Nowadays “Artificial Intelligence” is everywhere! And rightly so, it does enable us to do really cool things, things we couldn’t even imagine doing just a decade ago. In fact, it sometimes just feels like magic. This ‘magic’ behind it is often powered by “Machine Learning”. But even “AI” has its limitations. I’ll show examples where “AI” and ML have failed (sometimes with horrible consequences) and will explain why failures are unavoidable in ML but also mention what we can do to reduce them in the future. Furthermore, I’ll showcase how current AI implementations discriminate against minorities and how that in some cases even leads to a higher risk of death for those groups. I’ll cover the bias that humans introduce and I’ll explain how poor choice of data makes our world even more unjust than it already is.

1. Artificial
Intelligence?
more like Artificial Stupidity!

4. Demo Time!
Photo by Alex Radelich on Unsplash

7. Photo by Franck V. on Unsplash

9. What is Data Science, Artificial Intelligence(AI), Machine Learning (ML), Neural
Networks, Deep Learning?
9
Artificial IntelligenceData Science
Machine Learning
Deep Learning
Neural Networks
“the theory and development
of computer systems able to
perform tasks normally
requiring human intelligence”
- oxforddictionaries.com
Scientific and
engineering methods,
processes and systems
to extract useful
information or insights
from data.
Algorithms, which optimize
their performance by
processing of data.
Algorithms with an
architecture inspired by the
human brain.
Neural Networks with a
“deep” structure (= high
number of layers)
Expert Systems
Chess Programs
Big Data Analytics

11. Streetbump app boston

12. Demo Time!

16. Correctional Offender Management Profiling for Alternative
Sanctions (COMPAS)

18. Photo by Franck V. on Unsplash

19. What can we do about this?

22. Data
ML
model
Predictions
Standard ML
Generalization error
Data
ML
model
Verified Predictions
Interpretable ML
Generalization error + human experience
Interpreta
bility
humaninspection
model/data
improvement

23. L
I
M
E

25. More diverse teams
Photo by Chris Barbalis on Unsplash

26. Data Ethics
Canvas

28. Photo by Patricia Valério on Unsplash

31. What are your
questions?
@AikoPath Aiko Klostermann
Thanks for your attention!