Special issue on de‑personalisation, diversification, filter bubbles and search

The abstract is included in the text

Sosiaalisen median informaatiokuplat: Haitallisten vaikutusten estäminen käyttäjälähtöisesti

Pariserin esittelemää informaatiokuplaa (engl. filter bubble) sanotaan yhdeksi tärkeimmistä huolenaiheista suosittelujärjestelmiin liittyen. Suosittelujärjestelmiä käytetään internetpalveluissa, jotta käyttäjälle voidaan suositella mahdollisimman sopivaa sisältöä. Informaatiokupla on suosittelujärjestelmien haittapuolena syntyvä ilmiö, joka vaikuttaa käyttäjiin kaventamalla käyttäjälle saatavilla olevaa tietosisältöä määrällisesti ja laadullisesti. Tietosisällön kapenemisesta aiheutuu vaikutuksia esimerkiksi ajatteluun, oppimiseen ja luovuuteen. Tutkielmassa tutkitaan sosiaalisen median informaatiokuplien vaikutuksien estämistä ja heikentämistä käyttäjälähtöisesti. Lisäksi sivutaan järjestelmälähtöistä informaatiokuplien vaikutusten heikentämistä ja estämistä. Tutkielman tutkimuskysymys on: miten sosiaalisen median informaatiokuplien vaikutuksia voidaan heikentää tai estää käyttäjälähtöisesti? Tutkielman menetelmä on kirjallisuuskatsaus. Lähteinä käytetään pääasiassa tieteellisiä artikkeleja ja tutkimuksia. Mukana on myös aiheeseen liittyvä kirja sekä verkkojulkaisuja. Pääasiassa lähteet ovat tietojenkäsittelytieteiden alalta, mutta mukana on myös muiden alojen julkaisuja. Tutkielma osoittaa, että on olemassa keinoja, joilla käyttäjä voi pyrkiä estämään informaatiokuplan vaikutuksia. Keinot jaetaan teemoihin: oman verkkokäyttäytymisen muokkaaminen, kolmannen osapuolen tarjoamat tekniset ratkaisut sekä kriittinen ajattelu ja tietoinen pyrkimys erilaiseen tietoon. Teemojen sisältä löytyviä konkreettisia keinoja ovat esimerkiksi evästeiden poistaminen, kolmannen osapuolen tarjoamat tekniset ratkaisut sekä kriittinen ajattelu. Tutkielmassa kerrotaan myös lyhyesti siitä, mitä sosiaaliset mediat voivat tehdä heikentääkseen informaatiokuplia, jotta ymmärretään myös järjestelmien vastuu. Informaatiokuplia voidaan estää järjestelmälähtöisesti uudelleensuunnittelemalla suosittelujärjestelmät, tarjoamalla käyttäjälle työkaluja, jotka selittävät tiedon personointia ja suodatusta sekä parantamalla avoimuutta. Vaikka informaatiokuplasta löytyy kohtalaisen paljon tietoa, niin luetun kirjallisuuden perusteella voidaan sanoa, että kattavaa tutkimusta käyttäjälähtöisestä informaatiokuplien vaikutusten heikentämisestä ei ole tehty. Tutkielman tuloksena todetaan myös, että on kehitetty vain vähän kolmannen osapuolen tarjoamia teknisiä ratkaisuja, joiden avulla voisi heikentää informaatiokuplan vaikutuksia sosiaalisen median käyttäjään

Deconstructing the right to privacy considering the impact of fashion recommender systems on an individual’s autonomy and identity

Computing ‘fashion’ into a system of algorithms that personalise an individual’s shopping journey is not without risks to the way we express, assess, and develop aspects of our identity. This study uses an interdisciplinary research approach to examine how an individual’s interaction with algorithms in the fashion domain shapes our understanding of an individual’s privacy, autonomy, and identity. Using fashion theory and psychology, I make two contributions to the meaning of privacy to protect notions of identity and autonomy, and develop a more nuanced perspective on this concept using ‘fashion identity’. One, a more varied outlook on privacy allows us to examine how algorithmic constructions impose inherent reductions on individual sense-making in developing and reinventing personal fashion choices. A “right to not be reduced” allows us to focus on the individual’s practice of identity and choice with regard to the algorithmic entities incorporating imperfect semblances on the personal and social aspects of fashion. Second, I submit that we need a new perspective on the right to privacy to address the risks of algorithmic personalisation systems in fashion. There are gaps in the law regarding capturing the impact of algorithmic personalisation systems on an individual’s inference of knowledge about fashion, as well as the associations of fashion applied to individual circumstances. Focusing on the case law of the European Court of Human Rights (ECtHR) and the General Data Protection Regulation (GDPR), as well as aspects of EU non-discrimination and consumer law, I underline that we need to develop a proactive approach to the right to privacy entailing the incorporation of new values. I define these values to include an individual’s perception and self-relationality, describing the impact of algorithmic personalisation systems on an individual’s inference of knowledge about fashion, as well as the associations of fashion applied to individual circumstances. The study concludes with recommendations regarding the use of AI techniques in fashion using an international human rights approach. I argue that the “right to not be reduced” requires new interpretative guidance informing international human rights standards, including Article 17 of the International Covenant on Civil and Political Rights (ICCPR). Moreover, I consider that the “right to not be reduced” requires us to consider novel choices that inform the design and deployment of algorithmic personalisation systems in fashion, considering the UN Guiding Principles on Business and Human Rights and the EU Commission’s Proposal for an AI Act

