The Politics of Search: A Decade Retrospective

Granka, Laura

doi:10.1080/01972243.2010.511560

Cited by 130 publications

(68 citation statements)

References 32 publications

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“…Disclosing the structure of these algorithms would facilitate ill-intentioned manipulations of search results (or 'gaming the system'), while not bringing any advantage to the average non-techsavvy user (Granka, 2010;. The commercial viability of data processors in many industries (e.g.…”

mentioning

confidence: 99%

The ethics of algorithms: Mapping the debate

et al. 2016

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In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms.

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mentioning

confidence: 99%

The ethics of algorithms: Mapping the debate

et al. 2016

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“…For example, Google did not offer Boolean searching when Eysenbach et al was published but it does at the time of writing, albeit with limitations. However, the main developments in web search engines have been moving away from complex searches where the user retains a degree of control, towards simple searches where the user increasingly relinquishes control to undisclosed algorithms which determine the relevancy and ranking of the webpages retrieved (Granka, 2010;Pariser, 2011). This is a challenge for information professionals with complex and detailed information needs, in that search strategy development is limited, frequent changes to algorithms compromise the reproducibility of searches, and bias is introduced in cases where the search history of the user informs the webpages which are retrieved (Briscoe, 2015).…”

Section: Pilot Study Resultsmentioning

confidence: 99%

Eysenbach, Tuische and Diepgen’s Evaluation of Web Searching for Identifying Unpublished Studies for Systematic Reviews: An Innovative Study Which is Still Relevant Today

Briscoe

2016

EBLIP

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2016 Briscoe. This is an Open Access article distributed under the terms of the Creative Commons-AttributionNoncommercial-Share Alike License 4.0 International (http://creativecommons.org/licenses/by-nc-sa/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly attributed, not used for commercial purposes, and, if transformed, the resulting work is redistributed under the same or similar license to this one. AbstractObjective -To consider whether web searching is a useful method for identifying unpublished studies for inclusion in systematic reviews.Design -Retrospective web searches using the AltaVista search engine were conducted to identify unpublished studies -specifically, clinical trials -for systematic reviews which did not use a web search engine. Setting -The

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“…The pros and cons of enforcing transparency in the algorithms used for generating search results have been examined in [17,18]. Even in the absence of total transparency, anomaly detection systems such as BiAD could be useful in identifying bias in search engines.…”

Section: Applicationsmentioning

confidence: 99%

“…To prove that T (A) does not exceed the right hand side of inequality (18), we consider the following cases: ), combining the results from the above three cases with inequality (18) gives that S(A) ≥ T (A) for m large enough. Therefore, the algorithm declares the correct hypothesis in round A if the number of ad recommendations to the n players until round A is at least γ(1 − δ)nA.…”

Section: Type II Errormentioning

confidence: 99%

Detecting Sponsored Recommendations

Krishnasamy

Sen

et al. 2015

SIGMETRICS Perform. Eval. Rev.

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With a vast number of items, web-pages, and news to choose from, online services and the customers both benefit tremendously from personalized recommender systems. Such systems however provide great opportunities for targeted advertisements, by displaying ads alongside genuine recommendations. We consider a biased recommendation system where such ads are displayed without any tags (disguised as genuine recommendations), rendering them indistinguishable to a single user. We ask whether it is possible for a small subset of collaborating users to detect such a bias. We propose an algorithm that can detect such a bias through statistical analysis on the collaborating users' feedback. The algorithm requires only binary information indicating whether a user was satisfied with each of the recommended item or not. This makes the algorithm widely appealing to real world issues such as identification of search engine bias and pharmaceutical lobbying. We prove that the proposed algorithm detects the bias with high probability for a broad class of recommendation systems when sufficient number of users provide feedback on sufficient number of recommendations. We provide extensive simulations with real data sets and practical recommender systems, which confirm the trade offs in the theoretical guarantees.

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The Politics of Search: A Decade Retrospective

Cited by 130 publications

References 32 publications

The ethics of algorithms: Mapping the debate

The ethics of algorithms: Mapping the debate

Eysenbach, Tuische and Diepgen’s Evaluation of Web Searching for Identifying Unpublished Studies for Systematic Reviews: An Innovative Study Which is Still Relevant Today

Detecting Sponsored Recommendations

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