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Maarten van Smeden, PhD
Explainable AI workshop
12 April 2021
Five questions about AI in medicine
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Conflicts of interest
Financially
• I do not own (any) patents or stocks, and am I not involved in the
development of any Artificial Intelligence (AI) related products
• I am not paid for this talk
• I am involved in the development of a field standard for medical
AI, commissioned by the Dutch government, for which a financial
compensation was granted
Intellectually
• I am a statistician
• In interviews and on social media I have been quite sceptical
about AI (hype) in medicine
• Overall, I believe the interest in AI in medicine is net-beneficial
for someone in my position, although…
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
https://bit.ly/2CwW43A
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
https://bit.ly/2TOdd0F
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
https://bit.ly/2v2aokk
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Some general observations about AI in medicine
• Incredibly hot
• Incredibly heterogeneous
• Robots, data analyses, self-learning systems,…
• Types of data
• “Traditional” structured data
• Medical imaging
• Gene expression data
• Text mining electronic health records
• Analyzing social media posts (e.g. pharmacovigilance)
• Speech signal processing (e.g. )
• Incredibly opaque
• Limited information about actual use of AI in healthcare
• Almost no regulations (yet)
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Tech company business model
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Tech company business model
https://bit.ly/2HSp8X5; https://bit.ly/2Z0Pfop; https://bit.ly/2KIcpHG; https://bit.ly/33IJhr9
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Other success stories
https://go.nature.com/2VG2hS7; https://bbc.in/2Z1drXQ; https://bit.ly/2TAfRIP
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
IBM Watson winning Jeopardy! (2011)
https://bbc.in/2TMvV8I
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
IBM Watson for oncology
https://bit.ly/2LxiWGj
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Example: retinal disease
Gulshan et al, JAMA, 2016, 10.1001/jama.2016.17216; Picture retinopathy: https://bit.ly/2kB3X2w
Diabetic retinopathy
Deep learning (= Neural network)
• 128,000 images
• Transfer learning (preinitialization)
• Sensitivity and specificity > .90
• Estimated from training data
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Example: lymph node metastases
Bejnordi et al, JAMA, 2018, doi: 10.1001/jama.2017.14585. See our letter to the editor for a critical discussion: https://bit.ly/2kcYS0e
Deep learning competition
But:
• 390 teams signed up, 23 submitted
• “Only” 270 images for training
• Test AUC range: 0.56 to 0.99
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
AI is everywhere
https://bit.ly/2ka0HLq; https://go.nature.com/33TQgO6; https://bit.ly/2kp6X23; https://bit.ly/2lZuKWt; https://bit.ly/2lI298g
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
“As of today, we have deployed the system in 16 hospitals, and
it is performing over 1,300 screenings per day”
MedRxiv pre-print only, 23 March 2020,
doi.org/10.1101/2020.03.19.20039354
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Living review (update 3)
doi: 10.1136/bmj.m1328
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Living review (update 3)
doi: 10.1136/bmj.m1328
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Living review (update 3)
Risk of bias assessment ursing PROBAST tool: https://www.probast.org/
doi: 10.1136/bmj.m1328
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
5 questions about AI in medicine
1. Is AI truly intelligent?
2. Is AI old statistics wine in new machine learning bottles?
3. Is AI able to explain?
4. Surely, AI is better at making predictions?
5. Will AI make healthcare better, faster and cheaper?
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Q1: Is AI truly intelligent?
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Turing, Mind, 1950, doi: 10.1093/mind/LIX.236.433
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Turing, Mind, 1950, doi: 10.1093/mind/LIX.236.433
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Source: https://openai.com/blog/multimodal-neurons/
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
claiming that a classifier trained on
zillions of human-labelled images
containing cats and no cats, is
recognizing cats is just stupid – a
human can see a handful of cats,
including cartoons of pink panthers,
and lions and tigers and panthers, and
then can not only recognize many
other types of cats, but even if they
lose their sight, might have a pretty
good go at telling whether they are
holding their moggy or their doggy
https://bit.ly/326ghK8
Jon Crowcroft
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Adversarial example
https://bit.ly/2N4mQFo; https://bit.ly/2W7X9rF
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Skin cancer and rulers
Esteva et al., Nature, 2016, DOI: 10.1038/nature21056; https://bit.ly/2lE0vV0
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
https://arxiv.org/abs/2008.07371
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
All the impressive achievements of
deep learning amount to just curve
fitting
https://bit.ly/3t8kLfl
Judea Pearl
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Q2: is AI old statistics wine in
new machine learning bottles?
