Talk at the Global Investigative Journalism Conference 2019 in Hamburg

1. SciCAR 2019
Ways10
AI can be used
for investigations
@PaulBradshaw
Birmingham City University, BBC Data Unit

2. 1. Bullshit terms
2. I’ve got a BIG HAMMER.
Now, where can I get some
NAILS?
3. My head explodes.

3. 1. Bullshit terms
2. I’ve got a BIG HAMMER.
Now, where can I get some
nails?
3. My head explodes.

4. “Each weekday, my computer
program goes to the Chicago
Police Department's website
and gathers all crimes
reported in Chicago.”
Adrian Holovaty

5. More at pinboard.in/u:paulbradshaw/t:robotjournalism

6. • Simulation/
imitation of
intelligence, since
1950s (e.g. bots)
• A useless term
which means 427
different things
Artificial intelligence (AI)?
https://hai.stanford.edu/news/infographic-age-artificial-intelligence

7. https://insights.ap.org/uploads/images/the-future-of-augmented-journalism_ap-report.pdf

8. • Reasoning (solve problems)
• Knowledge (categorisation)
• Planning (identifying steps)
• Communication (language, e.g.
translation)
• Perception (identification, e.g. auto
cars)
What is AI used for?

9. • Content recommendations (59%)
• Commercial optimisation (e.g. ad
targeting, dynamic pricing) (39%)
• To assist journalists find stories
(intelligent agents) (36%)
• To automate or semi-automate
workflows (40% of respondents)
Reuters Institute
In news?

10. Machine
learning: subset
of AI that allows
the computer to
optimise the best
way to get results

11. • Training data
• Supervised learning (using x and y)
• Unsupervised learning (using x)
• Reinforcement learning (optimal
action)
Some ML concepts...

12. Rothe et al

13. Assogba, Machine Visions
Unsupervised learning

14. Instagram Engineering (2015)

15. • Supervised learning: classification
(“x”=“A”), regression (y=x*2)
• Unsupervised learning: association,
Clustering,
• Reinforcement learning: games,
route finding

16. 1. Bullshit terms
2. I’ve got a BIG HAMMER.
Now, where can I get some
NAILS?
3. My head explodes.

17. • Finding needles in haystacks
• Identifying trends (or departures from
trends)
• Examining an application of AI or co
mputation as the subject of the story itself
3 applications of AI for
newsrooms
Hansen et al 2017

18. BUT I’VE GOT
10!

19. 1. Establishing the scale of a
problem*

20. Atlanta Journal-Constitution
Training needed!

21. BuzzFeed

22. ICIJ

23. 2. Proving a problem exists

24. “.”
Random Forest algorithm: SRF Data (GitHub repo): https://www.srf.ch/radio-srf-virus/

25. “.”
https://www.latimes.com/local/cityhall/la-me-crime-stats-20151015-story.html

26. 3. Unmasking a system

27. https://www.propublica.org/nerds/how-propublicas-message-machine-reverse-engineers-political-microtargeting

28. NLP: Natural Language
Processing
Instagram Engineering (2015)

29. “NLP techniques trained on a
corpus of internet writing from the
1990s may reflect stereotypical
and dated word associations—the
word ‘female’ might be associated
with ‘receptionist’”
AI Now 2017 report

30. 4. Document histories:
sentiment and other clues

31. https://www.washingtonpost.com/investigations/whistleblowers-say-usaids-ig-removed-critical-details-from-public-reports/2014/10/22/68fbc1a0-4031-11e4-b03f-de718edeb92f_story.html

32. 5. Unmasking pseudo-human
behaviour

33. Kao, 2017

34. 6. Creating a basis
for further enquiry

35. https://www.apnews.com/08659d568d7448a6b500f27d98a6c3a6
https://insights.ap.org/uploads/images/the-future-of-augmented-journalism_ap-report.pdf

36. https://pudding.cool/2018/07/women-in-parliament/

37. https://stateofopendata.od4d.net/chapters/issues/artificial-intelligence.html

38. 7. Jargon and obfuscation

40. 8. Interesting associations,
linking data

41. https://artificialinformer.com/issue-one/dissecting-a-machine-learning-powered-investigation.html

