Search results for ""author marie lechner""

Computer Grrrlz brings together 23 international artistic positions that negotiate the complex relationship between gender and technology in past and present. The book deals with the link between women and technology from the first human computers to the current revival of techno-feminist movements. An illustrated timeline with over 200 entries covers these developments from the 18th century to the present day. The publication presents artists, hackers, makers and researchers who are working on how to think differently about technology: by questioning the gender bias in big data and artificial intelligence, promoting an open and diversified Internet, and designing utopian technologies. The perspectives presented here address a broad range of topics: electronic colonialism, the place of minorities on the Internet, the sexist bias of algorithms, the dangerous dominance of white men in the development of artificial intelligence and digital surveillance, but also ideas on how we can change our traditional ways of thinking. Artists included: Morehshin Allahyari, Manetta Berends, Zach Blas & Jemima Wyman, Nadja Buttendorf, Elisabeth Caravella, Jennifer Chan, Aleksandra Domanović, Louise Drulhe, Elisa Giardina Papa, Darsha Hewitt, Lauren Huret, Hyphen-Labs, Dasha Ilina, Roberte la Rousse, Mary Maggic, Caroline Martel, Lauren Moffatt, Simone C. Niquille, Jenny Odell, Tabita Rezaire, Erica Scourti, Suzanne Treister, Lu Yang. Text in English and German.

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In the popular imagination, artificial intelligence (AI) is usually portrayed as a divine entity that makes “just” and “objective” decisions. Yet AI is anything but intelligent. Rather, it recognises in large amounts of data what it has been trained to recognise. Like a sniffer dog, it finds exactly what it has been taught to look for. In performing this task, it is much more efficient than any human being – but this precisely is also its problem. AI only mirrors or repeats what it has been instructed to reflect. Seen in this light, it may be viewed as a kind of digital “house of mirrors”. Humans train machines, and these machines are only as good or as bad as the humans who train them. Based on this insight, the publication addresses not only algorithmic bias or discrimination in AI, but also AI-related issues such as hidden human labour, the problem of categorisation and classification – and our ideas and fantasies about AI. It also raises the question whether (and how) it is possible to reclaim agency in this context. Text in English and German.

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