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This chapter discusses the responsibilities of Google as the leading search engine provider to provide fair and unbiased results. In its role, Google has a large influence on what is actually searchable on the Web as well as what results users get to see when they search for information. Google serves billions of queries per month, and users only seldom consider alternatives to this search engine. This market dominance further exacerbates the situation. This leads to questions regarding the responsibility of search engines in general, and Google in particular, for providing fair and balanced results. Areas to consider here are (1) the inclusion of documents in the search engine’s databases and (2) results ranking and presentation. I find that, while search engines should at least be held responsible for their practices regarding indexing, results ranking, delivering results from collections built by the search engine provider itself and the presentation of search engine results pages; today’s dominant player, Google, argues that there actually is no problem with these issues. Its basic argument here is that “competition is one click away”, and, therefore, it should be treated like any other smaller search engine company. I approach the topic from two standpoints: from a technical standpoint, I will discuss techniques and algorithms from information retrieval and how decisions made in the design of the algorithms influence what we as users get to see in search engines. From a societal standpoint, I will discuss what biased search engines mean for knowledge acquisition in society and how we can overcome today’s unwanted search monopoly.
Dirk Lewandowski, Hamburg University of Applied Sciences, Germany
This is a preprint of an article accepted for publication.
Lewandowski, D.: Is Google responsible for providing fair and unbiased results? In: Floridi, L.; Taddeo,
M. (eds.): The Responsibilities of Online Service Providers. Berlin Heidelberg: Springer, 2017. S. 61-
77. doi: 10.1007/978-3-319-47852-4_4
This chapter discusses the responsibilities of Google as the leading search engine provider to
provide fair and unbiased results. In its role, Google has a large influence on what is actually
searchable on the Web as well as what results users get to see when they search for
information. Google serves billions of queries per month, and users only seldom consider
alternatives to this search engine. This market dominance further exacerbates the situation.
This leads to questions regarding the responsibility of search engines in general, and Google
in particular, for providing fair and balanced results. Areas to consider here are (1) the
inclusion of documents in the search engine’s databases and (2) results ranking and
presentation. I find that, while search engines should at least be held responsible for their
practices regarding indexing, results ranking, delivering results from collections built by the
search engine provider itself and the presentation of search engine results pages; today’s
dominant player, Google, argues that there actually is no problem with these issues. Its basic
argument here is that “competition is one click away”, and, therefore, it should be treated like
any other smaller search engine company. I approach the topic from two standpoints: from a
technical standpoint, I will discuss techniques and algorithms from information retrieval and
how decisions made in the design of the algorithms influence what we as users get to see in
search engines. From a societal standpoint, I will discuss what biased search engines mean for
knowledge acquisition in society and how we can overcome today’s unwanted search
In this chapter, I discuss Google’s role as the dominant search engine on the market and
responsibilities that could derive from this market position. There are many responsibilities
that could be discussed in the context of Google (e.g., whether it has responsibilities deriving
from it collecting its users’ query data), or Online Service Providers in general (see Taddeo &
Floridi 2015). I will focus on the results Google provides. I will discuss these results
concerning fairness and biases.
First of all, a search engine in the context of this chapter is defined as a computer system that
collects content distributed over the Web through crawling, orders the results to a query by
machine-determined relevance, and makes these results available to its users through a user
There is a vast body of research on techniques and technologies to improve search engines, on
measuring the quality of results of search engines, on the behaviour of search engine users,
and on the role search engines play for knowledge acquisition in society. This research is
embedded in the wider context of research on the role of algorithms in knowledge acquisition
and search engines as socio-technical systems. It is important to understand the decisions
made by search engines through their algorithms, as the algorithmic approach to finding
information can be seen as “a particular knowledge logic, one built on specific presumptions
about what knowledge is and how one should identify its most relevant components. That we
are now turning to algorithms to identify what we need to know is as momentous as having
relied on credentialed experts, the scientific method, common sense, or the word of God"
(Gillespie 2014, p. 168).
The main argument brought forward in this chapter is that every search engine produces
biased results in some way, resulting from Web crawling, indexing, and results ranking. As
there is no perfect or correct results set or ranking, search engine results are always a product
of the algorithmic interpretation of the Web’s content by the given search engine.
Nevertheless, a search engine can still provide fair results when there is no preferential
treatment of information objects, neither in the process of indexing nor in the process of
The remainder of this chapter is structured as follows: first, I will elaborate on Google’s role
as the world’s dominant search engine and how Google as a company sees its responsibility
for providing fair and unbiased results. Then, I will define the central concepts used in this
chapter, namely fair results and unbiased results. I will further discuss the related concepts. I
will then focus on the search engines’ databases (the indexes) and show how already in
building the index, search engines make decisions on which results they will later be able to
produce. Then, I will focus on what I call the “algorithmic interpretation of the Web’s
contents” and how different forms of interpretation shape the results a user gets to see when
using a given search engine. After that, I will discuss responsibility issues related to indexing
and ranking (or, more general, producing results). I will conclude the chapter with a summary
and some suggestions for further research.
First of all, the importance of search engines for finding information could not be
overestimated. Not only are fully-automated search engines like Google the dominant means
for finding information on the Web and have made all other approaches to finding content on
the Web (like Web directories) nearly obsolete, but more and more information is searched on
the Web nowadays instead of other sources outside the Web. While other information sources
like social networking sites are sometimes seen as competitors to search engines, as users are
directed to information objects through messages displayed there, they do not qualify for ad
hoc searches, i.e. where a user actually queries an information system in order to find
information objects related to his or her information need. Furthermore, when looking at the
query volume that search engines process (cf. “Stats: comScore” 2015), we find that search
engines not only respond to billions of queries per day, but the query volume is nowhere near
Nearly everybody who uses the Internet also uses search engines (Purcell, Brenner, and Raine
2012). Searching for information is one of the most popular activities on the Internet. On
average, European users issue 138 queries per month (comScore 2013). Google’s market
share is 86% in Europe (comScore 2013) – including eastern European countries, where
Yandex has a large market share – with many countries reporting Google’s market share well
over 90%. Users predominantly relying on one search engine leads to certain problems
regarding bias and fairness or at least increases problems resulting from biases inherent in
every search engine and from search engine provider’s decisions on the fairness of the results
and their presentation.