42. 9. AI in the sky: satellite
imagery, drone footage, sensors
— and target acquisition

43. https://blogs.nvidia.com/blog/2019/04/04/human-rights-watch-ai-gtc/

44. https://blogs.nvidia.com/blog/2019/04/04/human-rights-watch-ai-gtc/

45. http://texty.org.ua/d/2018/amber_eng/

46. https://www.thejakartapost.com/life/2019/08/02/googles-artificial-intelligence-helps-protect-west-sumatras-rainforests.html
https://techcrunch.com/2018/03/23/rainforest-connection-enlists-machine-learning-to-listen-for-loggers-and-jaguars-in-the-amazon/

48. 10. Conversion: OCR, audio,
video — and summaries

49. https://www.wired.com/story/polisis-ai-reads-privacy-policies-so-you-dont-have-to/

50. https://www.wired.com/story/polisis-ai-reads-privacy-policies-so-you-dont-have-to/

51. http://jonathanstray.com/extracting-campaign-finance-data-from-gnarly-pdfs-using-deep-learning

52. NLG: Natural Language
Generation
Microsoft deletes racist, genocidal tweets from AI chatbot Tay

53. “.”
Tow Center, Guide to Automated Journalism

54. Tow Center, Computational Campaign Coverage
Yellow: raw data inserted into the text
Purple: calculations with the raw data.
Green: synonyms, used to add variety
“exponentially increases the possible variants for the whole text”

55. “When we teach computers to
write, the computers don’t replace
us any more than pianos replace
pianists—in a certain way, they
become our pens, and we become
more than writers. We become
writers of writers.”
Ross Goodwin

56. • Underestimated effort needed for
quality control and troubleshooting
• Most common errors due to errors in
underlying data
“Added complexity often
increased the likelihood of new
errors”

57. 11. Entry points:
personalisation, A/B headlining,
translation and chatbots

58. 11!
HOLD ON! THAT’S

59. https://github.com/BBC-Data-Unit/tree-planting

60. https://www.akqa.com/work/lokai/walk-with-yeshi/

61. https://interactive.aljazeera.com/aje/2016/malaysia-babies-for-sale-101-east/index.html

62. “The real value is not in reaching
more people, but rather in deepening
the relationship with the people you
reach.”
John Keefe https://onlinejournalismblog.com/2018/06/04/gen-summit-ais-breakthrough-year-in-publishing/

63. 1. Recording: in machine-readable
forms
2. Recombining: via automation or
algorithm to create multiple versions
or narratives
3. Re-use: “the persistence in
databases of categorised atoms
which can be manipulated for
continuing use”
Jones and Jones 2019
Structured journalism:
3 characteristics

64. https://stateofopendata.od4d.net/chapters/issues/artificial-intelligence.html

65. Microsoft Cognitive Services, Google Cloud AI

66. 1. Bullshit terms
2. I’ve got a BIG HAMMER.
Now, where can I get some
nails?
3. My head explodes.

67. 1. Data availability
2. Amortisation (story is unique)
3. Difficulty
4. Accuracy
5. Cost effectiveness vs manual
methods (dataset size)
Stray 2019
Stray’s challenges

68. “We find journalists are “writing for
machines” by converting
unstructured information into
structured data to enable automated
recombination and future re-use of
content. This impacts editorial
control by delegating responsibility
to either the algorithm or the
audience, in the name of choice.”
Jones and Jones 2019
Structured journalism

69. >15 algorithms: e.g. random forest, Bayesian networks, Support Vector Machines
and… Deep Learning (model the brain, not the world)
DARPA

70. NLG: Natural Language
Generation
https://www.aclweb.org/anthology/W17-1613

71. “[Algorithms] are not isolated
deterministic actors but an
inextricable component within a
network of communicative
practices that includes economic,
institutional and increasingly legal
and ethical issues”
Matt Carlson

73. Danke.
@PaulBradshaw, Birmingham City University
Online Journalism Blog, BBC England data unit

74. BONUS. Object verification

75. https://www.scientificamerican.com/article/human-traffickers-caught-on-hidden-internet/