AI
100%
linear
models
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Terminology
In medical research, “artificial intelligence” usually
just means “machine learning” or “algorithm”
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
https://bit.ly/38A1ng0
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
“Everything is an ML method”
https://bit.ly/2lEVn33
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
“ML methods come from computer science”
https://bit.ly/2zhbwPv; https://stanford.io/2TVp1xK; https://stanford.io/2ZfED0k
Leo Breiman Jerome H Friedman Trevor Hastie
CART, random forest Gradient boosting Elements of statistical learning
Education Physics/Math Physics Statistics
Job title Professor of Statistics Professor of Statistics Professor of Statistics
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Two cultures
Breiman, Stat Sci, 2001, DOI: 10.1214/ss/1009213726
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Statistics Machine learning
Covariates Features
Outcome variable Target
Model Network, graphs
Parameters Weights
Model for discrete var. Classifier
Model for continuous var. Regression
Log-likelihood Cross-entropy loss
Multinomial regression Softmax
Measurement error Noise
Subject/observation Sample/instance
Dummy coding One-hot encoding
Measurement invariance Concept drift
Statistics Machine learning
Prediction Supervised learning
Latent variable modeling Unsupervised learning
Fitting Learning
Prediction error Error
Sensitivity Recall
Positive predictive value Precision
Contingency table Confusion matrix
Measurement error model Noise-aware ML
Structural equation model Gaussian Bayesian network
Gold standard Ground truth
Derivation–validation Training–test
Experiment A/B test
Adapted from Daniel Obserski: https://bit.ly/2YN12Xf and Robert Tibshirani: https://stanford.io/2zqEGfr
Language
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Robert Tibshirani: https://stanford.io/2zqEGfr
Machine learning: large grant = $1,000,000
Statistics: large grant = $50,000
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
ML/AI refers to a culture, not to methods
Distinguishing between statistics and ML/AI
• Substantial overlap methods
• Substantial overlap analysis goals
• Attempts to distinguish frequently results in disagreement
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Beam & Kohane, JAMA, 2018, doi : 10.1001/jama.2017.18391
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Q3: Is AI able to explain?
BLACK BOX
INPUT EXPLANATION
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Explanatory models
• Theory, cause and effect
• aetiology of illness
• effect of treatment
Prediction models
• Interest in (risk) predictions of future observations
• Cause and effect not a direct concern
• prognosis and diagnosis
Descriptive models
• Capture the data structure
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
The Basketball thought experiment
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
The Basketball thought experiment
Relation of interest:
player height -> player talent (“got game”)
Third variable: professional basketball player
CONFOUNDER?
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Red = professional, black = amateur basketballer
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Red = professional, black = amateur basketballer
• The third variable professional basketball player is a collider
• An algorithm should not control for this collider (as one should
do for a confounder)
• How should an algorithm know it should ignore “professional
basketball player”?
It cannot know based on the data alone!
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
AI and causal inference
1See further: Kreiff and Diaz Ordaz; https://bit.ly/2m1eYdK
Small selection1
• Superlearner (e.g. van der Laan)
• High dimensional propensity scores (e.g. Schneeweiss)
• The book of why (Pearl)
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
• Understanding cause and effect crucial in understanding
aetiology, effect of interventions -> explanatory modelling
• There is a large difference between explaining why the AI is
predicting what it is predicting (e.g. feature importance) and the
ability of AI to “truly explain” -> separate causes from effects
• Explanatory modelling is already challenging in structured data
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Q4: surely, AI is better at making predictions?
Img: https://bit.ly/3saKFO7
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Reviewer #2
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Systematic review clinical prediction models
Christodoulou et al. Journal of Clinical Epidemiology, 2019, doi: 10.1016/j.jclinepi.2019.02.004
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Sources of prediction error
Y = 𝑓 𝑥 + 𝜀
For a model 𝑘 the expected test prediction error is:
σ!
+ bias! -
𝑓" 𝑥 + var -
𝑓" 𝑥
See equation 2.46 in Hastie et al., the elements of statistical learning, https://stanford.io/2voWjra
Irreducible error Mean squared prediction error
(with E 𝜀 = 0, var 𝜀 = 𝜎!
, values in 𝑥 are not random)
What we don’t model How we model
≈
≈
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Irreducible error in medicine is often large
• Health and lack thereof complex to measure (‘no gold standard’)
• Predictors of diseases are often imperfectly and partly
measured
• We often don’t know all the causal mechanisms at play
• much easier to predict if you know the causal mechanisms!
