The document discusses the importance of designing ethical artificial intelligence by focusing on human behavior, societal impacts, and the need for transparency and accountability in data science. It emphasizes human-centered approaches, the relationship between humans and machines, and the significance of addressing bias and ethical standards in AI development. Key areas include ensuring trust, collaboration, and enhancing human potential while considering the broader societal consequences of AI technology.

1. Designing ethical
artificial intelligence
@jivanvirdee & @hollielubbock
Fjord
May 2018, #StrataData

2. Hello,
@jivanvirdee
@hollielubbock

3. Artificial intelligence in the news (Google trends)
0
25
50
75
100
Date 2008-07 2009-02 2009-09 2010-04 2010-11 2011-06 2012-01 2012-08 2013-03 2013-10 2014-05 2014-12 2015-07 2016-02 2016-09 2017-04 2017-11 2018-05
Instance
Rising interest

4. The Backlash
Guardian article

5. Fitter,
Happier,
More Productive

6. Lonelier,
Less satisfied,
Less productive

7. Predictive & proactive

8. The issue of
Dark Patterns
https://darkpatterns.org/hall-of-shame

9. Amplified at scale

10. AI will save us/kill us
https://twitter.com/Mark__Zukerberg | https://twitter.com/elonmusk
I think you can build things and the
world gets better, with AI especially,
I’m really optimistic
Until people see robots going down
the street killing people, they don’t
know how to react
Marks understanding of the subject
is limited.

11. “We are morphing so fast that
our ability to invent new
things outpaces the rate we
can civilise them.”
Kevin Kelly, The Inevitable

12. So how do we shift the trajectory?

13. 13
Human Centred
Created from an understanding of human behaviour, motivations, and needs.
Is it the best way to solve that problem?
How can we make their lives better, easier and more fulfilling?
Are the user needs put before the business needs?
Humanity centred
Considering the effect on society as a whole.
What if everyone used your product or service?
What the worst thing that could happen to society because of it?
Does it intrinsically favour one group of people over another?

14. Fair &
transparent data
science
Responsibility &
accountability
Trust & human
machine
collaboration
Human first
3 key areas

15. Fair & transparent
data science

16. Explainable
Artificial
Intelligence

17. Accuracy vs.
explainability Accuracy
Explainability
Deep learning
Ensemble methods
SVMs
Graphical models
Decision trees
https://arxiv.org/pdf/1802.00603.pdf

18. Why is
explainability
important?
http://www.jstor.org/stable/41238108

19. Methods for
Explainability
Interpretable models
• Regression
• Decision trees
• Classification rules
Models for interpretability
• LIME
• BETA
• LRP

20. Any sufficiently
advanced
technology is
indistinguishable
from magic
― Arthur C. Clarke

21. Increasing levels of context

22. Humble machines

23. Transparency is key
to trust

24. “Transparency does not happen
on its own: it has to be consciously
audited, understood, designed
and implemented to take root”
― Andy Polaine

25. Trust and human
machine
collaboration

26. 800 million jobs
lost to automation
by 2030
Source

27. There’s even an
algorithm to check if
you’ll lose your job to
automation
27
https://www.fastcompany.com/3047269/this-calculator-
will-tell-you-if-a-robot-is-coming-for-your-job

28. AI replaces workforces

29. AI replaces workforces
AI can enable super
human powers

30. Human Machine
Together
96% 99.5% 92%
Human &
Machine
Source

31. Enhancing human potential

32. Trust =
credibility + reliability + authenticity
self interest

34. HUMAN INTELLIGENT AGENT
Augmentation Automation
Define the
Relationship

35. Problem Complexity
Bounded Open ended
Consequence of Failure
Negligible Critical
Responsibility
Machine Human
Independent
Autonomy
Collaborative
Management by exception
Supervision
Continuous Engagement

36. Bounded Open ended
Negligible Critical
Machine Human
Independent Collaborative
Management by exception Continuous Engagement
Content Moderation
Problem Complexity
Consequence of Failure
Responsibility
Autonomy
Supervision

37. Bounded Open ended
Negligible Critical
Machine Human
Independent Collaborative
Management by exception Continuous Engagement
Movie Recommendation Service
Problem Complexity
Consequence of Failure
Responsibility
Autonomy
Supervision

38. Problem Complexity
Bounded Open ended
Consequence of Failure
Negligible Critical
Responsibility
Machine Human
Independent
Autonomy
Collaborative
Management by exception
Supervision
Continuous Engagement
Three Mile Island Nuclear Plant

39. One of the team,
play to your
strengths
http://humanrobotinteraction.org/journal/
index.php/HRI/article/view/173

40. Creativity is going
to be far more
important in a
future where
software can code
better than we can.
Tom Hulme

41. Automation could make us
more human

42. “This is not a race against the
machines. If we race against
them, we lose. This is a race
with the machines.”
― Kevin Kelly, The Inevitable

43. Responsibility and
accountability

44. It takes momentum to
get noticed

46. Ethical
standards
in science

47. An issue of scale

48. Mind the bias

49. You’re only as good
as your data
So how do we make
it better?

50. From unknown to known

51. Codes of ethics
Do you have one?

52. Data for Democracy
Its my job to understand, mitigate and communicate the
presence of bias in algorithms.
Be responsible for maximizing social benefit and minimizing
harm.
Practice humility and openness.
I will know my data and help future users know it as well.
Make reasonable efforts to know and document its origins and
document its transformation.
Bias will exist. Measure it. Plan for it.
Thou shalt document transparently, accessibly, responsibly,
reproducibly, and communicate.
Engaging the whole community. Do you have all relevant
individuals engaged?
People before data - data scientists should use a question
driven approach rather than a data-driving or methods
approach. Consider personal safety and treat others the way
they want to be treated.
Exercise ethical imagination.
Open by default - use of data should be transparent and fair.
I will not over/under represent findings.
You are part of an ecosystem understand context and
provenance.
Respecting human dignity.
Respect their data even more than your own. Understand
where its sources and think about the consequences of your
actions.
Protecting individual and institutional privacy.
Diversity for inclusivity.
Attention to bias.
Respect for others/persons.
Be intentional as you work to create value.
https://github.com/Data4Democracy/ethics-resources

53. So what do we do with them?

54. Ethics Overview
Informed Consent
Data Ownership
Privacy
Anonymity
Data Validity
Algorithmic Fairness
Societal Consequences
https://www.edx.org/course/mind-of-the-universe-robots-in-
society-blessing-or-curse
https://www.coursera.org/learn/data-science-ethics

55. Designing AI with
human values in mind

56. Video Link

57. Fair &
transparent data
science
Responsibility &
accountability
Trust & human
machine
collaboration
Human first
3 key areas

58. No one’s coming.
It’s up to us.
— Dan Hon
@jivanvirdee
@hollielubbock