Slides for the Computer Ethics course at Politecnico di Milano. More information and source on GitHub: https://github.com/pietrodn/filter-bubble

1. The Filter Bubble: a threat for plural
information?
The dangers of personalizing filters
Pietro De Nicolao
Computer Ethics (2016-2017), Politecnico di Milano
December 13, 2016

2. Introduction
Concerns about the Filter Bubble
Case study: Facebook News Feed
Proposed remedies and counter-objections
Conclusions

3. Introduction

4. What is the filter bubble?
Figure 1: A unique universe of information for each of us (Pariser 2011,
p. 10)

5. Definitions
Personalized search
Personalized search refers to search experiences that are
tailored specifically to an individual’s interests by incorporating
information about the individual beyond specific query provided.
(Wikipedia)
Filter bubble
A filter bubble is the restriction of a user’s perspective that can
be created by personalized search technologies. (Haughn 2015)
Political pluralism in the media
Political pluralism in the media refers to the fair and diverse
representation of and expression by various political and
ideological groups, including minorities, in the media. (Leuven
et al. 2009, p. 12)

6. What I will show
In this presentation, I will show that:
Personalization reduces information pluralism by giving
users only what they like to see
Personalizing filters define our perception of the world and
are not neutral intermediaries
Recommender systems are relevance maximizers
Important but non-relevant stories can be left out
Different point of views are shown less
Transparency about the use of data and about the algorithms
is needed
Users must know when personalization is active
Users should be able to control it

7. Concerns about the Filter Bubble

8. The dangers of personalization
The book The Filter Bubble (Pariser 2011) describes many risks
associated with it:
Data collection and privacy
Democracy
Information (I will focus on this)
Freedom
Creativity
Censorship
Serendipity

9. Importance vs. relevance of news stories
Two metrics can be defined for news stories:
Importance: intrinsic “value” of a story with respect to society
Relevance: probability that a story will be “liked” by the user;
performance index of the recommender system
Recommender systems (personalizing filters) are relevance
maximizers
Example
“A squirrel dying in front of your house may be more
relevant to your interests right now than people dying in
Africa.”
– Mark Zuckerberg (Facebook CEO)

10. Concerns about information
Friendly world syndrome
Personalizing filters block important, but unpleasant things:
Some topics will always be not likable: war, homelessness,
poverty. . .
Different point of views are less relevant to us
Autonomy
Autonomy of the readers is compromised, as they can’t choose
what’s in or what’s out their “bubble”
Worst case scenario
Deliberate use of filters to shape the public opinion, by
governments or multinational companies

11. Case study: Facebook News Feed

12. Facebook is too friendly!
Suppose that you are a Facebook user and you identify as a liberal,
and you have both liberals and conservatives friends.
News Feed recommendation algorithm: you get more posts
which reflect what you like (relevant to you)
You may not see conservatives’ stories at all, if you interact less
with your conservative friends
Cross-cutting stories (those different from our viewpoint) are
less likely to reach us
. . . but how much?
89.4% of under-30 Italians uses Facebook (CENSIS 2016)
The issue of biased content is certainly important!

13. Facebook: Exposure to ideologically diverse content
Facebook published a study (Bakshy, Messing, and Adamic 2015)
on Science about how likely are users to view and interact with
cross-cutting content.
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Viewer affiliation
Conservative
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Fig. 3. Cross-cutting content at
each stage in the diffusion pro-
cess. (A) Illustration of how
algorithmic ranking and individual
choice affect the proportion of ideo-
logically cross-cutting content that
individuals encounter. Gray circles
illustrate the content present at each
Liberal friends
Moderate friends
servatives
0% 25% 50% 75% 100%
Percentage of ties
1. 2. 3.
Figure 2: Exposure stages of news stories
1. Potential from network: all the content shared by friends
2. Exposed: content effectively shown in users’ News Feeds
3. Selected: content clicked by the user

14. % cross-cutting content vs. exposure stage on Facebook
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Friends network News Feed User selection

