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Age(years)
Explaining	Machine	Learning
Model	Predictions
Scott	Lundberg
Why	do	we	care	so	much	about	explainability
in	machine	learning?
model 55%
chance	John	will	have	
repayment	problems
John,	a	bank	customer
No	loan
Why?!
Why?! AI	magic!
Interpretable Accurate
Complex	model ✘ ✔
Simple model ✔ ✘
Interpretable	or	accurate:	choose	one.	
😀	⚖ 💰
3
Complex	models	are	
inherently	complex!
But	a	single	prediction	involves	only	a	
small	piece	of	that	complexity.
Input	value
Output	value
5
6
How	did	we	get	here?
Base	rate Prediction	for	John
20% 55%
7
Base	rate
Work	experience	=	1	yr
20% 35%
Day	trader Open	accounts	=	1
70%
Capital	gains
90%55%
8
The	order	matters!
Work	experience	=	1	yr
Day	trader
Nobel	Prize	in	2012
Lloyd	Shapley
9
SHapley Additive	exPlanation (SHAP)	values
Age	=	20
Day	trader
Shapley	values	result	from	averaging	over	all	N!	possible	orderings.
(NP-hard)
10
Mortality	risk	model
Global feature importance Local explanation summary(A)
(log relative risk of mortality)
Mortality model
(F/M)
Mortality	risk	model
Global feature importance Local explanation summary(A)
(log relative risk of mortality)
Mortality model
(F/M)
Mortality	risk	model
Global feature importance Local explanation summary(A)
(log relative risk of mortality)
Mortality model
(F/M)
Mortality	risk	model
Global feature importance Local explanation summary(A)
(log relative risk of mortality)
Mortality model
(F/M)
Mortality	risk	model
Global feature importance Local explanation summary(A)
(log relative risk of mortality)
Mortality model
(F/M)
Mortality	risk	model
Global feature importance Local explanation summary(A)
(log relative risk of mortality)
Mortality model
(F/M)
Mortality	risk	model
Reveal	rare	high-magnitude	mortality	effects
Global feature importance Local explanation summary(A)
(log relative risk of mortality)
Mortality model
(F/M)
Conflates	the
prevalence of	an	effect
with	the
magnitude of	an	effect
Mortality	risk	model
Reveal	rare	high-magnitude	mortality	effects
Global feature importance Local explanation summary(A)
(log relative risk of mortality)
Mortality model
(F/M)
Reveal	rare	high-magnitude	mortality	effects
Global feature importance Local explanation summary(A)
(log relative risk of mortality)
Mortality model
(F/M)
Rare	high	magnitude	effects
Reveal	rare	high-magnitude	mortality	effects
Global feature importance Local explanation summary(A)
(log relative risk of mortality)
Mortality model
(F/M)
Lots	of	ways	to	die	young…Not	many	ways	to	live	longer…
Dependence	plots	reveal	the	increased	
danger	of	early	onset	high	blood	pressure (C)
SHAPvalueforsystolicbloodpressure
withouttheageinteraction
(logrelativeriskofmortality)
Sys
(B)
(logrelativeriskofmortality)
Systolic blood pressure (mmHg)
Age(years)
Kidney model
=
Vertical	dispersion	is
driven	by	interaction	effects
21
The	varying	risk	of	sex	over	a	lifetime
22
23
Model	Monitoring
Model	monitoring
Time
Training	performance Test	performance
Can	you	find	where	we	introduced	the	bug?
24
Model	monitoring
Now	can	you	find	where	we	introduced	the	bug?
25
False True
Model	monitoring
Time
Transient	electronic	medical	record
Time
26
False True
Model	monitoring
Time
Gradual	change	in	atrial	fibrillation
ablation	procedure	durations
Time
27
False True
Don’t	take	my	word	for	it,	try	it	yourself	J
github.com/slundberg/shap
github.com/slundberg/shap
…
Don’t	take	my	word	for	it,	try	it	yourself	J
30
Important	questions	to	ask	when	using	SHAP:
1. If	you	are	using	a	model	agnostic	explainer,	have	you	drawn	
enough	samples?
2. What	background	population	are	you	using	to	estimate	the	
effect	of	a	feature	being	“missing”?
3. What	model	output	are	you	explaining?	(log-odds,	
probability,	rank-order,	etc.)
4. Are	you	perturbing	a	model	in	ways	that	don’t	make	sense?	
(dealing	with	tightly	correlated	features)
Thanks!

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