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Since the coronavirus (SARS-CoV-2) pandemic began, large amounts of (mis)information have been disseminated worldwide. We conducted an online survey in Switzerland (N = 1,129) in April 2021 to ask respondents which information has received too little attention in public discourse, which measures help containing coronavirus infection and Covid-19, and about subjectively perceived Covid-19 misinformation. Content analysis of the open answers revealed that vaccination and its potential side effects, aspects related to political measures, psychological and social aspects, as well as science and research topics deserved more attention in the eyes of the respondents, mostly from politics or media. The most frequently mentioned effective measures were social distancing, wearing masks, general hygiene, and vaccination. Notably, the number of measures mentioned was related to the degree to which the pandemic affected individuals subjectively, trust in public institutions, and their individual level of science-related populism. Swiss residents with less trust in public institutions and who consume less news media on Covid-19 are more likely to believe misinformation on (in)effective measures against the virus. Most respondents encountered Covid-19 misinformation and could name examples, including sources. Education and information use affect the frequency of subjectively encountered misinformation. More highly educated people can name more misinformation instances encountered than less educated people.

Das Virus verstehen, Todesfälle verhindern, Chaos vermeiden, Fakten aufzeigen und den Boden bereiten für gesellschaftliches Mittragen von Massnahmen – Wissenschaft, Politik und Gesellschaft kamen sich in der Pandemie näher, gleichzeitig kam es auch zu Konfrontationen. Der vorliegende Sammelband vereinigt Analysen und Diskussionsbeiträge, die im Umfeld der Akademien der Wissenschaften Schweiz entstanden sind. Es ist einerseits ein Zeitdokument über die Jahre 2020 bis zum Spätsommer 2021 und bildet eine Periode ab, in der WissenschafterInnen mit grossem Engagement geforscht haben und ihre Erkenntnisse unmittelbar der Gesellschaft zugänglich gemacht haben. Andererseits sind auch Texte entstanden, die vorwärts denken. Was können wir aus der Pandemie lernen? Wie sollte wissenschaftliche Politikberatung funktionieren? Wie können Wissenschaft, Gesellschaft und Politik im Dialog bleiben? Was können wir gegen Fake News tun? Die Publikation gibt Anregungen, diese Fragen weiterzuverfolgen, damit durch Diskussion ein Fundament für die Zukunft entsteht., Akademien der Wissenschaften Schweiz (2021) Covid-19 – Wissenschaft und Gesellschaft. Beiträge in Zeiten der Pandemie. Swiss Academies Communications 16 (5).

Forget Artificial Intelligence, it’s all about 'Ethical AI'. (© 2020 Ecowin Verlag by Benevento Publishing/mit freundlicher Genehmigung), {"references":["Allhutter, D. / Cech, F. / Fischer, F. / Grill, G. / Mager, A. (2020): Algorithmic Profiling of Job Seekers in Austria: How Austerity Politics Are Made Effective. In: Frontiers in Big Data, 3. https://doi.org/10.3389/fdata.2020.00005","Belinchon, E. / Bollmann, B. / Cussins Newman, J. / Jobin, A. / Nakonz, J. (2019): Towards an Inclusive Future in AI. A Global Participatory Process [Policy Recipes]. foraus – Forum Aussenpolitik. https://www.foraus.ch/wp-content/uploads/2019/10/20191022_Policy-Kitchen-AI_WEB-1.pdf","Broussard, M. (2018): Artificial Unintelligence: How Computers Misunderstand the World. The MIT Press, Cambridge MA","Buolamwini, J. / Gebru, T. (2018): Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In: Proceedings of Machine Learning Research, 81, pp. 1–15","Crawford, K. / Joler, V. (2018): Anatomy of an AI System. Anatomy of an AI System. http://www.anatomyof.ai","Elkin, C. / Witherspoon, S. (2019): Machine learning can boost the value of wind energy. DeepMind. https://deepmind.com/blog/article/machine-learning- can-boost-value-wind-energy","Eubanks, V. (2018): Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin's Press, New York NY","Greene, D. / Hoffmann, A. L. / Stark, L. (2019): Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning. In: Proceedings of the 52nd Hawaii International Conference on System Sciences. http://scholarspace.manoa.hawaii.edu/handle/10125/59651","Hagendorff, T. (2020): The Ethics of AI Ethics: An Evaluation of Guidelines. In: Minds and Machines. https://doi.org/10.1007/s11023-020-09517-8","Harwell, D. (2020): AI baby monitors attract anxious parents: ›Fear is the quickest way to get people's a ention‹. In: Washington Post. https://www.washingtonpost.com/technology/2020/02/25/ai-baby-monitors/","Jobin, A. (2019): Diagnose: Ambivalenz. Freiheit im digitalen Zeitalter. In: Marti, M. L. / Strub J. (Ed.), Freiheit. hier + jetzt, Baden","Jobin, A. (2020): Ethische KI-Richtlinien zuhauf – und jetzt? In: Zukunftsblog | ETH Zürich. https://ethz.ch/de/news-und-veranstaltungen/eth-news/ news/2020/01/blog-anna-jobin-ki-richtlinien.html","Jobin, A. / Ienca, M. / Vayena, E. (2019): The global landscape of AI ethics guidelines. In: Nature Machine Intelligence, 1, pp. 389–399. https://doi. org/10.1038/s42256-019-0088-2","Mittelstadt, B. (2019): Principles alone cannot guarantee ethical AI. In: Nature Machine Intelligence, 1, pp. 501–507. https://doi.org/10.1038/ s42256-019-0114-4","O'Neil, C. (2016): Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown, New York NY","Vaidhyanathan, S. (2011): The Googlization of Everything: (And why we should worry). University of California Press, Berkeley CA","Vaidhyanathan, S. (2018): Anti-Social Media: How Facebook Disconnects Us and Undermines Democracy. Oxford University Press, Oxford","Whittlestone, J. / Nyrup, R. / Alexandrova, A. / Cave, S. (2019): The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pp. 195–200. https://doi.org/10.1145/3306618.3314289"]}