When looking at public statements made by Google, we find that this search engine has a
clear view on what its position on the market is and how it should deal with results ranking
and transparency related to the rankings. This position can be summarised as follows:
1. There is competition on the search market, and users can decide to use another search
engine without any problem. In the words of Amit Singhal, Senior Vice President Search
at Google: “the competition is only one click away. […] Using Google is a choice—and
there are lots of other choices available to you for getting information: other general-
interest search engines, specialized search engines, direct navigation to websites, mobile
applications, social networks, and more”1.
2. Google generates its results purely through its algorithms, and does not manually
interfere with results generated by these: “No discussion of Google's ranking would be
complete without asking the common - but misguided! :) - question: "Does Google
manually edit its results?" Let me just answer that with our third philosophy: no manual
intervention”.2 And he gives the following reason: “If we messed with results in a way
that didn't serve our users' interests, they would and should simply go elsewhere”.3
3. Google does not treat its own content preferentially: “People often ask how we rank our
"own" content, like maps, news or images. In the case of images or news, it's not actually
Google's content, but rather snippets and links to content offered by publishers. We're
merely grouping particular types of content together to make things easier for users. In
other cases, we might show you a Google Map when you search for an address. But our
users expect that, and we make a point of including competing map services in our search
4. Google is as transparent as possible on how its results are generated: “Be
transparent. We share more information about how our rankings work than any other
search engine, through our Webmaster Central site, blog, diagnostic tools, support forum,
and YouTube”.5 In another blog post, Singhal says that, “Google's search algorithm is
actually one of the world's worst kept secrets”.6 On the other hand, Udi Manber, then Vice
President Engineering, Search Quality, said in a blog post: “For something that is used so
often by so many people, surprisingly little is known about ranking at Google. This is
entirely our fault, and it is by design. We are, to be honest, quite secretive about what we
do. There are two reasons for it: competition and abuse”.7
Much has been written about Google’s actual practices, and so I will only summarise some of
the findings on Google’s practices regarding these statements.
Considering the competition on the search engine market (cf. Lewandowski, forthcoming),
we can firstly see that Google has been the dominant player on the market for years and that it
only faces competition in general-interest search engines from Microsoft’s Bing. Either other
search engines do not have a large enough index to compete with these two engines or they do
not provide their own database at all but instead rely on partnerships with either Google or
Bing. Regarding vertical search engines, these are to a large degree accessed through general-
interest search engines and thus rely on being ranked high in Google.
Google says that it does not manually interfere with its results. While it is true that there is no
simple manipulation in the way that Google would manually adjust the organic results for
certain queries (although there has been some doubt in the past; see, Edelman 2010), it does
exclude certain results due to law or deliberate choice, or it penalizes information objects for
not confirming to its self-defined rules. As these information objects are not considered for
ranking, they will not have the chance to be discovered through a Google search. Examples
for the exclusion of documents from being found include:
Exclusion of SPAM documents: Some documents are excluded from Google’s index due
to being irrelevant and classified as SPAM. While it is necessary for any search engine to
take action against spamming, the criteria to qualify a document or a website as SPAM
are not transparent.
Penalties for gaming the system: Google reserves the right to penalise certain documents
or websites if it finds that the owners of these were trying to “game the system”, i.e.,
trying to achieve better rankings, e.g., by buying links to their documents. Such penalties
have nothing to do with the actual quality of the documents’ contents.
Deliberate choice on how to process “Right to Be Forgotten” (RTBF) queries: There is no
clearly defined and transparent process on when documents are not shown due to RTBF
requests. One has to admit, however, that the RTBF is relatively new and that it may take
some time to establish such a process.
Exclusion of certain sites from vertical searches: Some vertical search engines are built
through focussed crawling, i.e., a process where only content from pre-selected sources is
considered for inclusion in the search index. This approach is fundamentally different
from Web indexing in general, where a search engine basically crawls all the contents
from the Web without humans manually excluding some websites.8 An example of a
vertical search engine that uses focussed crawling is Google News, where humans pre-
8 It should be noted, however, that there are certain quality thresholds for the inclusion of websites,
although whether a website is below such a threshold is determined automatically.
select news sources that are then regularly crawled for new content. This means that if a
website is not considered a news source by Google, its documents do not have a chance to
be found through a news search (or in the news box in Google Web search).
A further example where Google does not exactly interfere with the results themselves, but
with the process that leads a user to the results, is its interference with the autocomplete
function. While Google claims that query suggestions are solely based on other users’ past
queries and determined automatically9, there are examples where one can easily see that for
certain queries, humans at Google have decided that no suggestions should be made or that
suggestions should be filtered (Diakopoulos 2013).
These examples show that Google does not function solely on algorithms and that there are
human decisions, not only in the design of the algorithms but also in maintaining the search
engine. It is a myth that Google does not manually interfere with the results. However, it is in
the interest of Google to prolong this myth because, otherwise, information providers whose
content is ranked low within Google’s results could argue for them to be ranked higher based
on an assumed better quality. Google will try not to raise any discussions on the actual quality
of its results (apart from it being produced by an algorithm that treats each document the
Regarding the question of whether Google gives its own content preferential treatment on its
Web search results pages (and therefore, using its dominant position in Web search to
promote its own content or the results from its vertical search engines, respectively), we can
see that giving these results a different (and more attractive) layout than results from
competitors alone constitutes preferential treatment. Users are not only attracted by the
position of a result but also by its graphical design. For instance, if a news box with a result
including an image is presented above the fold on a search engine results page, users will be
attracted to it to a large degree (see Lewandowski and Sünkler 2013). Therefore, the if-
question may be the wrong one. Instead, one should ask whether search engines have a moral
responsibility when it comes to their own content. I will discuss this further below.