Technology and Democracy: Understanding the influence of online technologies on political behaviour and decision-making

Drawing from many disciplines, the report adopts a behavioural psychology perspective to argue that “social media changes people’s political behaviour”. Four pressure points are identified and analysed in detail: the attention economy; choice architectures; algorithmic content curation; and mis/disinformation. Policy implications are outlined in detail.JRC.H.1-Knowledge for Policy: Concepts and Method

The impact of result diversification on search behaviour and performance

Result diversification aims to provide searchers with a broader view of a given topic while attempting to maximise the chances of retrieving relevant material. Diversifying results also aims to reduce search bias by increasing the coverage over different aspects of the topic. As such, searchers should learn more about the given topic in general. Despite diversification algorithms being introduced over two decades ago, little research has explicitly examined their impact on search behaviour and performance in the context of Interactive Information Retrieval (IIR). In this paper, we explore the impact of diversification when searchers undertake complex search tasks that require learning about different aspects of a topic (aspectual retrieval). We hypothesise that by diversifying search results, searchers will be exposed to a greater number of aspects. In turn, this will maximise their coverage of the topic (and thus reduce possible search bias). As a consequence, diversification should lead to performance benefits, regardless of the task, but how does diversification affect search behaviours and search satisfaction? Based on Information Foraging Theory (IFT), we infer two hypotheses regarding search behaviours due to diversification, namely that (i) it will lead to searchers examining fewer documents per query, and (ii) it will also mean searchers will issue more queries overall. To this end, we performed a within-subjects user study using the TREC AQUAINT collection with 51 participants, examining the differences in search performance and behaviour when using (i) a non-diversified system (BM25) versus (ii) a diversified system (BM25+xQuAD) when the search task is either (a) ad-hoc or (b) aspectual. Our results show a number of notable findings in terms of search behaviour: participants on the diversified system issued more queries and examined fewer documents per query when performing the aspectual search task. Furthermore, we showed that when using the diversified system, participants were: more successful in marking relevant documents, and obtained a greater awareness of the topics (i.e. identified relevant documents containing more novel aspects). These findings show that search behaviour is influenced by diversification and task complexity. They also motivate further research into complex search tasks such as aspectual retrieval -- and how diversity can play an important role in improving the search experience, by providing greater coverage of a topic and mitigating potential bias in search results

Peeking into the other half of the glass : handling polarization in recommender systems.

This dissertation is about filtering and discovering information online while using recommender systems. In the first part of our research, we study the phenomenon of polarization and its impact on filtering and discovering information. Polarization is a social phenomenon, with serious consequences, in real-life, particularly on social media. Thus it is important to understand how machine learning algorithms, especially recommender systems, behave in polarized environments. We study polarization within the context of the users\u27 interactions with a space of items and how this affects recommender systems. We first formalize the concept of polarization based on item ratings and then relate it to the item reviews, when available. We then propose a domain independent data science pipeline to automatically detect polarization using the ratings rather than the properties, typically used to detect polarization, such as item\u27s content or social network topology. We perform an extensive comparison of polarization measures on several benchmark data sets and show that our polarization detection framework can detect different degrees of polarization and outperforms existing measures in capturing an intuitive notion of polarization. We also investigate and uncover certain peculiar patterns that are characteristic of environments where polarization emerges: A machine learning algorithm finds it easier to learn discriminating models in polarized environments: The models will quickly learn to keep each user in the safety of their preferred viewpoint, essentially, giving rise to filter bubbles and making them easier to learn. After quantifying the extent of polarization in current recommender system benchmark data, we propose new counter-polarization approaches for existing collaborative filtering recommender systems, focusing particularly on the state of the art models based on Matrix Factorization. Our work represents an essential step toward the new research area concerned with quantifying, detecting and counteracting polarization in human-generated data and machine learning algorithms.We also make a theoretical analysis of how polarization affects learning latent factor models, and how counter-polarization affects these models. In the second part of our dissertation, we investigate the problem of discovering related information by recommendation of tags on social media micro-blogging platforms. Real-time micro-blogging services such as Twitter have recently witnessed exponential growth, with millions of active web users who generate billions of micro-posts to share information, opinions and personal viewpoints, daily. However, these posts are inherently noisy and unstructured because they could be in any format, hence making them difficult to organize for the purpose of retrieval of relevant information. One way to solve this problem is using hashtags, which are quickly becoming the standard approach for annotation of various information on social media, such that varied posts about the same or related topic are annotated with the same hashtag. However hashtags are not used in a consistent manner and most importantly, are completely optional to use. This makes them unreliable as the sole mechanism for searching for relevant information. We investigate mechanisms for consolidating the hashtag space using recommender systems. Our methods are general enough that they can be used for hashtag annotation in various social media services such as twitter, as well as for general item recommendations on systems that rely on implicit user interest data such as e-learning and news sites, or explicit user ratings, such as e-commerce and online entertainment sites. To conclude, we propose a methodology to extract stories based on two types of hashtag co-occurrence graphs. Our research in hashtag recommendation was able to exploit the textual content that is available as part of user messages or posts, and thus resulted in hybrid recommendation strategies. Using content within this context can bridge polarization boundaries. However, when content is not available, is missing, or is unreliable, as in the case of platforms that are rich in multimedia and multilingual posts, the content option becomes less powerful and pure collaborative filtering regains its important role, along with the challenges of polarization

