Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

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This article investigates an emerging class of contemporary machines: the robot companion. It is introduced as a robot that will accompany ‘us’ in ‘our’ human everyday lives. This article analyzes one example of how robot companionship is realized while querying how this realization might imply a change in how ‘we’ conceive of human/machine relations. Drawing on central insights into the making of the humanoid Armar, the author develops an approach to emerging human/machine relations through affects, more precisely through the affective strategies and affective labors taking place in the robotics laboratory. She furthermore suggests taking a posthumanist perspective on the analysis, which entails becoming attentive to the intra-active co-production between human and machine. Importantly, this also allows her to tweak the powerful differentiation between success and failure at work in this specific setting, the robotics laboratory. How can ‘we’ rethink human/machine relations of humanlike interaction through queering success and failure at the robot/human interface? Finally, the author suggests establishing an understanding of laboratory work on the project of the humanlike companion that takes into account the queering potential of failure – centrally by emphasizing the interweaving of knowing and affects, rather than neglecting their connection. At stake seems to be the possibility to develop visions of how to turn the capitalist endeavor of increasing rationalizations of ‘human everyday lives’ into a more responsible and accountable practice of technologization that takes into account the largely neglected dimensions of human/machine relations beyond the success/failure binary.   

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