We all want to act locally while going global, and maintain an inclusive multilingual work environment for the international workforce. Every AI model has its linguistic, cultural, and geopolitical biases. Besides providing better linguistic quality for specific languages and domains, a particular Machine Translation system may not be fully compliant with local dialect, tone of voice, gender, and data locality rules. In this talk, we consider practical cases when those biases create obstacles in building a global presence and an inclusive multilingual work environment for an international company. We discuss how to dodge those biases by using multi-vendor international AI, and in some cases go further, by leveraging those biases to create more diverse and inclusive translations.

1. INTENTO
Konstantin Savenkov
Intento CEO
Dodging AI Biases
in Future-Proof
Machine Translation
Solutions
© Intento, Inc. / October 2020
GlobalSaké

2. © Intento, Inc. / October 2020
AGENDA
2
Some context on Intento
—
Future of Work, AI and company culture
—
Using MT for multilingual communication
—
Case Study 1: Gender Bias
—
Case Study 2: Tone of Voice
—
Case Study 3: Data Locality
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Key Takeaways

3. © Intento, Inc. / October 2020
SOME CONTEXT ON INTENTO
3

4. ENTERPRISES
MASSIVELY FAIL
* Share of US companies with successful AI deployment
(Deloitte State of Cognitive Survey 2017)
INTENTO4
20%*
Wrong vendor selected
Failed integrations
Failed pilots
Failed to deliver ROI
© Intento, Inc. / September 2020
TO ADOPT
AI

5. © Intento, Inc. / October 2020
BRIDGING THE GAP BETWEEN
MT CAPABILITIES AND ADOPTION
5
MT Procurement
MT Need MT Systems
Localization

6. © Intento, Inc. / October 2020
BRIDGING THE GAP BETWEEN
MT CAPABILITIES AND ADOPTION
6
MT Procurement
MT Need MT Systems
Localization

7. © Intento, Inc. / October 2020
BRIDGING THE GAP BETWEEN
MT CAPABILITIES AND ADOPTION
7
MT Procurement
—
MT Curation
MT Need MT Systems
Localization

8. © Intento, Inc. / October 2020
BRIDGING THE GAP BETWEEN
MT CAPABILITIES AND ADOPTION
8
MT Procurement
—
MT Curation
—
Multi-Engine MT
MT Need MT Systems
Localization

9. © Intento, Inc. / October 2020
BRIDGING THE GAP BETWEEN
MT CAPABILITIES AND ADOPTION
9
MT Procurement
—
MT Curation
—
Multi-Engine MT
—
Multi-Purpose MT
MT Need MT Systems
Localization
Customer Service
Office Productivity
Global Community

10. © Intento, Inc. / October 2020
BRIDGING THE GAP BETWEEN
MT CAPABILITIES AND ADOPTION
10
MT Procurement
—
MT Curation
—
Multi-Engine MT
—
Multi-Purpose MT
—
Continuous Improvement
MT Need MT Systems
Localization
Customer Service
Office Productivity
Global Community

11. © Intento, Inc. / October 2020
FUTURE OF WORK, AI,
AND COMPANY CULTURE
11

12. © Intento, Inc. / October 2020
FUTURE OF WORK AND AI
FROM TOOLS TO COLLEAGUES
12
AI is more than just a tool
—
experience (pre-training)
—
test assignments (evaluations on your data)
—
onboarding (domain adaptation)
—
continuous learning

13. © Intento, Inc. / October 2020
RETHINKING TEAMS
13
Team as a cooperation of
cognitive models,
both human and artificial

14. © Intento, Inc. / October 2020
AI AND COMPANY CULTURE
14
Can we afford to have only a
part of the company aligned
with its culture and values?

15. © Intento, Inc. / October 2020
MT FOR MULTILINGUAL
COMMUNICATION
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16. © Intento, Inc. / October 2020
MT ADOPTION BOTTLENECKS
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technical (integration)
—
linguistic (domain adaptation)
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economical (supply chain)
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cultural (biases)
—
security & legal (privacy)

17. © Intento, Inc. / October 2020
MT FOR COMMUNICATION
17
INTENTO MT
HUB
INTENTO

18. © Intento, Inc. / October 2020
GETTING COMMUNICATION RIGHT
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pre-moderation is not feasible
—
right communication = right
culture

19. © Intento, Inc. / October 2020
GETTING COMMUNICATION RIGHT
19
pre-moderation is not feasible
—
right communication = right
culture
—
adding MT to the mix

20. © Intento, Inc. / October 2020
THINGS TO LOOK AFTER
20
Gender
—
Tone of Voice
—
Privacy

21. © Intento, Inc. / October 2020
WORKING AROUND
THE GENDER BIAS
21

22. © Intento, Inc. / October 2020
GENDER BIAS
IN MACHINE TRANSLATION
22
Gender Bias in MT as evaluated in WinoMT Challenge [1]
—
carefully measures
the bias
—
in practice,
other cases
create more issues
(see next slide)
[1] “Evaluating Gender Bias in Machine Translation”, Gabriel Stanovsky, Noah A. Smith, Luke Zettlemoyer, 2019, https://arxiv.org/abs/1906.00591

