Fairness in Image Search: A Study of Occupational Stereotyping in Image Retrieval and its Debiasing

Multi-modal search engines have experienced significant growth and widespread use in recent years, making them the second most common internet use. While search engine systems offer a range of services, the image search field has recently become a focal point in the information retrieval community, as the adage goes, "a picture is worth a thousand words". Although popular search engines like Google excel at image search accuracy and agility, there is an ongoing debate over whether their search results can be biased in terms of gender, language, demographics, socio-cultural aspects, and stereotypes. This potential for bias can have a significant impact on individuals' perceptions and influence their perspectives. In this paper, we present our study on bias and fairness in web search, with a focus on keyword-based image search. We first discuss several kinds of biases that exist in search systems and why it is important to mitigate them. We narrow down our study to assessing and mitigating occupational stereotypes in image search, which is a prevalent fairness issue in image retrieval. For the assessment of stereotypes, we take gender as an indicator. We explore various open-source and proprietary APIs for gender identification from images. With these, we examine the extent of gender bias in top-tanked image search results obtained for several occupational keywords. To mitigate the bias, we then propose a fairness-aware re-ranking algorithm that optimizes (a) relevance of the search result with the keyword and (b) fairness w.r.t genders identified. We experiment on 100 top-ranked images obtained for 10 occupational keywords and consider random re-ranking and re-ranking based on relevance as baselines. Our experimental results show that the fairness-aware re-ranking algorithm produces rankings with better fairness scores and competitive relevance scores than the baselines.Comment: 20 Pages, Work uses Proprietary Search Systems from the year 202

Quality of Information Regarding Repair Restorations on Dentist Websites: Systematic Search and Analysis

Background: Repairing instead of replacing partially defective dental restorations represents a minimally invasive treatment concept, and repairs are associated with advantages over complete restoration replacement. To participate in the shared decision-making process when facing partially defective restorations, patients need to be aware of the indications, limitations, and advantages or disadvantages of repairs. Patients are increasingly using the internet to gain health information like this online. Objective: We aimed to assess the quality of German-speaking dentist websites on repairs of partially defective restorations. Methods: Three electronic search engines were used to identify German-speaking websites of dental practices mentioning repairs. Regarding information on repairs, websites were assessed for (1) technical and functional aspects, (2) comprehensiveness of information, and (3) generic quality and risk of bias. Domains 1 and 3 were scored using validated tools (LIDA and DISCERN). Comprehensiveness was assessed using a criterion checklist related to evidence, advantages and disadvantages, restorations and defects suitable for repairs, and information regarding technical implementation. Generalized linear modeling was used to assess the impact of practice-specific parameters (practice location, practice setting, dental society membership, and year of examination or license to practice dentistry) on the quality of information. An overall quality score was calculated by averaging the quality scores of all three domains and used as primary outcome parameter. Quality scores of all three domains were also assessed individually and used as secondary outcomes. Results: Fifty websites were included. The median score of quality of information was 23.2% (interquartile range [IQR] 21.7%-26.2%). Technical and functional aspects (55.2% [IQR 51.7%-58.6%]) showed significantly higher quality than comprehensiveness of information (8.3% [IQR 8.3%-16.7%]) and generic quality and risk of bias (3.6% [IQR 0.0%-7.1%]; P.05/generalized linear modeling). Conclusions: The quality of German-speaking dentist websites on repairs was limited. Despite sufficient technical and functional quality, the provided information was neither comprehensive nor trustworthy. There is great need to improve the quality of information to fully and reliably inform patients, thereby allowing shared decision making

Developing Critical Thinking in online search

Digital skills especially those related to Information Literacy, are today considered fundamental to the education of students, both at school and at university. Searching and evaluating information found on the Internet is surely an important competency. An effective way to develop this competency is to educate students about the development of critical thinking. The article presents a qualitative-quantitative survey conducted during a course in Educational Technologies within a five year Degree program. The outcomes of the survey reveal some interesting behaviors and perceptions of students when they are faced with the Web search process and the characteristics of their critical thinking processes: some aspects of critical thinking are generally well supported, but others are acquired only after specific training. Experience shows that if properly motivated by metacognitive reflections and a clear method, students can actually critically evaluate the information presented online, the sources, and the sustainability of the arguments found. Positive results also occurred when the evaluation process was done in a collaborative modality

