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Gender bias in artificial intelligence (AI) should be solved as a priority before AI algorithms become ubiquitous, perpetuating and accentuating the bias. While the problem has been identified as an established research and policy agenda, a cohesive review of existing research specifically addressing gender bias from a socio-technical viewpoint is lacking. Thus, the purpose of this study is to determine the social causes and consequences of, and proposed solutions to, gender bias in AI algorithms.

A comprehensive systematic review followed established protocols to ensure accurate and verifiable identification of suitable articles. The process revealed 177 articles in the socio-technical framework, with 64 articles selected for in-depth analysis.

Most previous research has focused on technical rather than social causes, consequences and solutions to AI bias. From a social perspective, gender bias in AI algorithms can be attributed equally to algorithmic design and training datasets. Social consequences are wide-ranging, with amplification of existing bias the most common at 28%. Social solutions were concentrated on algorithmic design, specifically improving diversity in AI development teams (30%), increasing awareness (23%), human-in-the-loop (23%) and integrating ethics into the design process (21%).

This systematic review is the first of its kind to focus on gender bias in AI algorithms from a social perspective within a socio-technical framework. Identification of key causes and consequences of bias and the breakdown of potential solutions provides direction for future research and policy within the growing field of AI ethics.

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-08-2021-0452

The purpose of this paper is to explain to readers how intelligent systems can fail and how artificial intelligence (AI) safety is different from cybersecurity. The goal of cybersecurity is to reduce the number of successful attacks on the system; the goal of AI Safety is to make sure zero attacks succeed in bypassing the safety mechanisms. Unfortunately, such a level of performance is unachievable. Every security system will eventually fail; there is no such thing as a 100 per cent secure system.

AI Safety can be improved based on ideas developed by cybersecurity experts. For narrow AI Safety, failures are at the same, moderate level of criticality as in cybersecurity; however, for general AI, failures have a fundamentally different impact. A single failure of a superintelligent system may cause a catastrophic event without a chance for recovery.

In this paper, the authors present and analyze reported failures of artificially intelligent systems and extrapolate our analysis to future AIs. The authors suggest that both the frequency and the seriousness of future AI failures will steadily increase.

This is a first attempt to assemble a public data set of AI failures and is extremely valuable to AI Safety researchers.

The purpose of this paper is to raise attention within the records management community about evolving demands for explanations that make it possible to understand the content of records, also when they reflect output from algorithms.

The methodological approach is a conceptual analysis based in records management theory and the philosophy of science. The concepts that are developed are thereafter applied to “the right to an explanation” and “an algorithmic ethics approach,” respectively, to further examine their viability.

Different forms of explanations, ranging from “certain” explanations to predictions, as well as varying degrees of control over the input data to algorithms, affect the nature of the explanations and what kinds of records the explanations may reside in.

This paper contributes to a conceptual frame for discussing where explanations to algorithms may be documented, within different kinds of records, emanating from different kinds of processes.

The purpose of this study is to examine whether implicit bias exists within the graduate admissions process at a large public research university in the Southeast United States. Additionally, this research sought to identify the type of strategies graduate faculty in the USA use to assess their implicit bias and the support they may need to better recognize and gauge implicit bias during the graduate application review process.

This study used the use of a qualitative, phenomenological research design by conducting individual interviews with graduate faculty members that serve on admissions committees.

The findings revealed six themes in relation to the purpose of the study – bias recognition, faculty perceptions of their own bias, faculty perceptions on the bias of others, strategies for the application review process, admission committee safeguards and the need for implicit bias training.

The study outcomes are discussed in relation to the prior research and literature on this phenomenon. Additionally, the study presents research and practical implications, including actionable strategies for how its results can be practically applied.

The purpose of this paper is to propose that in order to tackle the question of bias in algorithms, a systemic, sociotechnical and holistic perspective is needed. With reference to the term “algorithmic culture,” the interconnectedness and mutual shaping of society and technology are postulated. A sociotechnical approach requires translational work between and across disciplines. This conceptual paper undertakes such translational work. It exemplifies how gender and diversity studies, by bringing in expertise on addressing bias and structural inequalities, provide a crucial source for analyzing and mitigating bias in algorithmic systems.

After introducing the sociotechnical context, an overview is provided regarding the contemporary discourse around bias in algorithms, debates around algorithmic culture, knowledge production and bias identification as well as common solutions. The key concepts of gender studies (situated knowledges and strong objectivity) and concrete examples of gender bias then serve as a backdrop for revisiting contemporary debates.

The key concepts reframe the discourse on bias and concepts such as algorithmic fairness and transparency by contextualizing and situating them. The paper includes specific suggestions for researchers and practitioners on how to account for social inequalities in the design of algorithmic systems.

A systemic, gender-informed approach for addressing the issue is provided, and a concrete, applicable methodology toward a situated understanding of algorithmic bias is laid out, providing an important contribution for an urgent multidisciplinary dialogue.

Due to the limitations to the purpose and practice of both phenomenological and duoethnographic research methodologies, the purpose of this paper is to propose phenomenological polyethnography as a hybrid qualitative methodology, which would guide skilled researchers in conducting phenomenological exploration of an emergent experience as insiders.

This study is an applied a hybridization approach to phenomenology and duoethnography as two distinct qualitative research traditions.

Employing a poststructuralist perspective, researcher-participants with relevant difference co-investigate a phenomenological question together. Borrowing elements from both hermeneutic phenomenology and duoethnography, this methodology involves the consideration of a phenomenon, the use of authors with relevant difference who have both special insight into that phenomenon as participants and skill as qualitative researchers, the intentional collection of prereflective data while all researcher-participants are experiencing the phenomenon or immediately after, the subsequent reflection upon and interpretation of the phenomenon as it was similarly and differently experienced by the researcher-participants, and the description of both the essence and meaning of the phenomenon.

