We rely on digital reputation to do every activity online - buying groceries, electronics, books, etc., going to a restaurant, finding a good doctor, etc. Many parties make a living based on this very principle of reputation - uber, airbnb, etc. While reputation systems empowers us there are many downsides to it - gaming the reputation systems, discriminatory behaviors, personal biases towards our reviews and feedbacks are some of them.

1. Research Team - InvisiBits
12/15/2015

2.  Reputation is a social mechanism by which we come to
trust one another, in all aspects of our society.
 In a very real sense, reputation enables friendships,
commerce, and everything else we do in society.
 It's old, older than our species, and we are finely tuned
to both perceive and remember reputation
information, and broadcast it to others.

3.  Reputation now involves technology.
 Feedback and review systems.
 eBay rankings
 Amazon reviews
 Uber ratings
 Google PageRank
 Wikipedia articles
 Our reputations are, at least in part, based on what we
say on social networking sites like Facebook and
Twitter.

4.  In a small town, everyone knows each other and
lenders can make decisions about who to loan money
based on personal reputation
 However it has issues
 Prone to personal preferences
 Discrimination against outsiders
 The system does not scale
 Solution – use technology – specifically use credit
reports and scores
 Allows us to borrow money from totally unknown
people

5.  Even technology based systems are not perfect.
 It can be attacked technologically.
 Someone could hack the credit bureau’s database and
enhance her reputation by boosting her credit score.
 She could steal someone else’s reputation.

6.  Social engineering attacks could fool someone by hijacking
the reputation of someone else.
 Anyone who can figure out how to game these reputation
systems could artificially boost their reputation.
 Fake reviews Amazon, eBay, Yelp, etc. (This is a very difficult
problem to solve when there are humans available to write
reviews for you for less than a penny.)
 Fake reputation in social media– buying Twitter followers,
Facebook likes
 Aristocratic behavior: the rich get richer phenomenon –
very difficult to enter the market (e.g. a new product in
Amazon)

7.  Our biases towards what we review
 For example, you have got a ½ price meal at a restaurant
– for the price you paid, you give a 5 start, but in reality,
it may not deserve a 5 star rating
 Demographics of people could have different tastes. For
example, not so techies may like the recent movie Ex-
Machina compared to those who are in the tech world.
 You may want to show off that you bought something
expensive or you went to an expensive restaurant; even
though the experience was below par, the human nature
is that we tend over-rate them to peers.

8.  Difficult to capture discriminatory behaviors
 For a product review, you may write a bad review
because a bad seller or a bad carrier – which does not
necessarily mean that the product is bad.

9.  It is an arms race between
 Those trying to artificially enhance their reputation
 Those trying to detect those enhancements
 Researchers have been addressing this topic for many
years – it is far from solved.

10.  Despite all the negatives mentioned earlier, we will
continue to rely on these systems.
 They empower us in so many ways.
 People can achieve a level of fame and notoriety much
more easily on the Internet.
 New ways of making a living.
 Uber, Airbnb
 Popular bloggers, YouTubers
 It helps us make better decisions based on the
experience others had.
 Amazon reviews for products.
 Yelp reviews for restaurants.

11.  Resilient reputation systems: stay ahead of the parties
those try to game the systems.
 E.g. detect fake reviews and feedbacks
 Support democratic behavior (not sure if this is ever
possible).
 E.g. better opportunity for new comers
 Reputation federation: take into consideration real social
network reputations.
 E.g. real FB likes, Twitter followers, etc.
 Take into account our biases towards reviews and segregate
them accordingly.
 E.g. demographics, context, etc.

12.  https://www.schneier.com/blog/archives/2015/11/reput
ation_in_t.html
 http://edge.org/conversation/gloria_origgi-what-is-
reputation