelligence would put radiologists out of

business, [Eric] Topol said, “Gosh, no!”

– New York Times,

5/22/2019

… Machine learning will displace much of the work of

radiologists and anatomical pathologists.

These physicians focus largely on interpreting digitized images,

which can easily be fed directly to algorithms instead.

– Ziad Obermeyer and

Ezekiel Emanuel, NEJM

“Freestyle x” in medicine – radiology


“Freestyle x” in medicine – coaching


O-ring jobs

“The O-ring production function conceives of work as a

series of interlocking steps, links in a chain, and every

one of those links must hold for the mission to

succeed…

In much of the work that we do, we are the O-

rings…

As our tools improve, technology magnifies our

leverage and increases the importance of our

expertise, judgment and creativity.”

-- David Autor, MIT

Human {judgment, science, ethics, creativity} in the loop


“The Last Mile Problem: How data science and behavioral science can work together” Deloitte Review, January 2015http://dupress.com/articles/behavioral-economics-predictive-analytics/

“The Importance of Misbehaving: A conversation with Richard Thaler” Deloitte Review, January 2016https://dupress.deloitte.com/dup-us-en/deloitte-review/issue-18/behavioral-economics-richard-thaler-

interview.html

“Cognitive collaboration: Why humans and computers think better together” Deloitte Review, January 2017https://dupress.deloitte.com/dup-us-en/deloitte-review/issue-20/augmented-intelligence-human-computer-collaboration.html

“Smarter together: Why artificial intelligence needs human-centered design” Deloitte Review, January 2018https://www2.deloitte.com/insights/us/en/deloitte-review/issue-22/artificial-intelligence-human-centric-design.html

“Superminds: How humans and machines can work together”(Interview with Thomas Malone, MIT Sloan School of Management)Deloitte Review, January 2019https://www2.deloitte.com/insights/us/en/focus/technology-and-the-future-of-work/human-and-machine-collaboration.html

Copies available on the conference websitedeloitte.co.uk/sharedservicesconference/insight

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