This brief comparison of Google’s statements with its actual practices shows that Google
operates on statements that are at least in part contrary to their actual practices. One could
simply qualify these statements as public relations, but the point is that in many cases, even
scholars use these arguments when discussing search engines and the role of Google. Moving
away from current practices, in the next sections, I will define what fair and unbiased results
are and whether search engines are able to provide such results.
The Oxford English Dictionary gives several definitions for the term “fair”, depending on the
“Of conduct, actions, methods, arguments, etc.: free from bias, fraud, or injustice;
equitable; legitimate, valid, sound.”
“Of conditions, circumstances, etc.: providing an equal chance of success to all; not
unduly favourable or adverse to anyone.”
“Of remuneration, reward, or recompense: that adequately reflects the work done,
service rendered, or injury received.”10
For our purposes, we can define fair search results as results that are produced in a way where
every document on the Web is treated in the same way by the search engine and, therefore,
has the same chance of being found and ranked by that search engine and that there are no
human interferences with algorithmic decisions on crawling, indexing and ranking.
Bias, then, is,
“An inclination, leaning, tendency, bent; a preponderating disposition or propensity;
predisposition towards; predilection; prejudice.”11
Search engine bias is the tendency of a search engine to prefer certain results through the
assumptions inherent in its algorithms. This means that every search engine is biased, as it is
impossible to design algorithms without human assumptions. Therefore, search engine bias
does not mean that search results are deliberately manipulated by the search engine vendor
but simply that results are ordered in a certain way that is determined by assumptions of what
constitutes a good or relevant result in response to queries. It is even at the core of every idea
of ranking, based on certain technically mediated assumptions, that certain items are preferred
over others.
Yet it should be mentioned that there are other definitions of search engine bias that do not
define search engines as biased per se but focus on the deliberate preference for certain results
(as in the case of Google discussed above, when it favours its own content over content from
its competitors. Tavani (2012) summarises the three concerns underlying the definitions of
search engine bias:
“(1) search-engine technology is not neutral, but instead has embedded features in its
design that favor some values over others; (2) major search engines systematically
favor some sites (and some kind of sites) over others in the lists of results they return
in response to user search queries; and (3) search algorithms do not use objective
criteria in generating their lists of results for search queries." (Tavani 2012)
So, is the question on whether Google is responsible for fair and biased results put the wrong
way? If there is no such thing as an unbiased search engine, Google cannot be made
responsible for being biased. In my opinion, the bias inherent in search algorithms in fact
leads to the severe need for more search engines rather than the demand for Google to reduce
or even erase the bias in its search results. I argued for a public search infrastructure (as
opposed to alternative search engines) elsewhere (Lewandowski 2014a) and see this as the
only solution for dealing with the problem of every search engine being biased by design.
Even if we consider that every search engine is biased by design, Google could still be made
responsible for providing fair results. Fair in this sense would be that every information object
on the Web has the same chance of being included in Google’s database (index) and that
every information object in the index has the same chance of being found, solely on the basis
of algorithms that treats every information object in the database in the same way.
However, there is an important restriction to this: As search engines not only provide textual
documents, but also images, videos etc., they need to treat these different kinds of information
objects differently, if only for their different properties. For instance, it is not possible to treat
images to be found in the same way by the same algorithm as textual documents. Therefore, it
is misleading to speak of the index of a search engine, as search engines have multiple
indexes for each type of content.
That being said, we must distinguish between fairness in including documents in the indexes
and fairness in ranking the results in the indexes in response to a query.
Search engines make decisions about what to include in their indexes and how often to refresh
already known documents. As there is an overwhelming amount of mostly automatically
generated content that can be considered SPAM, search engines require technical filters that
allow them to not even consider such documents in the crawling/indexing process, which can
lead to unwanted exclusion of documents from the search engine. In addition to filtering
SPAM content, search engines also apply filters based on often country-specific laws
(e.g., the “right to be forgotten” in the European Union) and based on the deliberate choices
of the search engine provider (mainly in the context of self-defined rules for the protection of
children and young persons and takedown notices from copyright holders).
As the Web is of an immense size and continuously changes (Ntoulas, Cho & Olston, 2004),
building and maintaining a comprehensive index is a huge challenge (Patterson 2004).
Related to these challenges are issues with comprehensiveness, freshness and the deliberate
choice of search engines to exclude certain documents from their indexes. The latter can
either be deciding not to index certain documents at all or excluding documents after
indexing, i.e., not making them available to users in certain countries or regions.
Issues with comprehensiveness
The first and arguably the most important issue with comprehensive search engine indexes is
the size of the Web. While we know that the Web consists of many billions of documents
(some years ago, Google even claimed that it knew more than one trillion different URLs12),
we do not know the exact number, as there is no central registration for URLs on the Web.
The best estimates we have are actually from numbers derived through Web crawling (i.e.,
finding content on the Web through following links), which on a large scale is mainly done by
search engines.
Some years ago, search engine companies stopped reporting index sizes. This can be seen as a
consequent move, as index sizes do not say much about the quality of a search engine. As
there are vast amounts of documents on the Web that a search engine will not want to index
(such as copies of the ever-same content and SPAM pages), a search engine having a lot of
these documents in its index would surely increase its index size but for no one’s benefit.
Then, there is the problem with defining what a document on the Web actually is. One could
argue that everything that has an URL should be considered to be a document. However, as
such documents can be easily created automatically, and can be built by combining elements
from other documents, a lot of documents without any benefit could be (and are) built. This
does not have to do with spamming search engines. Consider, for instance, blogs where
different kinds of overview pages (such as teasers for all articles from a certain month, teasers
for articles tagged with a certain keyword etc.) are generated. We can question whether a
search engine should index all these “documents”.
However, while this seems to be a purely technical problem, it still comprises decisions about
what is worth indexing, and there is no guarantee that no potentially relevant document will
slip through. Presumably due to the structure of the Web, certain content, e.g., from certain
countries, is not as well represented as content from other countries (cf. Vaughan and Zhang
Furthermore, search engines have technical and financial limitations regarding index sizes:
Even if a search engine wanted to build a complete index of the Web, it would still face
limitations due to its technical possibilities and financial resources. No search engine is able
to index the Web in its entirety. The problem arising from that, however, may not be the lack
of completeness but the lack of transparency regarding the criteria that lead a search engine to
index certain documents and exclude others.
