Dr. Pierre-Nicolas SchwabHEAD OF BIG DATA, RTBF

FOUNDER, INTOTHEMINDS

Data Innovation SummitMarch, 30 2017

#DIS2017

WHAT WE NEED ARE ETHICAL ALGORITHMS

WHY WE NEED ETHICAL ALGORITHMS

• Do you trust companies behaving badly ?

• Cow-boy behaviors must end !– Uber charging more when your

battery is low– Orbitz proposing more

expensive hotels to MAC users– Biased selection algorithm for

French universities

WHY TRUST MATTERS FOR RTBF

• Trust in medias has decreased further: 26% trust in online media in 2017 1

• Online media less trusted of all. Yet, main source of news among younger audiences 2

1 Kantar Sofres, January 20172 Reuter digital news report 2016

ALGORITHMS PLAY A ROLE IN BUILDING TRUST

Personalization algorithms represent a challenge with 3 key problems identified :

– Too much personalization key information may be missing

– Alternative viewpoints may be absent

– Privacy may be threatenedSource : Reuters Digital Report 2016

ALGORITHMS ARE NEVER NEUTRAL!

• Algorithms are

– Designed for a goal

– A reflection of a person’s biases

• Algorithms use data without knowledge of the context

• Automated data treatment may pose ethical threats

2 TYPES OF ALGORITHMIC THREATS

1. Technical limitationsRacist chatbots, less-than-perfect image recognition

2. The vision behind the algorithms– Filter bubbles– Gender inequity– Insurance: personalization vs. risk sharing– Company’s commercial goals :

« Netflix ’s metrics can not distinguish between an enriched life and addition »

Neil Hunt, Netflix CPO, RecSys 2014

5 RULES TO MAKE ALGORITHMS MORE ETHICAL

1. Avoid discrimination2. Promote gender equity3. Open up customers’ minds

(exploration) rather than trapping them (exploitation)

4. Respect right of not being tracked

5. Educate users on algorithms

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

WHAT WE DO CONCRETELY AT RTBF (1/3)

• Design of recommendation systems follow ethical rules (deliberative democracy model)

• Focus on serendipity (≠ filter bubble)

• Data scientists are not left alone: functional specifications describe what is acceptable

WHAT WE DO CONCRETELY AT RTBF (2/3)

Recommendation systems follow the deliberative democracy model:• Autonomy and choice• Information quality and debate• Respect of minorities and

marginalised groups • Gender equity• Promotion of real alternative

viewpoints

WHAT WE DO CONCRETELY AT RTBF (3/3)

GDPR and privacy “Privacy by design” :

• no recommendation compatible (ON/OFF)

• Standard profiles (persona) and possibility to « put you in the shoes of … »

CONCLUSION : DESIGN ALGORITHMS FOR GREATER GOOD

• Algorithms are instrumental to gain consumers’ trust

• Recommendation algorithms are not neutral design them carefully and ethically

• educate users on the role of algorithms