• Predicting the future even more difficult
Understanding prediction uncertainty is key
Courtesy Cecile Janssens: https://bit.ly/2Jf5ft6
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Classifier Technology and the Illusion of Progress
Hand, Stat Sci, 2006, doi: 10.1214/088342306000000060
David Hand
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Predicting mortality – the conclusion
PlosOne, 2018, DOI: 10.1371/journal.pone.0202344
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Predicting mortality – the results
PlosOne, 2018, DOI: 10.1371/journal.pone.0202344
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Predicting mortality – the media
PlosOne, 2018, DOI: 10.1371/journal.pone.0202344; https://bit.ly/2Q6H41R; https://bit.ly/2m3RLrn
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Q5: will AI make healthcare faster, better, cheaper?
Img: https://bit.ly/3wOv0aH
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Better?
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Faster?
https://dl.acm.org/doi/abs/10.1145/3313831.3376718
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Faster?
https://dl.acm.org/doi/abs/10.1145/3313831.3376718
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Cheaper?
The costs of running (cloud computing) the Transformer
algorithm are estimated at 1 to 3 million Dollars
https://bit.ly/33Dj38X
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Flexible algorithms are data hungry
From slide deck Ben van Calster: https://bit.ly/38Aqmjs
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
https://twitter.com/DrHughHarvey/status/1230218991026819077
SOME CONCLUDING REMARKS
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
When used right, AI will able to do amazing things
… while being subject to many of the same issues of traditional
prediction modelling, including the leaky implementation pipeline
Img: https://bit.ly/3
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Recidivism Algorithm
Pro-publica (2016) https://bit.ly/1XMKh5R
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
• Algorithms are not designed to automatically encourage equitable
healthcare and/or fair medical decision making
• Often we seem unaware of selection mechanisms in our data,
poorly reflecting society, enlarging existing inequalities or both
All photos of scientists I used in this presentation were white men
Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
Email: M.vanSmeden@umcutrecht.nl
Twitter: @MaartenvSmeden

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Five questions about artificial intelligence

  • 1. Maarten van Smeden, PhD Explainable AI workshop 12 April 2021 Five questions about AI in medicine
  • 2. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Conflicts of interest Financially • I do not own (any) patents or stocks, and am I not involved in the development of any Artificial Intelligence (AI) related products • I am not paid for this talk • I am involved in the development of a field standard for medical AI, commissioned by the Dutch government, for which a financial compensation was granted Intellectually • I am a statistician • In interviews and on social media I have been quite sceptical about AI (hype) in medicine • Overall, I believe the interest in AI in medicine is net-beneficial for someone in my position, although…
  • 3. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden https://bit.ly/2CwW43A
  • 4. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden https://bit.ly/2TOdd0F
  • 5. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden https://bit.ly/2v2aokk
  • 6. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Some general observations about AI in medicine • Incredibly hot • Incredibly heterogeneous • Robots, data analyses, self-learning systems,… • Types of data • “Traditional” structured data • Medical imaging • Gene expression data • Text mining electronic health records • Analyzing social media posts (e.g. pharmacovigilance) • Speech signal processing (e.g. ) • Incredibly opaque • Limited information about actual use of AI in healthcare • Almost no regulations (yet)
  • 7. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Tech company business model
  • 8. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Tech company business model https://bit.ly/2HSp8X5; https://bit.ly/2Z0Pfop; https://bit.ly/2KIcpHG; https://bit.ly/33IJhr9
  • 9. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Other success stories https://go.nature.com/2VG2hS7; https://bbc.in/2Z1drXQ; https://bit.ly/2TAfRIP
  • 10. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden IBM Watson winning Jeopardy! (2011) https://bbc.in/2TMvV8I
  • 11. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden IBM Watson for oncology https://bit.ly/2LxiWGj
  • 12. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Example: retinal disease Gulshan et al, JAMA, 2016, 10.1001/jama.2016.17216; Picture retinopathy: https://bit.ly/2kB3X2w Diabetic retinopathy Deep learning (= Neural network) • 128,000 images • Transfer learning (preinitialization) • Sensitivity and specificity > .90 • Estimated from training data
  • 13. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Example: lymph node metastases Bejnordi et al, JAMA, 2018, doi: 10.1001/jama.2017.14585. See our letter to the editor for a critical discussion: https://bit.ly/2kcYS0e Deep learning competition But: • 390 teams signed up, 23 submitted • “Only” 270 images for training • Test AUC range: 0.56 to 0.99
  • 14. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden AI is everywhere https://bit.ly/2ka0HLq; https://go.nature.com/33TQgO6; https://bit.ly/2kp6X23; https://bit.ly/2lZuKWt; https://bit.ly/2lI298g
  • 15. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
  • 16. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden “As of today, we have deployed the system in 16 hospitals, and it is performing over 1,300 screenings per day” MedRxiv pre-print only, 23 March 2020, doi.org/10.1101/2020.03.19.20039354
  • 17. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Living review (update 3) doi: 10.1136/bmj.m1328
  • 18. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Living review (update 3) doi: 10.1136/bmj.m1328
  • 19. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
  • 20. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Living review (update 3) Risk of bias assessment ursing PROBAST tool: https://www.probast.org/ doi: 10.1136/bmj.m1328
  • 21. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
  • 22. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden 5 questions about AI in medicine 1. Is AI truly intelligent? 2. Is AI old statistics wine in new machine learning bottles? 3. Is AI able to explain? 4. Surely, AI is better at making predictions? 5. Will AI make healthcare better, faster and cheaper?