15. Facebook study: conclusions
1. The friendship network is the most important factor limiting
the mix of content encountered in social media
if I have only friends of the same political affiliation, the filter
bubble is obvious
2. The effect of News Feed ranking on cross-cutting content is
limited:
-5% for liberals
-8% for conservatives
3. Individual choice influences the exposure to cross-cutting
content more than the News Feed filtering
[. . . ] we conclusively establish that on average in the
context of Facebook, individual choices more than
algorithms limit exposure to attitude-challenging
content (Bakshy, Messing, and Adamic 2015)

16. Facebook study: criticism
Limitations of the study
Underlying (false) assumption: the building of the friendship
network is independent from Facebook’s algorithms
Friends are only partly from “offline” connections
Facebook suggests both pages to like and new friends
What about sponsored content?
Methodological issues
Sample of the study: people which declare their political
affiliation
may not be representative of the entire Facebook community
Independent researchers can’t access Facebook data and
analyze it

17. Ranking = visibility
The position (rank) of a story in the News Feed is very important!
the position in the News Feed may be used to promote some
stories and not others
money can buy rankings!
even if the algorithm is “fair” now, what about the future?
Conservative Liberal
5%
10%
15%
20%
0 10 20 30 40 0 10 20 30 40
Min position
Selection(click)rate
Content type
Ideologically congruent
Ideologically cross cutting
Viewer affiliation
Conservative
Liberal
(a)
●
Figure 4: Click rate depends on the position of the story in the News Feed.

18. Proposed remedies and counter-objections

19. Moralizing filters
Problem: the Internet is showing off what we want to see, but not
what we need to see
Algorithms cannot compute “what should be seen” (Morozov
2011)
What if one day Google could urge us to stop obsessing over
Lady Gaga’s videos and instead pay attention to Darfur?
Let’s introduce “moralizing” filters!
Would it be a good idea to make multinational companies
moralizing agents?
Paternalistic, technocratic approach
Active, educated citizens should be able to autonomously
search and retrieve information
not just “ingest” whatever is thrown at them

20. Make the algorithms transparent
What if the algorithms and/or some of the data were public?
The inner working of complex neural networks and machine
learning agents is not intuitively understandable
Even if published, we may not understand those algorithms
They are often trade secrets
Knowing at least which personal data is used to make the
recommendation may prove useful

21. Facebook News Feed settings
Figure 5: A rather good solution: Facebook lets users see and customize
some parameters of the News Feed algorithm

22. Turn off the personalization!
What if we could turn off the personalization?
Personalization is the key feature of some services
Facebook without personalization would be. . . Twitter?
For other services, this would be a feasible solution
Without personalization ads would be less relevant and
profitable: no economic incentive to do so
Users should at least know whether personalization is enabled
or not

23. Conclusions

24. Conclusions
Personalization reduces information pluralism by giving
users only what they like to see
Recommender systems privilege relevance over importance
These technologies and their implementations are not neutral
Transparency about the use of data and about the algorithms
is needed
Always use those services with a critical eye!

25. References I
Eytan Bakshy, Solomon Messing, and Lada A Adamic. “Exposure to
ideologically diverse news and opinion on Facebook”. In: Science
348.6239 (2015).
CENSIS. 13 ◦
Rapporto Censis-Ucsi sulla comunicazione.
http://www.censis.it/7?shadow_comunicato_stampa=121073. 2016.
Matthew Haughn. "filter bubble" - WhatIs.com. url:
http://whatis.techtarget.com/definition/filter-bubble (visited
on 2015).
Katholieke Universitet Leuven et al. “Independent study on indicators for
media pluralism in the member states—Towards a risk-based approach”.
In: Prepared for the European Commission Directorate-General
Information Society and Media. 19 (2009).
Evgeny Morozov. “Your Own Facts”. In: New York Times Sunday Book
Review (2011).
Eli Pariser. The filter bubble: What the Internet is hiding from you.
Penguin UK, 2011.