Issues with freshness
Apart from the issues of building a comprehensive index of the Web, search engines must
also keep up with the ever-changing Web. New documents are created, existing documents
are changed and documents are deleted. The issue related to these Web dynamics is twofold:
Firstly, search engines have to make sure to index documents afresh and, secondly, provide
fresh results through ranking. It would be a bad idea for a search engine to present a user with
a result description that points him or her to a page that no longer exists.
The issue with freshness is that no search engine can keep all the documents in its index
current. On the one hand, no search engine could afford to crawl every document every
second. On the other hand, even if a search engine would be able to do so, this would account
for too much bandwidth and would send too many requests to Web servers. The approach that
search engines take is to decide which documents to revisit when based on popularity and on
the refreshment rate of each document in the past (Risvik and Michelsen 2002). On the one
hand, this leads to a technically feasible solution. On the other hand, decisions about freshness
(i.e., which documents to index more frequently) may lead to fairness issues. Preferring
popular and/or often refreshed content is a decision that could lead to the oppression of other
documents in the results sets.
Issues with deliberate choices made by the search engines
Search engine providers also make deliberate choices about documents to exclude from their
indexes, sometimes not actually excluding them, but removing them from the results in
certain regions or countries. The prime example for this is the “Right to Be Forgotten”
(RTBF) in European legislation. Persons can request for certain results to be removed from
search results if these results refer to the person’s past that is no longer occurring. A problem
with the RTBF may lie in it not being precise as to when such data should be removed.
However, the issue with search engines and the RTBF lies in that the procedure for having
content removed is not transparent. While Google provides information about how many
requests it received and how it decided as well as established an advisory council on the topic,
there are still no transparent rules on how these requests are treated. So, some documents may
have been removed even though they do not fall under the RTBF, while others may have been
removed without actually falling under the RTBF.
Very similar is the case of takedown notices by copyright holders. Here also, Google must
process a large number of requests, but this time, it mainly processes them automatically. This
could lead to documents taken down erroneously, simply due to the sheer volume of these
requests and standard procedures to treat them (Karaganis and Urban 2015).
A third area of concern is the protection of children and young persons. At the request of
authorities (e.g., the Bundesprüfstelle für jugendgefährdende Medien in Germany), Google
removes websites from its search results no matter if a child or adult is searching for that
content. Furthermore, adjusting rankings in a way that prefers non-offensive content is also a
decision on what constitutes a document potentially relevant in response to a query (see the
section in rankings below).
From this brief discussion of the RTBF, takedown notices and the protection of minors, we
can see how Google and other search engines make decisions about which documents to
include in their results sets that are opaque to their users. In contrast to the technical and
financial issues related to index comprehensiveness and index freshness, these decisions are,
even though they are founded on law, deliberate decisions made by the search engines and,
therefore, moral decisions. Mainly due to technical reasons, we cannot expect a search engine
to provide a complete and fresh copy of the Web. However, what we could expect from a
search engine is to make transparent how its index is built, what is left out and for what
For every query, a search engine – through its algorithms – presents certain results in a certain
way. We can call this an “algorithmic interpretation” of the Web’s content (Lewandowski
2015a), and users tend to follow this interpretation uncritically (e.g., Pan et al. 2007; Purcell,
Brenner, and Raine 2012). Algorithmic interpretation does not only consider the ranking of
the results lists (Pan et al. 2007) but also the positioning of elements on the search engine
results pages (Lewandowski and Sünkler 2013; Liu et al. 2015), the correctness of results
(e.g., (White and Horvitz 2009), the labelling of advertisements and the diversity within the
top results (Denecke 2012).
Due to search engines presenting different kinds of results from different collections within
search engine results pages (so-called “Universal Search”), the ranking of these results is not
only list-based anymore but consists of at least three different ranking functions: (1) Ranking
of the results lists from the Web index, (2) ranking of vertical results within collections such
as news, images, etc. and (3) ranking of Universal Search containers (i.e., boxes presenting
top results from vertical search engines) within the general SERP or the list of ranked Web
search results, respectively.
Ranking within collections (whether considering the Web collection or vertical collections) is
based on groups of ranking factors as follows (cf. Lewandowski 2015b, p. 91-92):
(1) Text statistics: This is where the query is matched with the representation of the
information objects and statistics are applied to rank documents according to their fit
with the query. As queries on the Web are usually very short and there is no standard
quality for documents found on the Web (opposed to documents in a curated database,
like the electronic archive of a newspaper), text statistics alone are not sufficient for
ranking Web documents. They must be accompanied by so-called quality factors.
(2) Popularity: Link-based ranking algorithms (e.g., PageRank) as well as click-based
algorithms assign popularity scores to documents and use these scores for a ranking
based on the assumption that what has been useful to others will also be useful to a
given user. Popularity-based algorithms can either take all users into account or only a
certain group of users.
(3) Freshness: As new content is produced in large amounts on the Web, the freshness of
information objects is also considered in ranking algorithms.
(4) Locality: Information objects are matched to the geographical location of the user.
(5) Personalisation: Information objects are matched to the interests of an individual user,
mainly based on his past behaviour (e.g., queries entered, results viewed).
(6) Technical ranking factors: These factors are mainly used to determine how reliable
Web servers are in providing results to the user. As a search engine in most cases only
links to information objects from external sources, a user clicking on a result on the
search engine results page (SERP) will have to wait for the information object to be
produced by the server. Search engines take into account how fast a server is able to
process requests and how reliable it is (concerning downtimes). Technical ranking
factors are an interesting case, as they judge where an information object should be
displayed in a results set, not based on the assumed quality of the content of the
information object but rather on the convenience for the user to get to the information
To understand what types of results search engines prefer, it is important to consider that the
popularity group is considered one of the most important to determine the quality of
individual documents. This means that while search engines try to measure such things as
credibility, this can only be simulated through measuring popularity (cf. Lewandowski 2012).