Künstliche Intelligenz ist out, ethische KI ist in. (© 2020 Ecowin Verlag by Benevento Publishing/mit freundlicher Genehmigung), {"references":["Allhutter, D. / Cech, F. / Fischer, F. / Grill, G. / Mager, A. (2020): Algorithmic Profiling of Job Seekers in Austria: How Austerity Politics Are Made Effective. In: Frontiers in Big Data, 3. https://doi.org/10.3389/fdata.2020.00005","Belinchon, E. / Bollmann, B. / Cussins Newman, J. / Jobin, A. / Nakonz, J. (2019): Towards an Inclusive Future in AI. A Global Participatory Process [Policy Recipes]. foraus – Forum Aussenpolitik. https://www.foraus.ch/wp-content/uploads/2019/10/20191022_Policy-Kitchen-AI_WEB-1.pdf","Broussard, M. (2018): Artificial Unintelligence: How Computers Misunderstand the World. The MIT Press, Cambridge MA","Buolamwini, J. / Gebru, T. (2018): Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In: Proceedings of Machine Learning Research, 81, S. 1–15","Crawford, K. / Joler, V. (2018): Anatomy of an AI System. Anatomy of an AI System. http://www.anatomyof.ai","Elkin, C. / Witherspoon, S. (2019): Machine learning can boost the value of wind energy. DeepMind. https://deepmind.com/blog/article/machine-learning- can-boost-value-wind-energy","Eubanks, V. (2018): Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin's Press, New York NY","Greene, D. / Hoffmann, A. L. / Stark, L. (2019): Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning. In: Proceedings of the 52nd Hawaii International Conference on System Sciences. http://scholarspace.manoa.hawaii.edu/handle/10125/59651","Hagendorff, T. (2020): The Ethics of AI Ethics: An Evaluation of Guidelines. In: Minds and Machines. https://doi.org/10.1007/s11023-020-09517-8","Harwell, D. (2020): AI baby monitors attract anxious parents: ›Fear is the quickest way to get people's a ention‹. In: Washington Post. https://www.washingtonpost.com/technology/2020/02/25/ai-baby-monitors/","Jobin, A. (2019): Diagnose: Ambivalenz. Freiheit im digitalen Zeitalter. In: Marti, M. L. / Strub J. (Hrsg.), Freiheit. hier + jetzt, Baden","Jobin, A. (2020): Ethische KI-Richtlinien zuhauf – und jetzt? In: Zukunftsblog | ETH Zürich. https://ethz.ch/de/news-und-veranstaltungen/eth-news/ news/2020/01/blog-anna-jobin-ki-richtlinien.html","Jobin, A. / Ienca, M. / Vayena, E. (2019): The global landscape of AI ethics guidelines. In: Nature Machine Intelligence, 1, S. 389–399. https://doi. org/10.1038/s42256-019-0088-2","Mittelstadt, B. (2019): Principles alone cannot guarantee ethical AI. In: Nature Machine Intelligence, 1, S. 501–507. https://doi.org/10.1038/ s42256-019-0114-4","O'Neil, C. (2016): Weapons of Math Destruction: How Big Data Increases Inequality and threatens Democracy. Crown, New York NY","Vaidhyanathan, S. (2011): The Googlization of Everything: (And why we should worry). University of California Press, Berkeley CA.","Vaidhyanathan, S. (2018): Anti-Social Media: How Facebook Disconnects Us and Undermines Democracy. Oxford University Press, Oxford","Whittlestone, J. / Nyrup, R. / Alexandrova, A. / Cave, S. (2019): The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, S. 195–200. https://doi.org/10.1145/3306618.3314289"]}

