Nitesh Chawla of Notre Dame discusses society, policy and ethics in the context of cognitive and data sciences

1. Driving Data and Cognitive Sciences
Curriculum at the Nexus of Society,
Policy, and Ethics
Nitesh Chawla, PhD
Frank M. Freimann Professor of Computer Science & Engg.
Director, iCeNSA
@nvchawla

2. Data and cognitive science is …
...multidisciplinary
… curiosity + science
...a process
…about augmenting human ability and intelligence
… about deriving value

3. Brendan Tierney, 2012. Source: kdnuggets Steven Geringer, 2014. Source: kdnuggets
Data Science Venn Diagrams Galore

4. Gartner, 2014. Source: kdnuggets Gartner, 2016. Source: kdnuggets

5. Wikipedia

6. Over 100 Graduate Programs in Data Science, Analytics
Shortage of 100,000 data scientists by 2020 – Gartner, 2012

7. Concern: Driving success of data science/analytics
initiatives in corporations.

8. Building and managing big data infrastructure is hard
Processing and managing data at scale is hard
Modeling and analytics is hard
Discovering and communicating new game-changing insights
is hard
But it is even harder to actually drive deep
understanding and action.

9. Insights don’t exist in a vacuum
e.g.
• (Class-aware) Rules
X = Rule(supp, conf) → Y =f: ?

10. X = Rule(supp, conf) → Y =f: Dollar value of prediction
Original:

11. X = Rule(supp, conf) → Y =f: Insurance Profitability
Original:

12. Net Present Value of Analytics
What’s the best
choice?
What’s the best
choice given a
fixed budget?
Which predictive
model goes best
with which
external data
strategy?

13. The Nation must promote ethics in Big Data by ensuring that technologies
do not propagate errors or disadvantage certain groups, either explicitly or
implicitly. Efforts to explore ethics-sensitive Big Data research would enable
stakeholders to better consider values and societal ethics of Big Data
innovation alongside utility, risk, and cost.” White House Big Data
Strategic Research Plan.
“To reap the societal benefits of AI systems, we will first need to trust it.”
Learning to trust artificial intelligence systems, IBM.

14. “Scientific rigor and transparency in conducting biomedical research is key
to the successful application of knowledge toward improving health
outcomes.” National Institute of Health.
“The confidence in and reliability of science and engineering research is
truly invaluable and especially so at the National Science Foundation (NSF).
“Reproducibility”, “replicability” and “robustness” are broad terms that
encompass research aspects that relate to confidence in published
findings.” NSF.

15. http://www.nature.com/news/1-500-
scientists-lift-the-lid-on-reproducibility-
1.19970

16. http://www.nature.com/news/1-500-
scientists-lift-the-lid-on-reproducibility-
1.19970

17. Evolving Data and Cognitive Science
Curriculum
• Not just a product or project, but a process and
experience
• Encapsulate data science process with elements of design
and systems thinking
– Example: participatory design, interactions among elements
• Experimentation and validation
• Interpretability and comprehensibility
• Enabling action

18. Evolving Data and Cognitive Science
Curriculum
• Extends beyond data science methods to include social
engagement and responsibility, ethics, trust and safety, and
policy
• Reproducibility, rigor, repeatability, and responsibility
• Community and society engagement
• Identify, communicate, and address bias and sources of
distrust in data-enabled science
• Increased public scientific literacy and public engagement
In this era of big data, augmentation of human capabilities, and increased
automation, these particular issues have never been so important!

19. Augmenting Human Intelligence and Creativity: The
Evolving Paradigm
Ethics,
Policy,
Law
Reproducibility and
Rigor
Methods,
Algorithms,
Tools, System
Design and
Systems
thinking
Society

20. Augmenting Human Intelligence and Creativity:
Evolving Paradigm
Ethics,
Policy,
Law
Reproducibility
and Rigor
Methods,
Algorithms,
Tools,
System
Design and
Systems
thinking
Society
Source: IBM Smarter Planet

21. Vulnerability
Readiness
0 .1 .2 .
3
.4 .5 .6 .7 .8
.2
.3
.4
.5
.6
.1
Cote d’Ivoire
Laos
Guinea
Solomon
IslandsRwanda
Philippine
s
Mongolia
Poland
Russia
Georgia
C
I
V
S
L
B
R
W
A
G
I
N
L
A
O
P
H
L
M
N
G
G
E
O
P
O
LR
U
S
Invasive Species Climate Change Adaptation

22. Personalized Healthcare eSeniorCare: Successful Aging

23. Sources
…
Attribute:
Reliability
Attribute:
True/False
Events
Paris Attacks
Hurricane Sandy
Boston Marathon
Bombing
Spatial Information
(location of claims)
Temporal Information
(timestamp of claims)
Claims
Paper
Author
Content
Venue Social
Reference
Temporal
1
2
3 4
5
6
Science of SuccessSocial Sensing

24. Personalized Learning
Analyzing MOOCs for Retention

25. “What people say
What people do
And what people say they do
Are entirely different things,” Margaret Mead
Last mile challenge

26. Thank you
nchawla@nd.edu
http://www.nd.edu/~nchawla