The ranking of search results is often misinterpreted as either correct or wrong. I argue that
this is mainly due to users having success with navigational queries (where they search for a
certain website and the aim is to navigate to that website), with transactional queries where
they already have a certain website in mind and with informational queries where they either
only search for trivia that can be found on a multitude of websites or they already have a
website providing that information in mind (such as Wikipedia). Based on their experience of
success for these queries, they assume that their favourite search engine will also produce
“correct” results for other types of – mainly informational – queries where there may be many
relevant results, and, therefore, no single correct ordering of these results (Lewandowski
The basis for algorithmic interpretation is the assumptions that search engine engineers put
into these algorithms. Little research has been conducted on the motivations, beliefs and
assumptions of this group of people. However, the research already done shows that engineers
(and other search engine employees) see search engines as rather purely technical systems,
and they conform to a capitalistic way of looking at them (van Couvering 2007; Mager 2012).
The effects of algorithmic interpretation can be seen on different levels. Most obviously,
algorithmic interpretation affects the ranking of the organic results, i.e., the results produced
from the basic Web index. Here, every information object included in the index is treated in
the same way by the same ranking algorithms, i.e., the results are ranked in a fair way,
although not without bias towards the information objects that fulfil assumptions inherent in
the ranking algorithms. The effect of algorithmic interpretation for organic results can best be
seen when considering drastic examples like the infamous website
(Piper 2000) that, even in 2015, still ranks very well in Google, and the results produced by
Google in response to queries related to race and gender (Noble 2013; Noble 2012). These
examples also show that a good ranking position in Google does not necessarily conform with
a result being credible or trustworthy (Lewandowski 2012).
Applying certain algorithms can also lead to search engines presenting one side of an
argument or only the results of a certain type or tendency. Some algorithms not only try to
rank results according to relevance but also mix different result types within the top results to
achieve diversity (Giunchiglia et al. 2009).
Algorithmic interpretation also affects the composition of results pages from different indexes
(“Universal Search”). For instance, in addition to results from the Web index, results from
vertical indexes like news, images and videos can be included in the results pages. This leads
to a manifold ranking: Firstly, the results within each vertical index and the main Web index
must be ranked. Then, the top results from these indexes must be incorporated into one search
engine results page, where results from the vertical searches are to be positioned, and which is
another form of ranking. As the presentation of results on the SERP (and even more
importantly, in the area “above the fold” of the SERP) heavily influences users’ decision on
what results to select, search engines are able to lead users to certain types of results merely
through results presentation. An important example for this is Google presenting results from
its own vertical search engines (such as Google News, Google Scholar and Google Maps) as
attractive boxes within the SERP, which then preferably leads to users clicking on them
(Lewandowski and Sünkler 2013).
The personalisation of search results is another form of algorithmic interpretation, this time
also related to a user’s preferences and interests. Results are then produced according to these
assumed preferences, mostly without the user knowing what data about him or her is actually
collected and how the use of this data affects his or her results. In extreme cases,
personalisation can lead to what Eli Pariser termed the “filter bubble” (Pariser 2011), where
information objects presenting contradicting views and beliefs from the users are oppressed,
and the users only receive results confirming their already established opinions.
Last but not least, search engines present text-based, contextual advertisements on the SERPs.
These can be seen as a distinct type of result, and the often-used term “sponsored link” may
describe them best: They are a type of result but different from organic results in that they are
paid for. Studies lead to the conclusion that users are not able to properly distinguish between
organic results and advertisements (Bundesverband Digitale Wirtschaft 2009; Filistrucchi et
al. 2012), and, in the case of Google, ad labelling has not become clearer in recent years
(Edelman 2014).
It should also be mentioned that apart from the assumptions underlying search engines’
algorithms, there is also external influence on the results, namely in the form of search engine
optimisation (SEO). The aim of SEO is to optimise information objects in a way that leads to
optimal findability through search engines, mainly Google. Search engine optimisation has
grown to be a billion-dollar industry, and, at least for queries assumed to have a commercial
intent, it will be difficult to find top results in Google that have not been optimised.
While it is common knowledge in the industry and academia that search results are heavily
influenced by search engine optimisers, users’ knowledge about these practices seems to be
low. Furthermore, we see that users generally know little about search engines’ workings in
general (see, e.g., Purcell, Brenner, and Raine 2012). They often have misconceptions about
how a search engine actually works (e.g., Hendry and Efthimiadis 2008), they are not good at
searching and they lack knowledge about search engines’ ways of making money. On the
other hand, they trust in Google’s rankings when it comes to results quality (Keane, O’Brien,
and Smyth 2008; Bar-Ilan et al. 2009), sometimes even more than their own judgments (Pan
et al. 2007).
As we can see from the discussion above, there are multiple areas where we can ask for the
responsibility of search engines, especially Google as the dominant player on the market.
While we cannot expect Google to provide unbiased results, since we can see that search
engine rankings are biased per se, we can expect Google to give every information object in
its index a fair chance of being ranked in response to a query. “Fair results” here would mean
that every information object is treated in the same way. This leads to the conclusion that
Universal Search is an unfair treatment to certain results as soon as Google presents results
from its own offerings preferentially.
We can also demand for Google to be transparent about its practices, be it the sources its
vertical results come from and why they are given preferential treatment or the labelling of its
advertisements. While information on both can be found on Google’s help pages, we can see
in practice that users do not understand or are not interested in – the workings behind the
composition of search engine results pages. This may be seen as the users’ own fault, but in
its current practices, Google at least accepts that users are deceived about the true reasons for
the display of certain results (types).
We cannot expect a search engine to provide fair and unbiased results. Every search engine is
per definition biased in that it is not able to provide sets of correct results, as separated from
“incorrect” results. Correct results can only be provided for a subset of queries, mostly
navigational queries (Broder 2002; Lewandowski 2014b). With informational queries, search
engines can provide relevant results. However, as relevance always refers to a given user in a
given context (Saracevic 2015), a search engine can only make more or less good guesses to
what a user may find relevant in his or her current context.
