Bias in algorithmic filtering and personalization

By Engin Bozdag. Ethics and Information Technology, 2013

581 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/abb054df8076453474879fe9d25830af5715ad1b

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In Information Systems

Citations in the corpus

Algorithmic Accountability and Public Reason (2017, background): “In this way, decisions based on machine learning algorithms might end up reinforcing underlying social inequalities (Kamiran & Calders, 2012; Bozdag, 2013; Diakopoulos, 2015; Sandvig, Hamilton, Karahalios, & Langbort, 2014; Barocas & Selbst, 2016).”
Algorithms (and the) everyday (2017, background): “More recently, in relation to algorithms and social media, Bozdag (2013) noted how Facebook prioritises popularity of posts or particular types of interactions in its ordering and inclusion of items in a user’s newsfeed, and the ways Twitter prioritises currency as an important ranking value: these ”
Towards Transparency by Design for Artificial Intelligence (2020, background): “However, such a conception is not suitable for learning algorithms (Mittelstadt et al. 2016; Bozdag 2013).”
Exposure diversity as a design principle for recommender systems (2018, background): “There are also commercial and strategic decisions behind the algorithms that determine which information we will see, which information is prioritized and which information is excluded (Bozdag, 2013; Foster, 2012; Schulz, Dreyer, & Hagemeier, 2012; Webster, 2010).”
Exploring Artist Gender Bias in Music Recommendation (2020, background): “The notion of bias has been extensively explored in the Information Retrieval domain [4, 5, 7, 11, 24].”
Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship (2018, background): “All these psychological mechanisms, together with other biases, such as, algorithmic filtering and personalization [9], are connected to the phenomenon of echo chambers.”
Algorithmic Accountability (2015, methodology): “Google’s Content ID is an example of an algorithm that makes classification decisions that feed into filtering decisions (“How Content ID Works” 2013).”
Recommender systems and their ethical challenges (2020, background): “…biases and “filter bubbles” that are damaging to the normal functioning of public debate, group deliberation, and democratic institutions more generally (Bozdag 2013; Bozdag and van den Hoven, 2015; Harambam et al. 2018; Helberger et al. 2016; Koene et al. 2015; Reviglio 2017; Zook et al. 2017).”
Ethical Implications and Accountability of Algorithms (2018, background): “Within the narrative of neutrality, arguments acknowledging a biased algorithmic decision emphasize that the bias is due to the many ways individuals remain involved in the algorithmic decisions (Bozdag 2013).”
Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development (2020, background): “These rankings often take demographic features (e.g., name, age, country, and education level) into account, and this can yield a disadvantage for some candidates (Bozdag 2013; Chen et al. 2018).”
Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity (2018, unknown): “Yet, algorithms are created by humans and their design choices regarding the sorting and ranking of items influence the output (Bozdag, 2013).”
Society-in-the-loop: programming the algorithmic social contract (2017, background): “Others raised concern over people unwittingly living in filter bubbles created by news recommendation algorithms [11, 57].”
"Algorithms ruin everything": #RIPTwitter, Folk Theories, and Resistance to Algorithmic Change in Social Media (2017, background): “Algorithmically-driven content curation systems such as the Facebook Newsfeed or Instagram Feed are an increasingly common feature of social media platforms [3, 11, 13].”
Progressive Disclosure: Designing for Effective Transparency (2018, background): “Without such transparency it may be unclear whether an algorithm is optimizing the intended behavior [32,49], or whether an algorithm accidentally promotes negative, unintended consequences (e.g. filter bubbles in social media; [10,62]).”
Governance of algorithms: options and limitations (2015, background): “…(Sunstein, 2001, 2009) and filter bubbles (Pariser, 2011), biases and distortions of reality (Zhang and Dimitroff, 2005; Cushing Weigle, 2013; Bozdag, 2013); 3. constraints on the freedom of communication and expression, for example, censorship by intelligent filtering (Zittrain and Palfrey,…”
Breaking the filter bubble: democracy and design (2015, background): “This is because information can be prioritized, filtered and hidden depending on a user’s previous interaction with the system and other factors (Bozdag 2013; Diakopoulos 2014).”