This document discusses the ethics of creating and consuming imagery through the lens of meta, normative, and applied ethics. It provides examples of how biases can arise in machine learning systems from training data and how technologies like deepfakes and artificially generated photos can violate principles of empathy, autonomy, and integrity if used without consent or to misrepresent. The document argues that emerging technologies require careful consideration and solutions to combat potential ethical issues regarding issues like bias, consent, and authenticity.

1. The Ethics of ImageryThe Ethics of Imagery
Jen LooperJen Looper
Developer Advocate atDeveloper Advocate at
ProgressProgress
@jenlooper@jenlooper

2. agendaagenda
basics of ethical philosophybasics of ethical philosophy
ethics and the creation andethics and the creation and
consumption of imagesconsumption of images
AI, ethics, and imagesAI, ethics, and images

3. what is ethics?what is ethics?

4. "The field of ethics (or moral philosophy)"The field of ethics (or moral philosophy)
involves systematizing, defending, andinvolves systematizing, defending, and
recommending concepts of right andrecommending concepts of right and
wrong behavior."wrong behavior."
https://www.iep.utm.edu/ethics/

5. Meta EthicsMeta Ethics
How do we understand
what is right or wrong?
Abstract questioning about
value-bearing things: "Is
murder ever permissible?"

6. Normative EthicsNormative Ethics
Determines standards of
the rightness or wrongness
of actions: "If I am attacked
with deadly force, deadly
retaliation is permissible."

7. Applied EthicsApplied Ethics
Determines practical
application of moral
considerations. "In a case
of murder, how can we
categorize first degree vs.
second degree?"

8. what are the ethics of creating imagery?what are the ethics of creating imagery?
(meta ethics)(meta ethics)
consuming & sharing images?consuming & sharing images?
(normative ethics)(normative ethics)
what if the imagery is created by an AI?what if the imagery is created by an AI?
(applied ethics)(applied ethics)

9. ethics inethics in
photographyphotography
https://thomsonfoundation.edcastcloud.co
m/learn/the-photographer-s-ethical-
toolkit-self-paced-6636

10. The meta ethics of aThe meta ethics of a
producer of imageryproducer of imagery

11. Images must beImages must be
produced andproduced and
consumed with:consumed with:
EmpathyEmpathy
AutonomyAutonomy
IntegrityIntegrity

12. EmpathyEmpathy
understand yourunderstand your
boundaries, require consentboundaries, require consent
stock photo

13. AutonomyAutonomy
respect your subject's self-respect your subject's self-
governancegovernance
“I left Belize when I was 12 or
13, just as all my friends were
getting swept up into gangs.
...After I came to America, they
were all killed in the exact
order that they were standing
in that photograph.” Photo by
Brandon Stanton, HONY.

14. IntegrityIntegrity
respect context, don'trespect context, don't
misrepresentmisrepresent

15. Note: ethics are subjectiveNote: ethics are subjective
and fluid according to theand fluid according to the
genre/audience of an imagegenre/audience of an image
You can edit an image destined for an art
gallery, but not for a documentary

16. Normative ethicsNormative ethics
test cases that allow us totest cases that allow us to
set standardsset standards

17. classic examples ofclassic examples of
unethical imageunethical image
creationcreation
normative ethics of image creation

18. CroppingCropping
violates integrity of image and entirely
changes its meaning

19. PhotoshoppingPhotoshopping
violates subject's autonomy and intent

20. AdditionsAdditions
violates empathetical restraints via racial
pandering, especially pre-trial

21. SubtractionsSubtractions
violates context - the image is better but for edits
are inappropriate for a documentary

22. AcceptabilityAcceptability
"Napalm Girl"
Does the end justify the means?
violates subject's autonomy, but
photographer was on the receiving end of
a war, and the course of the war was
changed because of this photo

23. A short quizA short quiz
source: Thomson Foundation
images: Austin Neill, Savannah Dodd

24. You have been hired to take photographs of a beach to promote
tourism, but the beach is littered with plastic waste. What do you do?
A: Take well-framed pictures that represent the beach accurately and
do basic editing to make the scene more vivid, but leave the rubbish
B: Clean up a small area and photograph at an angle so that no litter
is visible in the frame
C. Digitally edit away the rubbish so that it looks more inviting

25. You are on a small boat tour on a river. You are impressed by the
paddling technique of the person hired to steer your boat, and you
want to take photographs of her. What do you do?
A. You ask her if you can take photographs.
B. You take a few photographs without asking. You show her the
images you took, and ask her if you can take more. You offer to send
her a few if she likes them.
C. You take photographs without asking. This boat tour is a tourist
attraction, so surely it is appropriate to photograph her.