23. © Intento, Inc. / October 2020
GENDER BIAS
IN COMMUNICATION
23
Source text (English)
Machine Translation
(French)
COMMENT
Are you ready? Es-tu prêt? MASCULINE
Are you ready? Es-tu prête? FEMININE
Are you surprised? Tu es surpris? MASCULINE
Are you surprised? Tu es surprise? FEMININE
Lack of context
—
Defaults to either
feminine or
masculine
—
Baseline MT
engines are not
consistent

24. © Intento, Inc. / October 2020
GENDER BIAS
MOSTLY MASCULINE BY DEFAULT
24
English to French
—
31 segment
—
stock models
—
mostly masculine
A B C D E F GA B C D E F G
Default gender distribution

25. © Intento, Inc. / October 2020
GENDER BIAS CONTROL
HOW TO FIX IT?
25
Option 1: Copy & paste from
Google Translate Web App
(supports gender control)
—
Option 2: Use long phrases,
adding some context
—
Option 3: MT-agnostic NLP
Not for French, not secure, cumbersome,
no customization.
You can instruct support operators, but
not employees and or.
Works to a certain extent, provides a
wider choice of MT engines

26. © Intento, Inc. / October 2020
GENDER CONTROL
ADJUST TO FEMININE
26
English to French
—
31 segments
—
stock models
—
let’s make it more
FEMININE
A B C D E F GA B C D E F G
Gender adjustment => feminine

27. © Intento, Inc. / October 2020
GENDER CONTROL
ADJUST TO FEMININE
27
English to French
—
31 segments
—
stock models
—
let’s make it more
MASCULINE
A B C D E F GA B C D E F G
Gender adjustment => masculine

28. © Intento, Inc. / October 2020
CONTROLLING
TONE OF VOICE
28

29. © Intento, Inc. / October 2020
TONE OF VOICE CONTROL
SAMPLES FROM SUPPORT CHATS
29
Source text (English)
Machine Translation
(German)
COMMENT
Can you share your screen?
Können Sie Ihren Bildschirm
freigeben?
FORMAL
Could you help me? Kannst du mir helfen? INFORMAL
Make sure you report any of
these issues.
Stellen Sie sicher, dass Sie eines
dieser Probleme melden.
FORMAL
Can you give an example? Kannst du ein Beispiel geben? INFORMAL
Formal vs.
Informal
—
Crucial for Live
Chats
—
Baseline MT
engines are not
consistent

30. © Intento, Inc. / October 2020
TONE OF VOICE CONTROL
DEFAULT MT OUTPUT
30
English to German
—
210 segments
—
stock models
A B C D E F G

31. © Intento, Inc. / October 2020
TONE OF VOICE CONTROL
HOW TO MAKE IT INFORMAL?
31
Option 1: Use DeepL with
formality=less (99.5% accuracy)
—
Option 2: Generate synthetic
training data, hoping
translations become more
informal
—
Option 3: MT-agnostic NLP
What if you need a custom model and
terminology, or another MT has better
linguistic quality for you?
Expensive and time-consuming, also
introduces bias into the model
Works to a certain extent, provides a
wider choice of MT engines

32. © Intento, Inc. / October 2020
TONE OF VOICE CONTROL
MT-AGNOSTIC ADJUSTMENT
32
English to German
—
210 segments
—
stock models
—
let’s make it more
INFORMAL
A B C D E F G

33. © Intento, Inc. / October 2020
TONE OF VOICE CONTROL
MT-AGNOSTIC ADJUSTMENT
33
English to German
—
210 segments
—
stock models
—
let’s make it more
FORMAL
A B C D E F G

34. © Intento, Inc. / October 2020
PRIVACY PROTECTION
34

35. © Intento, Inc. / October 2020
DATA PROTECTION LAWS
35
A B C D E F
According to DLA Piper https://www.dlapiperdataprotection.com/index.html?t=world-map as fetched on 2020-10-20

36. © Intento, Inc. / October 2020
CLOUD MT DEPLOYMENTS
36
A B C D E F
Alibaba
Amazon
Baidu
DeepL
Globalese*
Google
GTCom*
IBM
Microsoft
Mirai
ModenMT*
Naver
Niutrans*
PROMT
Rozetta
SDL*
Sogou
Systran*
Tencent
Tilde*
Yandex
Youdao
* On-premise and private cloud deployment available

37. © Intento, Inc. / October 2020
DATA AND PRIVACY PROTECTION
37
Communication may contain PII, healthcare, HR, financial data.
—
Option 1:
- select proper MT vendor for every region
- when in doubt, use private-cloud deployments
—
Option 2:
- proper DPA and data protection clauses + insurance
—
Option 3: pseudonymization to remove PII

38. © Intento, Inc. / October 2020
KEY TAKEAWAYS
38
Machine Translation becomes more and more ubiquitous. It
becomes more like our coworker than a tool.
—
When it’s biased, it may damage our work environment and
culture.
—
As of today, it’s mostly masculine by gender and quite
inconsistent by tone of voice.
—
It’s possible to dodge those biases using NLP paired with MT.
—
Make sure you know where your MT sits so that you stay
compliant.

39. THANKS!
ks@inten.to
39
Konstantin Savenkov, CEO
ks@inten.to
2150 Shattuck Ave
Berkeley CA 94705
INTENTO
https://inten.to