Contexts

The aim of this report is to support the Competence Center Citizen Science Zurich (CCCS). Starting out from the point of research oversight in the Swiss context, it gives an overview of existing ethics guidelines in citizen science as promoted by the Citizen Science Association (CSA), the European Citizen Science Association (ECSA) and similar organizations. It highlights the relative lack of attention to ethical governance and processes in current citizen science guidelines and recommends to set up an intra-institutional process to deal with ambiguities in the future.

In response to the accelerating potential of artificial intelligence (AI) to transform our lives, various governments, multilateral bodies, and other organizations have produced high-level principles and guidelines for the ethical use of AI in recent years. Despite the staggering number of such documents (over 90 by October 2019), there appears to be a relatively high degree of convergence on the level of principles. «Inclusiveness» is one of just a handful of principles that most actors seem to agree upon. However, a closer look reveals that the principle is interpreted very differently in terms of the domain, scope and actors it pertains to. As the global community now works on transitioning from principle to practice, there is a clear need to specify what the principles mean in context and how they can be operationalized and evaluated. To respond to this gap, the swissnex Network, foraus, and AI Commons launched the global campaign «Towards an Inclusive Future in AI» with foraus’ new Policy Kitchen methodology. This joint experiment resulted in 11 workshops in 8 countries, involving 10 partner organizations and about 120 participants from a wide range of perspectives, who collaboratively generated 43 ideas for an inclusive future in AI. The preliminary output was presented at the AI for Good Global Summit 2019. This paper presents a more in-depth exploration of ideas and proposals on inclusion collected during the participatory process.

Geschichte und Informatik = Histoire et informatique, 20, ISSN:1420-5955, Enjeux politiques du web: approches historiques. Das Web als politische Herausforderung: historische Perspektiven

Freiheit. Grundwert in Bedrängnis, ISBN:978-3-03919-487-2, ISBN:978-3-03919-955-6

Tomatoes, grapes, and bread can be ‘organic.’ But search results? Anna Jobin and Malte Ziewitz wonder about the currency of agricultural metaphors in web search and show how they do different work for different users.

Tomatoes, applesand bread can be ‘organic.’ But search results? Anna Jobin and Malte Ziewitz wonder about the currency of agricultural metaphors in web search and show how they do different work for different users.This article is part of our blog series How metaphors shape the digital society.

Algorithmen sind nicht neutral, sondern bilden Wertvorstellungen ab. Oft sind wir uns deren Einflusses jedoch gar nicht bewusst und können somit auch keine Mitsprache einfordern. Dies wäre aber zentral in einer Gesellschaft, in der Informationsverarbeitung und -konsum zunehmend automatisiert sind.

Review of "Online Evaluation of Creativity and the Arts" by Hiesun Cecilia Suhr (ed.)

Review of "Online Evaluation of Creativity and the Arts" by Hiesun Cecilia Suhr (ed.)

Google's linguistic prosthesis have become common mediators between our intended queries and their actual expressions. By correcting a mistyped word or extending a small string of letters into a statistically plausible continuation, Google offers a valuable service to users. However, Google might also be transforming a keyword with no or little value into a keyword for which bids are more likely. Since Google's word bidding algorithm accounts for most of the company's revenues, it is reasonable to ask whether linguistic prosthesis are biased towards commercially more interesting expressions. This study describes a method allowing for progressing in this understanding. Based on an optimal experiment design algorithm, we are reconstructing a model of Google's autocompletion and value assignment functions. We can then explore and question the various possible correlations between the two functions. This is a first step towards the larger goal of understanding how Google's linguistic economy impacts natural language.