By Chris Diehl, The Data Guild Our modern world represents an inextricable blend of the cyber and physical domains. Networked devices with sensing and computational capabilities continue to expand the extent of the cyber-physical interface, increasing risks across scales from the individual to the societal level. No longer do we fully understand the extent of what is being collected and assimilated about us. For marginalized populations globally, the consequences of this can be severe. What is the nature of the risk at the level of the individual? How can the data science community respond to improve the current reality? Chris will present some initial thoughts to frame the concerns and outline a way forward.

1. Digital Vulnerability:
Characterizing Risks and Contemplating
Responses
@ChrisDiehl
July 28, 2016

3. Global Impact
&
Local Perspective

4. Agenda
Current Risks
Potential Responses
Existing Gaps
Future Options

5. Digital Vulnerability
Unwitting
Disclosure
Discrimination
Witting
Disclosure
Privilege
Marginalization Opportunity

6. Disclosure
Risk

7. Prediction
Risk

8. Actuation Risk

9. Where to Start?
The Design Conversation

10. Defining the Middle Ground
No Raw Data
Collection
and
Retention
Unconstrained
Raw Data
Collection and
Retention
No Personalization
“Measure and Predict
All the Things”
Private, Fair,
Interpretable
Inference
“Equitable Inference”

11. Existing Gaps
Widely Understood Options
Trusted Implementations
Design Patterns and Principles

12. Community Knowledge Creation
and Dissemination
1. Define Algorithmic Options
2. Develop Best Practices and
Open-Source Implementations
3. Teach the Approaches
4. Gather Feedback from
Practitioners

13. Let’s Do This!
bit.ly/equitable_inference_experim
ent
chris@thedataguild.com