This presentation was given by Pierre-Nicolas Schwab, Head of Big Data at RTBF, on the occasion of the 2nd annual EBU Big Data conference in Geneva (Switzerland)

1. PANEL « BUILDING TRUST »
EBU BIG DATA CONFERENCE, 21 MARCH 2017
Pierre-Nicolas Schwab
Head of Big Data, RTBF (Belgium)
psn@rtbf.be

2. WHY TRUST MATTERS
• Trust in medias has
decreased further: 26%
trust in online media *
• Online media less
trusted of all. Yet, main
source of news among
younger audiences
(source: Reuter digital
news report 2016)
* Source: Kantar Sofres (2016, 2017)

3. ALGORITHMS TOO CAN BUILD (DIS)TRUST
Personalization represent a
challenge with 3 key
problems identified :
– Too much personalization
 key information may be
missing
– Alternative viewpoints may
be absent
– Privacy may be threatened
Source : Reuters Digital Report 2016

4. 5 RULES TO MAKE ALGORITHMS MORE
ETHICAL
1. Avoid discrimination
2. Promote gender equity
3. Open up customers’
minds (exploration)
rather than trapping
them (exploitation)
4. Respect right of not
being tracked
5. Educate users on
algorithms

5. EDUCATION IS KEY
• Empower users: give
control back
• Build knowledge
• « Show » your algorithms
(open the black boxes !)
“An [algorithmic] system
that you can't audit is a
system that you can't use"
Marc Rotenberg, CPDP
conf. 2017

6. 4 MEASURES TAKEN AT RTBF (1/4)
CONFIDENTIALITY CHARTER
• New privacy and
confidentiality charter
(effective 01 Dec 16)
• Simplified yet complete
• Clear promises
• Special focus on
children
• Dedicated video

7. 4 MEASURES TAKEN AT RTBF (2/4)
SINGLE-SIGN-ON
• Single-Sign-On (SSO)
launched 01 Dec 2016
• Personal information
centralized
• Easier for user to control,
modify and erase
information
• Essential tool for
reconciliation of online
usages and respect of
GDPR

8. 4 MEASURES TAKEN AT RTBF (3/4)
RECOMMENDATION SYSTEMS
• Design of
recommendation
systems follow ethical
rules
• Personalization will
follow the concept of
deliberative democracy
• Focus on exploration
and discovery (≠ filter
bubble)

9. 4 MEASURES TAKEN AT RTBF (4/4)
EDUCATION TO ARTIFICIAL INTELLIGENCE
• 1-hour TV program with
real users (shooting on 29
March)
• Communication on
corporate website
• Badge on website to
signal algorithmic
curation
• Systems designed to
support visualization of
algorithms’ effects

10. CONCLUSIONS
• Media consumption more
and more digital : gaining
online trust is key
• Recommendation
algorithms are not neutral
 design them carefully
and ethically
• PSM have a major role to
play to educate users on
the role of algorithms