Slides pierre nicolas schwab DISummit 2017 (Big Data, Brussels)
Transcript of Slides pierre nicolas schwab DISummit 2017 (Big Data, Brussels)
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