  • 23. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Q1: Is AI truly intelligent?
  • 24. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Turing, Mind, 1950, doi: 10.1093/mind/LIX.236.433
  • 25. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Turing, Mind, 1950, doi: 10.1093/mind/LIX.236.433
  • 26. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
  • 27. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Source: https://openai.com/blog/multimodal-neurons/
  • 28. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden claiming that a classifier trained on zillions of human-labelled images containing cats and no cats, is recognizing cats is just stupid – a human can see a handful of cats, including cartoons of pink panthers, and lions and tigers and panthers, and then can not only recognize many other types of cats, but even if they lose their sight, might have a pretty good go at telling whether they are holding their moggy or their doggy https://bit.ly/326ghK8 Jon Crowcroft
  • 29. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Adversarial example https://bit.ly/2N4mQFo; https://bit.ly/2W7X9rF
  • 30. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Skin cancer and rulers Esteva et al., Nature, 2016, DOI: 10.1038/nature21056; https://bit.ly/2lE0vV0
  • 31. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden https://arxiv.org/abs/2008.07371
  • 32. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden All the impressive achievements of deep learning amount to just curve fitting https://bit.ly/3t8kLfl Judea Pearl
  • 33. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Q2: is AI old statistics wine in new machine learning bottles? AI 100% linear models
  • 34. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Terminology In medical research, “artificial intelligence” usually just means “machine learning” or “algorithm”
  • 35. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden https://bit.ly/38A1ng0
  • 36. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden “Everything is an ML method” https://bit.ly/2lEVn33
  • 37. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden “ML methods come from computer science” https://bit.ly/2zhbwPv; https://stanford.io/2TVp1xK; https://stanford.io/2ZfED0k Leo Breiman Jerome H Friedman Trevor Hastie CART, random forest Gradient boosting Elements of statistical learning Education Physics/Math Physics Statistics Job title Professor of Statistics Professor of Statistics Professor of Statistics
  • 38. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Two cultures Breiman, Stat Sci, 2001, DOI: 10.1214/ss/1009213726
  • 39. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Statistics Machine learning Covariates Features Outcome variable Target Model Network, graphs Parameters Weights Model for discrete var. Classifier Model for continuous var. Regression Log-likelihood Cross-entropy loss Multinomial regression Softmax Measurement error Noise Subject/observation Sample/instance Dummy coding One-hot encoding Measurement invariance Concept drift Statistics Machine learning Prediction Supervised learning Latent variable modeling Unsupervised learning Fitting Learning Prediction error Error Sensitivity Recall Positive predictive value Precision Contingency table Confusion matrix Measurement error model Noise-aware ML Structural equation model Gaussian Bayesian network Gold standard Ground truth Derivation–validation Training–test Experiment A/B test Adapted from Daniel Obserski: https://bit.ly/2YN12Xf and Robert Tibshirani: https://stanford.io/2zqEGfr Language
  • 40. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Robert Tibshirani: https://stanford.io/2zqEGfr Machine learning: large grant = $1,000,000 Statistics: large grant = $50,000
  • 41. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden ML/AI refers to a culture, not to methods Distinguishing between statistics and ML/AI • Substantial overlap methods • Substantial overlap analysis goals • Attempts to distinguish frequently results in disagreement
  • 42. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Beam & Kohane, JAMA, 2018, doi : 10.1001/jama.2017.18391
  • 43. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Q3: Is AI able to explain? BLACK BOX INPUT EXPLANATION
  • 44. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden
  • 45. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Explanatory models • Theory, cause and effect • aetiology of illness • effect of treatment Prediction models • Interest in (risk) predictions of future observations • Cause and effect not a direct concern • prognosis and diagnosis Descriptive models • Capture the data structure
  • 46. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden The Basketball thought experiment
  • 47. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden The Basketball thought experiment Relation of interest: player height -> player talent (“got game”) Third variable: professional basketball player CONFOUNDER?
