To better navigate social interactions, we routinely (consciously or unconsciously) categorize people based on their distinctive features. One important way we do this is by ascribing political orientations to them. For example, based on certain behavioral cues, we might perceive someone as politically liberal, progressive, conservative, libertarian, Marxist, anarchist, or fascist. Although such ascriptions may appear to be mere descriptions, I argue that they can have deeper, regulative effects on their targets, potentially politicizing and polarizing them in ways that remain (...) underexplored in research on political polarization. To capture this dynamic, I introduce the notion of ‘politicizing mindshaping,’ distinguish it from other types of politicization, and review evidence suggesting that politicizing mindshaping is likely common. Finally, I contend that the pernicious effects of politicizing mindshaping may currently be significantly underestimated. This is because, unlike in many other cases of social labeling, negativity in political labeling is, to some extent, widely tolerated – even encouraged – and largely unconstrained by social norms in democratic countries. (shrink)

Does algorithmic political bias contribute to an entrenchment and polarization of political positions? Franke argues that it may do so because the bias involves classifications of people as liberals, conservatives, etc., and individuals often conform to the ways in which they are classified. I provide a novel example of this phenomenon in human–computer interactions and introduce a social psychological mechanism that has been overlooked in this context but should be experimentally explored. Furthermore, while Franke proposes that algorithmic political classifications entrench (...) political identities, I contend that they may often produce the opposite result. They can lead people to change in ways that disconfirm the classifications. Consequently and counterintuitively, algorithmic political bias can in fact decrease political entrenchment and polarization. (shrink)

Many philosophers hold that generics (i.e., unquantified generalizations) are pervasive in communication and that when they are about social groups, this may offend and polarize people because generics gloss over variations between individuals. Generics about social groups might be particularly common on Twitter (X). This remains unexplored, however. Using machine learning (ML) techniques, we therefore developed an automatic classifier for social generics, applied it to 1.1 million tweets about people, and analyzed the tweets. While it is often suggested that generics (...) are ubiquitous in everyday communication, we found that most tweets (78%) about people contained no generics. However, tweets with generics received more “likes” and retweets. Furthermore, while recent psychological research may lead to the prediction that tweets with generics about political groups are more common than tweets with generics about ethnic groups, we found the opposite. However, consistent with recent claims that political animosity is less constrained by social norms than animosity against gender and ethnic groups, negative tweets with generics about political groups were significantly more prevalent and retweeted than negative tweets about ethnic groups. Our study provides the first ML-based insights into the use and impact of social generics on Twitter. (shrink)

In this paper, we argue that because of the advent of Artificial Intelligence, the secret ballot is now much less effective at protecting voters from voting related instances of social ostracism and social punishment. If one has access to vast amounts of data about specific electors, then it is possible, at least with respect to a significant subset of electors, to infer with high levels of accuracy how they voted in a past election. Since the accuracy levels of Artificial Intelligence (...) are so high, the practical consequences of someone inferring one’s vote are identical to the practical consequences of having one’s vote revealed directly under an open voting regime. Therefore, if one thinks that the secret ballot is at least partly justified because it protects electors against voting related social ostracism and social punishment, one should be morally troubled by how Artificial Intelligence today can be used to infer individual electors’ past voting behaviour. (shrink)

The emergence of new techniques in both microbial biotechnology and artificial intelligence (AI) is opening up a completely new field for monitoring and sometimes even controlling the evolution of pathogens. However, the now famous generative AI extracts and reorganizes prior knowledge from large datasets, making it poorly suited to making predictions in an unreliable future. In contrast, an unfamiliar perspective can help us identify key issues related to the emergence of new technologies, such as those arising from synthetic biology, whilst (...) revisiting old views of AI or including generative AI as a generator of abduction as a resource. This could enable us to identify dangerous situations that are bound to emerge in the not-too-distant future, and prepare ourselves to anticipate when and where they will occur. Here, we emphasize the fact that amongst the many causes of pathogen outbreaks, often driven by the explosion of the human population, laboratory accidents are a major cause of epidemics. This review, limited to animal pathogens, concludes with a discussion of potential epidemic origins based on unusual organisms or associations of organisms that have rarely been highlighted or studied. (shrink)

This article uses critical discourse analysis to investigate artificial intelligence (AI) generated images of aged care nurses and considers how perspectives and perceptions impact upon the recruitment and retention of nurses. The article demonstrates a recontextualization of aged care nursing, giving rise to hidden ideologies including harmful stereotypes which allow for discrimination and exploitation. It is argued that this may imply that nurses require fewer clinical skills in aged care, diminishing the value of working in this area. AI relies on (...) existing data sets, and thus represent existing stereotypes and biases. The discourse analysis has highlighted key issues which may further impact upon nursing recruitment and retention, and advocates for stronger ethical consideration, including the use of experts in data validation, for the way that aged care services and nurses are depicted and thus valued. (shrink)

This short commentary on Peters identifies the entrenchment of political positions as one additional concern related to algorithmic political bias, beyond those identified by Peters. First, it is observed that the political positions detected and predicted by algorithms are typically contingent and largely explained by “political tribalism”, as argued by Brennan. Second, following Hacking, the social construction of political identities is analyzed and it is concluded that algorithmic political bias can contribute to such identities. Third, following Nozick, it is argued (...) that purist political positions may stand in the way of the pursuit of all worthy values and goals to be pursued in the political realm and that to the extent that algorithmic political bias entrenches political positions, it also hinders this healthy “zigzag of politics”. (shrink)

This paper examines how metaphors shape our thinking about and conceptualizing of artificial intelligence (AI), noting that their inherent imprecision leads to discrepancies in our understanding and objectives for AI. By exploring the concept of 'bad metaphors' that equate artificial intelligence with human intelligence, paper argues that these metaphors often carry additional, unintended meanings that distort our understanding and expectations of AI. The terms “artificial” and “intelligence” themselves are ambiguous and ideologically loaded, contributing to the complexity. The paper critiques the (...) anthropocentric and mechanistic metaphors, like “AI as human” and “brain-computer,” which perpetuate unrealistic expectations. By deconstructing these metaphors within broader cultural and historical contexts, the paper calls for a more nuanced and precise understanding of AI, moving beyond simplistic and potentially misleading analogies. (shrink)

Why hasn’t democracy been promoted by nor ICT been controlled by democratic governance? To answer this question, this research begins its investigation by comparing knowledge acquisition systems throughout history: orality, literacy, hyperlink, and hyperlead. “Hyperlead” is a newly coined concept to emphasize the passivity of people when achieving knowledge and information via algorithmic recommendation technologies. Subsequently, the four systems are compared in terms of their epistemological characteristics and political implications. It is argued that, while literacy and hyperlink contributed to the (...) furthering of democracy, hyperlead poses a fundamental challenge to it, undermining human autonomy to make decisions and aggravating vertical and lateral polarizations. In addition, the similarity between orality and hyperlead is addressed. Finally, suggestions to improve or to advert the current trend are provided. What happened during the transition period from orality to literacy and subsequently to hyperlink could be a reference for an alternative to hyperlead. Some technical adjustments and appropriate regulations requiring more transparency in algorithmic recommendation systems would help us to overcome hyperlead and preserve human autonomy. (shrink)