Rethinking Media Plurality Regulation: Promoting Exposure Diversity and Controlling the Power of New Online Selection intermediaries

Due to the Internet and the convergence of technology, the media landscape has dramatically changed. The new problem of online media network is no longer the lack of information but rather the overload of information and how to find the information. This is because, with the abundance of information, what people see is not equal to what made available. The information-overload can result in narrowing the range of people’s attention to concentrate around a few preference sources. From the media law scholars’ perspective, the significant question now is whether people choose media contents diversely. The notion of media plurality has shifted from the concern about the diversity of available source and content to diversity of choices people make and diversity of actual content consumed by individuals, or the so-called ‘exposure diversity’. Since people cannot consume all information presented to them, they rely on new ‘online selection intermediaries’ (i.e. search engine and social media) to assist them to find relevant information from infinite information. As a result, there is a shift of power from traditional media to new ‘online selection intermediaries’ which act as a gatekeeper to access to information. Selection intermediaries consequently have a significant influence on exposure diversity. Selection intermediaries therefore need to be regulated for the ultimate purpose of exposure diversity. However, the current existing laws have not moved from the design to regulate traditional media and to ensure a wide range of source and viewpoints available. They are still based on the perspective of the time when there were scarcity of analogue spectrum and high barriers to enter to media market. Consequently, the existing laws are inadequate to regulate the selection intermediaries to achieve diversity of viewpoints exposed to citizen. This thesis, therefore, discusses that the media regulation should be reformed to regulate selection intermediaries to encourage diversity of viewpoints actually exposed to people. This thesis proposes the appropriate approaches to regulate these new selection intermediaries for the achievement of exposure diversity. This thesis is a correct and up to date statement of the relevant law as of 1 August 2018

Personalization in Social Media: Challenges and Opportunities for Democratic Societies

Personalization algorithms perform a fundamental role of knowledge management in order to restrain information overload, reduce complexity and satisfy individuals. Personalization of media content in mainstream social media, however, can be used for micro-target political messages, and can also create filter bubbles and strengthen echo chambers that restrain the exposure to diverse, challenging and serendipitous information. These represent fundamental issues for media law and ethics both seeking to preserve autonomy of choice and media pluralism in democratic societies. As a result, informational empowerment may be reduced and group polarization, audience fragmentation, conspiratorial thinking and other democratically negative consequences could arise. Even though research about the detrimental effects of personalization is more often inconsistent, there is no doubt that in the long run the algorithmic capacity to steer our lives in increasingly sophisticated ways will dramatically expand. Key questions need to be further discussed; for instance, to what extent can profiling account for the complexity of individual identity? To what extent are users, media and algorithms responsible in such practices? What are the main values and trade-offs that inform designers in such a fundamental societal algorithmic arbitrage? How is social media’s personalization directly or indirectly regulated in the European Union? The thesis firstly presents a critical overview of information societies, analyzing social media content personalization practices, dynamics and unintended consequences. Secondly, it explores the role of serendipity as a design and ethical principle for social media. Thirdly, the European legal landscape with regard to personalization is analyzed from a regulatory, governance and ethical perspective. Finally, it is introduced the concept of ‘algorithmic sovereignty’ as a valuable abstraction to begin to frame technical, legal and political preconditions and standards to preserve users’ autonomy, and to minimize the risks arising in the context of personalization