Users' trust in information resources in the Web environment: a status report

This study has three aims; to provide an overview of the ways in which trust is either assessed or asserted in relation to the use and provision of resources in the Web environment for research and learning; to assess what solutions might be worth further investigation and whether establishing ways to assert trust in academic information resources could assist the development of information literacy; to help increase understanding of how perceptions of trust influence the behaviour of information users

Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work

Deep networks thrive when trained on large scale data collections. This has given ImageNet a central role in the development of deep architectures for visual object classification. However, ImageNet was created during a specific period in time, and as such it is prone to aging, as well as dataset bias issues. Moving beyond fixed training datasets will lead to more robust visual systems, especially when deployed on robots in new environments which must train on the objects they encounter there. To make this possible, it is important to break free from the need for manual annotators. Recent work has begun to investigate how to use the massive amount of images available on the Web in place of manual image annotations. We contribute to this research thread with two findings: (1) a study correlating a given level of noisily labels to the expected drop in accuracy, for two deep architectures, on two different types of noise, that clearly identifies GoogLeNet as a suitable architecture for learning from Web data; (2) a recipe for the creation of Web datasets with minimal noise and maximum visual variability, based on a visual and natural language processing concept expansion strategy. By combining these two results, we obtain a method for learning powerful deep object models automatically from the Web. We confirm the effectiveness of our approach through object categorization experiments using our Web-derived version of ImageNet on a popular robot vision benchmark database, and on a lifelong object discovery task on a mobile robot.Comment: 8 pages, 7 figures, 3 table

Report on the Information Retrieval Festival (IRFest2017)

The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017

The Hegelian Inquiring System and Critical Triangulation Tools for the Internet Information Slave

This paper discusses informing, i.e. increasing people’s understanding of reality by providing representations of this reality. The Hegelian inquiry system is used to explain the nature of informing. Understanding the Hegelian inquiry system is essential for making informed decisions where the reality can be ambiguous and where sources of bias and manipulation have to be understood for increasing the level of free-informed choice. This inquiry system metaphorically identifies information masters and slaves, and we propose critical dialectic information triangulation (CDIT) tools for information slaves (i.e. non-experts) in dialect interactions with informative systems owned by supposed information masters. The paper concludes with suggestions for further research on informative triangulation tools for the internet and management information systems

New perspectives on Web search engine research

Purpose–The purpose of this chapter is to give an overview of the context of Web search and search engine-related research, as well as to introduce the reader to the sections and chapters of the book. Methodology/approach–We review literature dealing with various aspects of search engines, with special emphasis on emerging areas of Web searching, search engine evaluation going beyond traditional methods, and new perspectives on Webs earching. Findings–The approaches to studying Web search engines are manifold. Given the importance of Web search engines for knowledge acquisition, research from different perspectives needs to be integrated into a more cohesive perspective. Researchlimitations/implications–The chapter suggests a basis for research in the field and also introduces further research directions. Originality/valueofpaper–The chapter gives a concise overview of the topics dealt with in the book and also shows directions for researchers interested in Web search engines

A three-year study on the freshness of Web search engine databases

This paper deals with one aspect of the index quality of search engines: index freshness. The purpose is to analyse the update strategies of the major Web search engines Google, Yahoo, and MSN/Live.com. We conducted a test of the updates of 40 daily updated pages and 30 irregularly updated pages, respectively. We used data from a time span of six weeks in the years 2005, 2006, and 2007. We found that the best search engine in terms of up-to-dateness changes over the years and that none of the engines has an ideal solution for index freshness. Frequency distributions for the pages’ ages are skewed, which means that search engines do differentiate between often- and seldom-updated pages. This is confirmed by the difference between the average ages of daily updated pages and our control group of pages. Indexing patterns are often irregular, and there seems to be no clear policy regarding when to revisit Web pages. A major problem identified in our research is the delay in making crawled pages available for searching, which differs from one engine to another