Before producing a ranked results set in response to a query, a search engine must build an
index from content found on the Web. A problem here is the size of the Web and its
dynamics. Due to the vast amount of information objects on the Web, search engines produce
more results than a user is able to consider for most queries. This means that users must trust
the ranking provided by the search engine. However, this does not mean that there may be no
additional relevant results (or even results being more relevant to a given user in a given
context) on lower positions of the results lists.
So, even if Google treated all information objects in a fair manner, users would still see only a
fraction of the relevant results available. And as all ranking algorithms organise results in a
certain order based on assumptions about what is relevant to users, results from different
search engines could differ considerably (or may not even overlap at all) without the results
from one search engine being less relevant than the others.
This leads to the conclusion that to release us from only one (or considering the current
competition, from a few) of many possible algorithmic interpretations of the Web’s content,
we need more search engines. With “more”, I do not mean just one or two more search
engines but a considerable amount of them. One way to achieve this is to view Web indexing
as a public service to be provided for the good of all and then have services built upon that
infrastructure (Lewandowski 2014a).
Further research is needed on the actual differences of the algorithmic interpretation by
different search engines. While some empirical studies already determined overlaps between
search engine results (e.g., Spink, Jansen, Blakely and Koshman, 2006), they do not deal with
the actual content found, but from a technical viewpoint with URL overlap only. Furthermore,
more research is needed on the types of results and the beliefs reproduced through search
engine algorithms.
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... The fundamental question is thus no longer whether search engine providers behave in a "neutral" manner but what responsibilities arise from their role as dominant technical information brokers in society. Until now, search engine providers have argued on a purely technical level and rejected any further responsibility for their services (see Lewandowski, 2017). ...
... The effects that an interest-driven presentation of universal search results Lewandowski & Sünkler, 2013) The fact remains that a ranking will never produce a neutral result list because the basic assumption of every ranking is that a set of results is ranked according to predefined criteria (Grimmelmann, 2010;Lewandowski, 2017). And no matter how often the myth that the ranking is only determined by machines (or algorithms) is propagated: Algorithms are still based on human valuations, which decide which documents will later appear at the top of the result lists and which will remain invisible to the user. ...
In this chapter, we focus on the societal role of search engines. What role do search engines play in knowledge acquisition today, and what role should they play? We start discussing this by providing background on search engine providers’ self-interests and then focus on search engine bias. Next, we categorize existing biases and show how they influence results. Based on this, we discuss how search engines could provide a fair result presentation and, more generally, how fair search could be made possible.
... Also the overdominance of search functionality in many information environments has restricted support for other important forms of information acquisition, such as serendipitous information encountering (Erdelez & Makri, 2020) and creative 'inspiration hunting. . Furthermore, search results and recommendations has also been found to unfairly favor some types of content due to algorithmic bias (Lewandowski, 2017;Ferraro et al., 2021). ...
... Rather than acting as a great leveler by making information acquisition effective, efficient and enjoyable for all, search is not always equitable (Lewandowski, 2017). We frame this issue as inequity rather than inequality as it extends beyond unequal information distribution; it involves unfairly favoring some types of user, task or content over others. ...
The ubiquitous search box promised to democratize knowledge access by making information universally accessible. But while many search engines cater well for certain user groups, information tasks and content types, they cater poorly for others. Poorly‐served users include those with certain types of impairment (e.g. dyslexia), and weakly‐supported tasks include highly exploratory goals, where it can be difficult to express information needed as a query. Furthermore, the overdominance of search functionality in many information environments has restricted support for other important forms of information acquisition, such as serendipitous information encountering and creative “inspiration hunting.” Search results and recommendations can also promote certain types of content due to algorithmic bias. Rather than act as a great leveler by making information acquisition effective, efficient and enjoyable for all, search engines often unfairly favor some types of user, task or content over others. In short, search is not always equitable. This panel discussion will elucidate the inequity of search as an information acquisition paradigm from multiple perspectives and propose design principles to ensure more equitable information acquisition.
... B. die großen Suchmaschinenanbieter vor allem aus ökonomischen Gründen Einfluss auf das Ranking der Suchergebnisse nehmen. Maßstab des Normalzustands sollten hier "fair and unbiased results" (im Titel von) (Lewandowski 2017) sein. ...
... Furthermore, these two factors are in complex and continuous interaction, where the user chooses how to conduct a search and which content to consume, while the information they consume may affect their health knowledge (Moreland et al., 2015), beliefs about symptoms (Marcu et al., 2019), emotional reaction (Medlock et al., 2015), and willingness to continue searching (Hämeen-Anttila et al., 2014). Similarly, search engines tailor and personalize search results based on the previous actions of a given user (Lewandowski, 2017), perhaps making it even more likely for an anxious user to come across frightening content over time. ...
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Cyberchondria is defined as excessive online health research followed by distress. Theoretical models of cyberchondria suggest that it can be influenced by both characteristics of the internet (content, information ranking, amount and quality of information) and individual vulnerability factors (general health anxiety or COVID-19 fear). In order to simultaneously explore the role of both factors, an innovative search engine software (Foogle) was developed and used in the present study that enables manipulation of the presented content and content ranking while also recording users' online behavior. A total of 36 participants with high and 28 participants with low COVID-19 fear searched for the long-term health effects of COVID-19 using Foogle. They were presented with search engine results that rank long-term health effects of COVID-19 from more to less severe or vice versa (randomized). Results revealed that participants who were presented with articles describing more to less severe long-term COVID-19 health effects accessed articles with a higher mean severity index. In general, participants spent more time on articles depicting more severe content. Participants with high COVID-19 fear felt more anxious post-search than those with low COVID-19 fear and expressed a greater wish to continue searching.
... We advocate for information literacy education that teaches people the best ways to use search engines and how external actors can influence the results these engines provide. However, one should keep in mind that such interventions place responsibility in the hands of users while ignoring the responsibility of the search engine providers (Lewandowski, 2017a). The processes that lead users to select a particular result are complex and lie not only with the user but also with the search engine provider and external parties aiming to influence the results. ...