  • 48. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Red = professional, black = amateur basketballer
  • 49. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Red = professional, black = amateur basketballer • The third variable professional basketball player is a collider • An algorithm should not control for this collider (as one should do for a confounder) • How should an algorithm know it should ignore “professional basketball player”? It cannot know based on the data alone!
  • 50. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden AI and causal inference 1See further: Kreiff and Diaz Ordaz; https://bit.ly/2m1eYdK Small selection1 • Superlearner (e.g. van der Laan) • High dimensional propensity scores (e.g. Schneeweiss) • The book of why (Pearl)
  • 51. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden • Understanding cause and effect crucial in understanding aetiology, effect of interventions -> explanatory modelling • There is a large difference between explaining why the AI is predicting what it is predicting (e.g. feature importance) and the ability of AI to “truly explain” -> separate causes from effects • Explanatory modelling is already challenging in structured data
  • 52. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Q4: surely, AI is better at making predictions? Img: https://bit.ly/3saKFO7
  • 53. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Reviewer #2
  • 54. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Systematic review clinical prediction models Christodoulou et al. Journal of Clinical Epidemiology, 2019, doi: 10.1016/j.jclinepi.2019.02.004
  • 55. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Sources of prediction error Y = 𝑓 𝑥 + 𝜀 For a model 𝑘 the expected test prediction error is: σ! + bias! - 𝑓" 𝑥 + var - 𝑓" 𝑥 See equation 2.46 in Hastie et al., the elements of statistical learning, https://stanford.io/2voWjra Irreducible error Mean squared prediction error (with E 𝜀 = 0, var 𝜀 = 𝜎! , values in 𝑥 are not random) What we don’t model How we model ≈ ≈
  • 56. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Irreducible error in medicine is often large • Health and lack thereof complex to measure (‘no gold standard’) • Predictors of diseases are often imperfectly and partly measured • We often don’t know all the causal mechanisms at play • much easier to predict if you know the causal mechanisms! • Predicting the future even more difficult Understanding prediction uncertainty is key Courtesy Cecile Janssens: https://bit.ly/2Jf5ft6
  • 57. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Classifier Technology and the Illusion of Progress Hand, Stat Sci, 2006, doi: 10.1214/088342306000000060 David Hand
  • 58. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Predicting mortality – the conclusion PlosOne, 2018, DOI: 10.1371/journal.pone.0202344
  • 59. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Predicting mortality – the results PlosOne, 2018, DOI: 10.1371/journal.pone.0202344
  • 60. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Predicting mortality – the media PlosOne, 2018, DOI: 10.1371/journal.pone.0202344; https://bit.ly/2Q6H41R; https://bit.ly/2m3RLrn
  • 61. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Q5: will AI make healthcare faster, better, cheaper? Img: https://bit.ly/3wOv0aH
  • 62. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Better?
  • 63. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Faster? https://dl.acm.org/doi/abs/10.1145/3313831.3376718
  • 64. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Faster? https://dl.acm.org/doi/abs/10.1145/3313831.3376718
  • 65. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Cheaper? The costs of running (cloud computing) the Transformer algorithm are estimated at 1 to 3 million Dollars https://bit.ly/33Dj38X
  • 66. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Flexible algorithms are data hungry From slide deck Ben van Calster: https://bit.ly/38Aqmjs
  • 67. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden https://twitter.com/DrHughHarvey/status/1230218991026819077
  • 69. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden When used right, AI will able to do amazing things … while being subject to many of the same issues of traditional prediction modelling, including the leaky implementation pipeline
  • 71. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Recidivism Algorithm Pro-publica (2016) https://bit.ly/1XMKh5R
  • 72. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden • Algorithms are not designed to automatically encourage equitable healthcare and/or fair medical decision making • Often we seem unaware of selection mechanisms in our data, poorly reflecting society, enlarging existing inequalities or both All photos of scientists I used in this presentation were white men
  • 73. Explainable AI workshop, April 12 2021 Twitter: @MaartenvSmeden Email: M.vanSmeden@umcutrecht.nl Twitter: @MaartenvSmeden