The document discusses the concept of reputation as a fundamental social mechanism that influences trust and interactions in society, particularly through technology-driven feedback and review systems. It highlights the challenges and vulnerabilities of these systems, including the potential for manipulation and discrimination, while acknowledging their various benefits in enabling commerce and decision-making. Despite the issues, the reliance on reputation systems is likely to continue as they empower individuals and shape online interactions.

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