Addressing Social Bias in Information Retrieval

Otterbacher, Jahna

doi:10.1007/978-3-319-98932-7_11

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Together with the earlier cases of addressing skewed web search outputs that were identified by the researchers (e.g., racialized gender bias [38]), our observations support the argument of Otterbacher [39] about the importance of designing new approaches for detecting bias in IR systems. In order to be addressed, bias has first to be reliably identified, but so far there is only a few IR studies that investigate the problem in a systematic way.…”

Section: Discussionsupporting

confidence: 85%

Advances in Bias and Fairness in Information Retrieval

2021

Communications in Computer and Information Science

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Web search engines influence perception of social reality by filtering and ranking information. However, their outputs are often subjected to bias that can lead to skewed representation of subjects such as professional occupations or gender. In our paper, we use a mixed-method approach to investigate presence of race and gender bias in representation of artificial intelligence (AI) in image search results coming from six different search engines. Our findings show that search engines prioritize anthropomorphic images of AI that portray it as white, whereas non-white images of AI are present only in non-Western search engines. By contrast, gender representation of AI is more diverse and less skewed towards a specific gender that can be attributed to higher awareness about gender bias in search outputs. Our observations indicate both the the need and the possibility for addressing bias in representation of societally relevant subjects, such as technological innovation, and emphasize the importance of designing new approaches for detecting bias in information retrieval systems.

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Section: Discussionsupporting

confidence: 85%

Advances in Bias and Fairness in Information Retrieval

2021

Communications in Computer and Information Science

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“…Therefore, we should have in mind that the resulting collections, including the one presented here, have the implicit bias of the social network itself, requiring analysis of demographic profiles and even treatment regarding their balance to try to minimise the implicit bias. Several authors have recently dealt with this topic from IR [16,36], with different methods for adjusting results and minimising bias [10,32]. Therefore, we recommend that both in the use of the collection and of the platform and methodology, these issues should have taken into account from the very beginning of the archival project and from a transdisciplinary perspective, an approach which is naturally taken in Computational Archival Science [26,37].…”

Section: Discussionmentioning

confidence: 99%

“…What else can we know about them? The inclusion of user profile's information in the collection also will require work on privacy issues and bias correction of the retrieval results, following innovative methods [10,21,36].…”

Section: Future Stepsmentioning

confidence: 99%

Building Cultural Heritage Reference Collections from Social Media through Pooling Strategies: The Case of 2020’s Tensions Over Race and Heritage

Otero

Martín-Rodilla

Parapar

2021

J. Comput. Cult. Herit.

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Social networks constitute a valuable source for documenting heritage constitution processes or obtaining a real-time snapshot of a cultural heritage research topic. Many heritage researchers use social networks as a social thermometer to study these processes, creating, for this purpose, collections that constitute born-digital archives potentially reusable, searchable, and of interest to other researchers or citizens. However, retrieval and archiving techniques used in social networks within heritage studies are still semi-manual, being a time-consuming task and hindering the reproducibility, evaluation, and open-up of the collections created. By combining Information Retrieval strategies with emerging archival techniques, some of these weaknesses can be left behind. Specifically, pooling is a well-known Information Retrieval method to extract a sample of documents from an entire document set (posts in case of social network’s information), obtaining the most complete and unbiased set of relevant documents on a given topic. Using this approach, researchers could create a reference collection while avoiding annotating the entire corpus of documents or posts retrieved. This is especially useful in social media due to the large number of topics treated by the same user or in the same thread or post. We present a platform for applying pooling strategies combined with expert judgment to create cultural heritage reference collections from social networks in a customisable, reproducible, documented, and shareable way. The platform is validated by building a reference collection from a social network about the recent attacks on patrimonial entities motivated by anti-racist protests. This reference collection and the results obtained from its preliminary study are available for use. This real application has allowed us to validate the platform and the pooling strategies for creating reference collections in heritage studies from social networks.

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“…Biases in the information provided by AI algorithms, perpetuating gender and racial stereotypes (Otterbacher, 2018), coupled with human's tendency to process information through lenses protecting their initial beliefs and biases , favor one-sided thinking, extremism, and fanaticism, often leveraged in racist ways, and seldom to indict white supremacy (Cave & Dihal, 2020) and Western imperialism, with deleterious effects on democracy and the societal wellbeing. Several ethical issues have been acknowledged arising from AI and Big Data in the literature, including exploitation of behavioural biases, deception, and addiction generation to maximize profit (Costa & Halpern, 2019), manipulation (Helbing et al, 2019), spread of misinformation, hate speech and conspiracy theories (Scheufele & Krause, 2019).…”

Section: Introductionmentioning

confidence: 99%

AI and Society

Morr¹

2022

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Responsible research and innovation is critical when it comes to AI and Big Data technologies, and their integration in society. Engaging different stakeholders in the dialog on how to address the ethical challenges of AI and tackle AI bias, is fundamental for finding viable solutions that would ensure the alignment of AI with core ethical values and would support individual and societal wellbeing. The chapter presents data from two qualitative studies that took place a few years apart, each under the umbrella of a different European project, namely the EU Horizon 2020 COMPASS project (2016-2019) and the EU Horizon 2020 SHERPA project (2018-2021).The aim of these studies was to capture different stakeholder views on AI, Big Data and Responsible Research and Innovation (RRI), with a particular focus on the dimension of ethics.While the COMPASS data collection focused on the responsible use of emerging technologies in specific sectors, i.e. healthcare and nanotechnology, the SHERPA data collection focused on the specific technologies themselves, i.e. AI and Big Data. Thirty Industry leaders, from the healthcare and nanotechnology sectors across Europe, participated in in-depth interviews, in the context of COMPASS. For SHERPA, six focus groups (N = 49, in total) were conducted that focused on discussing proposed guidelines for the responsible development and use of such technologies as well as requirements for AI regulation in Europe. The interviews and focus groups featured a broad range of stakeholders from different sectors, such as technology experts and stakeholders, industry leaders, technology practitioners and social scientists. The projects aimed to capture different perspectives and varying expertise related to the aims of the projects .

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Addressing Social Bias in Information Retrieval

Cited by 5 publications

References 9 publications

Advances in Bias and Fairness in Information Retrieval

Advances in Bias and Fairness in Information Retrieval

Building Cultural Heritage Reference Collections from Social Media through Pooling Strategies: The Case of 2020’s Tensions Over Race and Heritage

AI and Society

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