Support for the course Technologies and Innovation

1. Digital Technologies and Innovation
Ethics
April 2019
http://DSign4Change.com

2. Course Portal:
http://DSign4Methods
.com
©2019 Business Analytics Institute
The objective of this course is to
build the students’ knowledge of the
practice of Business Analytics in a
variety of industrial settings
Ethics

3. “The digitalization of human activity shapes the very definitions of how we
evaluate the world around us.“ L. Schlenker
@SOURCE

4. Ethics can be defined as shared
values that differentiate right from
wrong
• “fairness”
• “free choice”
• “privacy”
• “representation”
• “truth”
• “trust”

5. Personal Information
• Personal information reveals who we
are, what we think, and what we do
• PII includes a name, an e-mail address,
a zip code, an IP address
• The Database of Intentions
• GDPR, PDPA, the California
Consumer Privacy Act
Ethics

6. 6©2017 LHST sarl
• GRDP develops a vision that integrates
nature, context and treatment needs
• as well as risks for individual freedoms
• The legislation modifies the processes of data
entry, processing and archiving of personal
and sensitive data
• Establish Privacy by design - technical,
operational and strategic aspects
• There is no single solution!
Schlenker (2017), The DPO and Data Science
Ethics

7. • How has data redrawn the lines between rich and
poor?
• Which personal rights can be assigned with explicit
or implicit consent?
• To what extent such intermediaries collect and
control the records of human interaction?
• Who should be held accountable for the loss or
misuse of our data?
7©2017 LHST sarl
Ethics

8. Introduction
• Implicit bias refers to the attitudes that
affect our understanding, actions, and
decisions
• The impact of the cognitive sciences
• Examples of implicit bias – framing,
anchoring, proximity bias…
• How objective should decisions be?

9. • Taking decisions separates man from machines
• Innovations in algorithmic trading, self-driving
cars, and robotics
• The distinction between human and artificial
intelligence is becoming increasing difficult to
distinguish
• The goal of AI is to replace human decision-
making
Ethics

10. • Who is legally responsible for the implicit bias
inherent in automated decision -making?
• Can we bank on methodologies we don’t
understand?
• Does reinforced learning make us prisoners of the
past?
• Do we understand that the inherent logic of these
platforms can be gamed?
Ethics

11. • Management is all about taking better
decisions
• What do better decisions mean (faster,
more impressive, more precise) ?
• Is it observable – how is something more
precise answer to a problem?
• The challenge is deciding what we want to
measure
Ethics

12. • Data Science is about understanding objectives,
motivations and actions The illusion of rational
decision-making
• Cluster Analysis requires classifying a sample of
objects on the basis of a set of measured variables
• Michal Kosinski and David Stillwell suggest profiing
patterns of behavior rather than demographic data
• Applications in marketing, politics and economics
• The “success” of Cambridge Analytica
Ethics

13. • Is freedom of choice compatible
with Internet silos?
• How can we control the
extrapolation of personally
sensitive data?
• Has the “customer become the
product” in your business?
• Should innovation be limited to
creating objects of manipulation?
Ethical considerations
Ethics

14. Fake news travels faster than the truth
• The goal of Information technology
always been to provide the Ground
Truth
• From Blind trust to referred trust
• Fake news travels faster than the
truth
• The gap between truth and trust
Ethics

15. • The Blockchain is a distributed database of transactions
• Each transaction generates a hash dependent upon both
the transaction and previous transaction.
• Transactions are entered in the order in which they
occurred
• All nodes (computers) are able to validate a transaction
• If a transaction is approved by a majority of the nodes
then it is written into a block
• No one or several nodes control the Blockchain.
Ethics

16. • Can technology be the standard of both truth
and trust?
• To what degree do you accept the primacy of
transparency?
• Can digital citizen’s rights be reconciled with
the requirements of public ledgers?
• Can human nature accept a radically different
basis for the distribution of wealth?
Ethics

17. • To what extent must we understand the
context of how the data is collected,
analyzed and transmitted?
• Do we need to understand their
assumptions and and limits of Data
Science?
• Is a manager’s job today to coordinate
human and software agents?
• Which functions of management should be
delegated to bots, and why?
Ethics

18. • Management is all about taking better
decisions
• What do better decisions mean (faster,
more impressive, more precise) ?
• Is it observable – how is something more
precise answer to a problem?
• The challenge is deciding what we want to
measure
©2019 L. SCHLENKER
Ethics

19. • To what extent must we understand the
context of how the data is collected,
analyzed and transmitted?
• Do we need to understand their
assumptions and and limits of Data
Science?
• Is a manager’s job today to coordinate
human and software agents?
• Which functions of management should be
delegated to bots, and why?
Ethics

20. • To what extent must we understand the
context of how the data is collected,
analyzed and transmitted?
• Do we need to understand their
assumptions and and limits of Data
Science?
• Is a manager’s job today to coordinate
human and software agents?
• Which functions of management should be
delegated to bots, and why?
Ethics