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This research focuses on what users know about search engine optimization (SEO) and how well they can identify results that have potentially been influenced by SEO. We conducted an online survey with a sample representative of the German online population (N = 2,012). We found that 43% of users assume a better ranking can be achieved without paying money to Google. This is in stark contrast to the possibility of influence through paid advertisements, which 79% of internet users are aware of. However, only 29.2% know how ads differ from organic results. The term "search engine optimization" is known to 8.9% of users but 14.5% can correctly name at least one SEO tactic. Success in labelling results that can be influenced through SEO varies by search engine result page (SERP) complexity and devices: participants achieved higher success rates on SERPs with simple structures than on the more complex SERPs. SEO results were identified better on the small screen than on the large screen. 59.2% assumed that SEO has a (very) strong impact on rankings. SEO is more often perceived as positive (75.2%) than negative (68.4%). The insights from this study have implications for search engine providers, regulators, and information literacy.
... Likewise, search results are also influenced by advertising as part of the business of search engines (Rieder and Sire, 2014). In this sense, the delivery of results through a content ranking, implements a biased model, according to which the algorithm determines the priority of some content over others (Lewandowski, 2017, Rieder and Sire, 2014, Jiang 2014a2014b), directly influencing the access to information by users. Another factor to consider is the media influence of each country, as it would also influence the decision making of the search algorithm (Cano-Orón, 2019 p. 98). ...
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... They tend to melt into the background of our practices, and the constitutive role of search engines for society and everyday life is therefore often even more difficult to recognize and challenge. The results an individual gets from a Google search are often considered natural, despite their obvious embeddedness in society's value systems (e.g., Hillis et al., 2012;Lewandowski, 2017;Mager, 2012;Noble, 2018). ...
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This opinion piece takes Google's response to the so‐called COVID‐19 infodemic, as a starting point to argue for the need to consider societal relevance as a complement to other types of relevance. The authors maintain that if information science wants to be a discipline at the forefront of research on relevance, search engines, and their use, then the information science research community needs to address itself to the challenges and conditions that commercial search engines create in. The article concludes with a tentative list of related research topics.
This research focuses on what users know about search engine optimization (SEO) and how well they can identify results that have potentially been influenced by SEO. We conducted an online survey with a sample representative of the German online population (N = 2,012). We found that 43% of users assume a better ranking can be achieved without paying money to Google. This is in stark contrast to the possibility of influence through paid advertisements, which 79% of internet users are aware of. However, only 29.2% know how ads differ from organic results. The term ‘search engine optimization’ is known to 8.9% of users but 14.5% can correctly name at least one SEO tactic. Success in labelling results that can be influenced through SEO varies by search engine result page (SERP) complexity and devices: participants achieved higher success rates on SERPs with simple structures than on the more complex SERPs. SEO results were identified better on the small screen than on the large screen. 59.2% assumed that SEO has a (very) strong impact on rankings. SEO is more often perceived as positive (75.2%) than negative (68.4%). The insights from this study have implications for search engine providers, regulators, and information literacy.
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The search neutrality debate stems from content or service providers complaining about being discriminated and ranked unfairly low by search engines, raising the need for methodologies and tools to verify bias in search engine rankings. For that purpose, we propose in this paper a simple yet effective framework based on the comparison of the results provided by several search engines, and build the corresponding tool to carry out a campaign of tests. The main objectives are to develop an interpretable model of search engine behaviors and to design statistical tests pointing out suspicious instances as possible bias, without knowing the detailed ranking algorithms implemented by search engines. Our approach consists in reasoning in terms of the visibility that search engines give webpages when ranking them among their results; different types of possible bias can then be detected using statistical tests for outlier detection. We apply this methodology to a test campaign over the most searched terms, which highlights some similarities and discrepancies among search engines, and possible instances of bias. Our approach can be of interest to regulators or any actor in the Internet, and is directly applicable to any search term through a publicly-available tool performing extensive comparisons and bias investigations, and offering two (bias-reducing) meta rankings.
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After the World Health Organization declared the spread of the novel coronavirus (COVID-19) a global pandemic in March 2020, they cautioned of another outbreak: an “infodemic.” This study examines how online search engines are influencing the global spread of immunization information about COVID-19. It aims to address the various ways in which search technology is shaping users’ perceptions of the pandemic and to measure the credibility of the sources they provide.
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Suchmaschinen spielen eine zentrale Rolle für die Auffindbarkeit von Inhalten im Web; andere Zugänge haben hinsichtlich des vermittelten Traffics nur eine untergeordnete Rolle. In diesem Kapitel wird die Bedeutung der Suchmaschinen für die Auffindbarkeit von Inhalten, für die Vermittlung von Traffic und für die Online-Werbung beschrieben. Darauf aufbauend wird die aktuelle Situation auf dem Suchmaschinenmarkt betrachtet und in Bezug zu den von den Suchmaschinenbetreibern verfolgten Geschäftsmodellen gesetzt. Daraus ergeben sich Fragen der Marktmacht und ihrer Ausnutzung. Auswege aus der aktuellen Situation auf dem Suchmaschinenmarkt werden diskutiert, vor allem in Hinblick auf Strategien zur Gewinnung von Marktanteilen und Vorschlägen zu einer gesellschaftlich wünschenswerten Umgestaltung des Suchmaschinenmarkts.