26. Using imagesUsing images
ethicallyethically
normative ethics of image consumption

27. The ethics of memesThe ethics of memes

28. "where are they now?""where are they now?"

29. tl;dr; memes aretl;dr; memes are
totally unethicaltotally unethical
maybe stick to cute animals
images taken out of context for fun
altered and cropped
recaptioned
zero consent

30. (is it ok to dress them up?)

31. buckle up, peoplebuckle up, people

32. The next generation:The next generation:
Machine Learning +Machine Learning +
ImagesImages

33. ML is consuming andML is consuming and
producing images inproducing images in
completely new wayscompletely new ways

34. are we ok with that?are we ok with that?

35. Can we used appliedCan we used applied
ethics to combat theethics to combat the
challenges that arechallenges that are
coming?coming?

36. Example 1: bias inExample 1: bias in
imageryimagery
premise: because bias in the training of
data imagesets creates flawed outcomes,
we need to remove bias

37. https://www.youtube.com/embed/QxuyfWoVV98?enablejsapi=1

38. Machine LearningMachine Learning
BiasBias
how does it happen?how does it happen?

39. ML Algorithms learn onlyML Algorithms learn only
what they are trained towhat they are trained to
learnlearn
Garbage In, Garbage Out
Incomplete training data = incorrect
assumptions
Human bias easily translates to ML bias,
but ML 'sounds more legit' so watch out

40. what's at stake?what's at stake?

41. Predict the future criminalPredict the future criminal
(guess who)(guess who)
algorithmic 'risk assessment'

42. is this ok?is this ok?
tl;dr; NO

43. how can we fix it?how can we fix it?

44. Make sure youMake sure you
remove bias fromremove bias from
data from thedata from the
beginningbeginning

45. Check yourself: whatCheck yourself: what
biases do you bringbiases do you bring
to the project? Checkto the project? Check
the desired outcomethe desired outcome

46. Example 2Example 2
machine-generated photosmachine-generated photos
premise: most images are scraped from
datasets without consent, so their use
must be carefully controlled

47. thispersondoesntexist.com

48. Is this ok?Is this ok?
the algorithm uses 70k 'real people' images scraped
from Flickr
you are given the choice to remove your photo from
the image set (
) - the burden is on you
is it better to generate fake people for stock images?
problem: we don't know the eventual use of this set
https://github.com/NVlabs/ffhq-
dataset

49. Example 3Example 3
DeepfakesDeepfakes
video impersonation
premise: with new technology, videos can
be convincingly faked - maybe it should
should be illegal

50. https://www.youtube.com/embed/cQ54GDm1eL0?
enablejsapi=1

51. Deepfakes are super usefulDeepfakes are super useful
for...for...
Revenge porn
Screwing up politics
Causing mayhem

52. is this ok?is this ok?
Deepfakes are totally unethical - violating
all the norms of empathy, autonomy, and
integrity

53. What can we do?What can we do?
check the source
tech problems may require tech
solutions:
check video metadata with InVid
check for authenticity using
Blockchain (TruPic service)
create a certification service for
authenticity
educate yourself

54. Example 4Example 4
AI Generated ArtAI Generated Art
premise: art - real art - is starting to be
created by ML algorithms. It's coming on
the market and needs to be valuated.

55. Style transferStyle transfer

56. AI "Art" auctionsAI "Art" auctions
by a Generative Adversarial Network, sold for $423,500 at Christie's

57. GANGAN
2 NNs: a generator that synthesizes new
samples from scratch, and a discriminator
that takes samples from both the training
data and the generator’s output and
predicts if they are “real” or “fake”.

59. yes butyes but
is it art?is it art?
or just a mashup?

60. CANCAN

61. Creative AdversarialCreative Adversarial
NetworkNetwork
Generating art based onGenerating art based on
style evolutionstyle evolution

63. AIs are getting prettyAIs are getting pretty
creativecreative
Should we be worried?
What if museums come underpressure to
sell off their permanent collection in favor
of this new 'modern art'
Does it qualify?
When does it evolve from mashup to
creatively-generated art?

64. Example 5Example 5
fashion, photoshopping,fashion, photoshopping,
and those weird makeupand those weird makeup
appsapps
premise: 'beautifying' technology that
alters images should be monitored as it
comes with serious social consequences.

65. Meitu Makeup apps