This talk is going to present the concept
of informational friction from philosopher Luciano Floridi
and demonstrate its value in describing and perceiving
the privacy implications of algorithmic systems.
Currently, popular discourse and traditional theories of privacy
do not fully account for the unique risks posed
by predictive models which are becoming more and more popular.
Facebook is no stranger to criticism over their privacy
practices, but this criticism often
centers around issues like data breaches, hacks, and handling
of personal information.
Popular theories of privacy such as those from Altman, mailing
and duration, nissenbaum capture these issues well,
what becomes strained when the focus turns from disclosure
to inference this makes it difficult to fully describe
the privacy impacts of Facebook's ad
targeting algorithms, and also to account
for the feelings of creepiness reported by users and studies.
This infographic here shows all the traits which advertisers
can target including demographic information, entertainment
interests, and much more however,
very few of these traits are provided directly
by the users themselves the rest are possible
only because Facebook's algorithm can infer them
from users data there then exists
a disconnect between what people knowingly disclose
and what machine learning algorithms can infer,
we want a conceptual lens that focuses our attention not
on the specific information being collected,
but on everything that can be learned given available data
and tools.
Informational friction may provide such a lens,
It's a qualitative measure of how much
work is required to access someone's information.
The level of friction determines how much
privacy is attainable in a given system.
An example will help illustrate this better.
So imagine an observer inside of a dark room
without any light source informational friction
and there is very high some work must
be expended in order to access any information about the sea,
this friction can be reduced however, in several ways,
including globally for any observer in the room
by turning on a light or for only one person by giving them
night vision goggles.
However, if an observer is visually impaired
or is made to wear a blindfold these illusions
will have no effect on friction.
Informational friction is affected
by environmental technological and individual factors,
all these factors together determine
how easily information flows within a system
and how much additional work is required to access information
that's currently inaccessible.
We suggest that the fundamental problem with Facebook's
targeting algorithms that they greatly
reduce informational friction limiting
the privacy of its users as an example of how little work is
actually required, this study from 2018
recruited cannabis users despite Facebook
not offering the option by targeting
other categories, which were believed
to be correlated with that one.
So the full picture of the information
that Facebook can access on its users
looks something like this.
The two inner circles represent information that's explicitly
disclosed and also traits that can be directly targeted
through the ad system.
However, as the last example demonstrated,
much more information can be extracted and targeted
their indirect methods, which is represented here
by the gray circle.
Finally, we must acknowledge the possibility of future methods
to infer even more information than what's
currently available.
Our current methods are good at dealing with the first two
types of data.
For example, reactions to Facebook allowing advertisers
to target certain ads based on race
reveal culturally and legally entrenched norms regarding
which information Facebook should
be allowed to collect and share with advertisers however,
this is a point where both traditional theories
and Facebook power to restrain advertisers [? ent. ?]
Writing after Facebook revise their policy
to bar ethnicity for certain ad campaigns,
[? Spiker ?] at all demonstrated how malicious advertisers could
still infer and target this trait
through the use of indirect markers,
these markers can seem completely innocuous
like online games or mountain biking
and would be unlikely to violate any privacy norms on their own.
However, in combination and within the context
of Facebook the targeted ad model it can reveal potentially
sensitive information and be used in discriminatory ways,
thus restrictions on what information can be targeted
will always be insufficient.
Informational friction brings us to a rather simple conclusion,
it's just too easy for Facebook to access information
on its users.
The powerful machine learning algorithms enormous amounts
of data and craftiness of advertisers
is nearly impossible to account for all the ways
in which user's information can be accessed and used.
This weekend any effort to protect privacy
through explicit restrictions, instead
it might be better to focus on ways
to increase in informational friction
and make it more difficult for users information
to be inferred in the first place.
The points on screen if implemented,
would go some way towards making it more difficult to infer
this information.
And finally, we note that I ask is also
relevant to policies surrounding privacy as well
as social computing and CSCW research,
especially in interpreting the results of empirical studies
on privacy behaviors and attitudes.
So in conclusion, we think that I
have is a valuable perspective that
allows us to more readily perceive privacy related
aspects of algorithmic systems and we could easily
do with other frameworks.
Thank you and I'd be happy to answer any questions
in this short time.