This new, hybrid qualitative methodology will enable researchers to more efficiently analyze and disseminate the research of insider knowledge on emergent phenomena in higher education and other settings.

As a new methodology, it may be used to investigate events and provide rich, thick description in a way not before seen.

Belkin and Robertson (1976a) reflected on the ethical implications of theoretical research in information science and warned that there was potential for abuse of knowledge gained by undertaking such research and applying it to information systems. In particular, they identified the domains of advertising and political propaganda that posed particular problems. The purpose of this literature review is to revisit these ideas in the light of recent events in global information systems that demonstrate that their fears were justified.

The authors revisit the theory in information science that Belkin and Robertson used to build their argument, together with the discussion on ethics that resulted from this work in the late 1970s and early 1980s. The authors then review recent literature in the field of information systems, specifically information retrieval, social media and recommendation systems that highlight the problems identified by Belkin and Robertson.

Information science theories have been used in conjunction with empirical evidence gathered from user interactions that have been detrimental to both individuals and society. It is argued in the paper that the information science and systems communities should find ways to return control to the user wherever possible, and the ways to achieve this are considered.

The ethical issues identified require a multidisciplinary approach with research in information science, computer science, information systems, business, sociology, psychology, journalism, government and politics, etc. required. This is too large a scope to deal with in a literature review, and we focus only on the design and implementation of information systems (Zimmer, 2008a) through an information science and information systems perspective.

The authors argue that information systems such as search technologies, social media applications and recommendation systems should be designed with the recipient of the information in mind (Paisley and Parker, 1965), not the sender of that information.

Information systems designed ethically and with users in mind will go some way to addressing the ill effects typified by the problems for individuals and society evident in global information systems.

The authors synthesize the evidence from the literature to provide potential technological solutions to the ethical issues identified, with a set of recommendations to information systems designers and implementers.

This paper presents recently published resources on library instruction and information literacy. It provides an introductory overview and a selected annotated bibliography of publications covering various library types, study populations and research contexts. The selected bibliography is useful to efficiently keep up with trends in library instruction for busy practitioners, library science students and those wishing to learn about information literacy in other contexts.

This article annotates 424 English-language periodical articles, monographs, dissertations, theses and reports on library instruction and information literacy published in 2021. The sources were selected from the EBSCO platform for Library, Information Science, and Technology Abstracts (LISTA), Education Resources Information Center (ERIC), Scopus, ProQuest Dissertations and Theses, and WorldCat, published in 2021 that included the terms “information literacy,” “library instruction,” or “information fluency” in the title, abstract or keywords. The sources were organized in Zotero. Annotations summarize the source, focusing on the findings or implications. Each source was categorized into one of seven pre-determined categories: K-12 Education, Children and Adolescents; Academic and Professional Programs; Everyday Life, Community, and the Workplace; Libraries and Health Information Literacy; Multiple Library Types; and Other Information Literacy Research and Theory.

The paper provides a brief description of 424 sources and highlights sources that contain unique or significant scholarly contributions.

The information may be used by librarians, researchers and anyone interested as a quick and comprehensive reference to literature on library instruction and information literacy within 2021.

This study aims to present a new framework for ethical creation and evaluation of multi-perspective knowledge organization systems.

Applying Held's understanding of the ethics of care, this paper proposes five operative criteria for ethical building and evaluation of multi-perspective knowledge representation and organization systems.

This paper argues that a carefully designed multipoint view of representation and organization conforms to the proposed ethical criteria and shifts concerns associated with the expectation of neutrality of library information professionals to the necessity to humanize and diversify the representation and organization of knowledge to build inclusive and equitable systems.

This paper presents multi-perspectiveness as key to ethical knowledge organization. The paper proposes a generic taxonomy of the main stages in the creation of multi-perspective knowledge representation and organization systems and demonstrates how to apply the proposed framework in each stage to ensure ethical outcomes.

This study aims to explore the dynamics enabling strategic account management (SAM) to function as a value co-creation selling model in the pharmaceutical industry.

Using an inductive qualitative research design, data are collected within 11 industry customers in Canada. This work focuses on hospitals as strategic accounts of pharmaceutical companies, exploring SAM value co-creation in the “hospital-pharmaceutical company” relationship.

The findings suggest the presence of two key dimensions that can enable a value co-creation SAM model in the hospital-pharmaceutical relationship: “customer-tailored value-added initiatives” and “relationship enhancers”. Customer-tailored value-added initiatives explain the activities that are central to the hospital-pharmaceutical company relationship and can lead to the provision of value added that is unique to the hospital. Relationship enhancers explain the activities that can help strengthen hospital-pharmaceutical company relations in the pursuit of enhanced value-added interactions between the two parties. The research demonstrates a cyclical relationship between “customer-tailored value-added initiatives” and “relationship enhancers”, leading to value co-creation through a SAM model.

The study informs pharmaceutical industry practitioners on how to improve their value proposition through new, more sustainable selling practices. It offers information on implementing a value co-creation SAM model, which can enable pharmaceutical companies to sustain long-lasting value-added relationships with key accounts such as hospitals.

The study contributes to the field of SAM by conceptualizing SAM as a value co-creation system. It introduces new knowledge in pharmaceutical marketing by offering empirical insight on the applicability and use of SAM in the hospital-pharmaceutical company dyad.