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Online service providers (OSPs)-such as AOL, Facebook, Google, Microsoft, and Twitter-significantly shape the informational environment (infosphere) and influence users' experiences and interactions within it. There is a general agreement on the centrality of OSPs in information societies, but little consensus about what principles should shape their moral responsibilities and practices. In this article, we analyse the main contributions to the debate on the moral responsibilities of OSPs. By endorsing the method of the levels of abstract (LoAs), we first analyse the moral responsibilities of OSPs in the web (LoAIN). These concern the management of online information, which includes information filtering, Internet censorship, the circulation of harmful content, and the implementation and fostering of human rights (including privacy). We then consider the moral responsibilities ascribed to OSPs on the web (LoAON) and focus on the existing legal regulation of access to users' data. The overall analysis provides an overview of the current state of the debate and highlights two main results. First, topics related to OSPs' public role-especially their gatekeeping function, their corporate social responsibilities, and their role in implementing and fostering human rights-have acquired increasing relevance in the specialised literature. Second, there is a lack of an ethical framework that can (a) define OSPs' responsibilities, and (b) provide the fundamental sharable principles necessary to guide OSPs' conduct within the multicultural and international context in which they operate. This article contributes to the ethical framework necessary to deal with (a) and (b) by endorsing a LoA enabling the definition of the responsibilities of OSPs with respect to the well-being of the infosphere and of the entities inhabiting it (LoAFor).
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Algorithms (particularly those embedded in search engines, social media platforms, recommendation systems, and information databases) play an increasingly important role in selecting what information is considered most relevant to us, a crucial feature of our participation in public life. As we have embraced computational tools as our primary media of expression, we are subjecting human discourse and knowledge to the procedural logics that undergird computation. What we need is an interrogation of algorithms as a key feature of our information ecosystem, and of the cultural forms emerging in their shadows, with a close attention to where and in what ways the introduction of algorithms into human knowledge practices may have political ramifications. This essay is a conceptual map to do just that. It proposes a sociological analysis that does not conceive of algorithms as abstract, technical achievements, but suggests how to unpack the warm human and institutional choices that lie behind them, to see how algorithms are called into being by, enlisted as part of, and negotiated around collective efforts to know and be known.
Das Buch betrachtet das Thema Suchmaschinen ausgehend von der täglichen Recherche und führt in die technischen Grundlagen, in Recherchetechniken sowie die gesellschaftlichen und wirtschaftlichen Bedingungen der Recherche im Web ein. Suchmaschinen sind heute die wichtigsten Werkzeuge, um an Informationen zu gelangen. Wir verwenden Suchmaschinen täglich, meist ohne weiter darüber nachzudenken. Doch wie funktionieren diese Suchwerkzeuge eigentlich genau? Neben einer ausführlichen Darstellung der in den bekannten Suchmaschinen verwendeten Rankingverfahren wird auch ausführlich auf das Nutzerverhalten eingegangen, das wiederum die Ergebnisdarstellung prägt. Dazu kommen grundlegende Betrachtungen des Suchmaschinenmarkts, der Bedeutung der Suchmaschinenoptimierung und der Rolle der Suchmaschinen als technische Informationsvermittler. Nicht zuletzt wird auch die Seite der Recherche betrachtet und gezeigt, wie man mit den bekannten Suchmaschinen effizient recherchieren kann. Das Buch verhilft allen, die mit Suchmaschinen recherchieren oder sich beruflich mit der Optimierung, Aufbereitung und Sichtbarmachung von Inhalten beschäftigen, zu einem umfassenden Verständnis der Ansätze, Stärken und Schwächen verschiedener Suchmaschinen und der ihnen zugrunde liegenden Technologien.
Conference Paper
Research in how users examine results on search engine result pages (SERPs) helps improve result ranking, advertisement placement, performance evaluation and search UI design. Although examination behavior on organic search results (also known as "ten blue links") has been well studied in existing works, there lacks a thorough investigation on how users examine SERPs with verticals. Considering the fact that a large fraction of SERPs are served with one or more verticals in the practical Web search scenario, it is of vital importance to understand the influence of vertical results on search examination behaviors. In this paper, we focus on five popular vertical types and try to study their influences on users' examination processes in both cases when they are relevant or irrelevant to the search queries. With examination behavior data collected with an eye-tracking device, we show the existence of vertical-aware user behavior effects including vertical attraction effect, examination cut-off effect in the presence of a relevant vertical, and examination spill-over effect in the presence of an irrelevant vertical. Furthermore, we are also among the first to systematically investigate the internal examination behavior within the vertical results. We believe that this work will promote our understanding of user interactions with federated search engines and bring benefit to the construction of search performance evaluations.
The study examined search engine coverage of websites across Countries and domains. Websites in four domains (commercial, educational, governmental, and organizational) from four countries (U.S., China, Singapore, and Taiwan) were randomly sampled by Custom-built Computer programs and then manually filtered for their suitability for the study. Representation of the 1,664 sampled sites in four major search engines (Google, Yahoo!, MSN, and Yahoo! China) was examined in terms of whether the site was covered and the number of pages indexed by the search engines. The study found that U.S. sites received higher coverage rates than their counterparts in other countries. The language of a site did riot affect the site's chance of being indexed by search engines. Sites that were more visible had a higher chance of being indexed, but this factor did riot seem to explain the differentiated coverage across countries. Yahoo! China provided better coverage of sites from China and surrounding regions than its global counterpart, Yahoo!, The poor coverage of Chinese commercial and governmental sites is noted and the implications are discussed in light of the tremendous development of the Web in China.
We seek to gain improved insight into how Web search engines should cope with the evolving Web, in an attempt to provide users with the most up-to-date results possible. For this purpose we collected weekly snapshots of some 150 Web sites over the course of one year, and measured the evolution of content and link structure. Our measurements focus on aspects of potential interest to search engine designers: the evolution of link structure over time, the rate of creation of new pages and new distinct content on the Web, and the rate of change of the content of existing pages under search-centric measures of degree of change. Our findings indicate a rapid turnover rate of Web pages, i.e., high rates of birth and death, coupled with an even higher rate of turnover in the hyperlinks that connect them. For pages that persist over time we found that, perhaps surprisingly, the degree of content shift as measured using TF.IDF cosine distance does not appear to be consistently correlated with the frequency of content updating. Despite this apparent noncorrelation, the rate of content shift of a given page is likely to remain consistent over time. That is, pages that change a great deal in one week will likely change by a similarly large degree in the following week. Conversely, pages that experience little change will continue to experience little change. We conclude the paper with a discussion of the potential implications of our results for the design of effective Web search engines.
Examining the conflicting claims involving the use of automated tools in copyright-related notice-and-takedown procedures.