Buddhika Nettasinghe, Nazanin Alipourfard, Vikram Krishnamurthy, Kristina Lerman
Structural inequalities persist in society, conferring systematic advantages to one group of people, for example, by giving them substantially more influence and opportunities than others. Using bibliometric data about authors of scientific publications from six different disciplines, we first present evidence for the existence of two types of citation inequalities. First, female authors, who represent a minority in each discipline, receive less recognition for their work relative to male authors; second, authors affiliated with top-ranked institutions, who are also a minority in each discipline, receive substantially more recognition compared to other authors. We then present a dynamic model of the growth of directed citation networks and show that such systematic disparities in citations can arise from individual preferences to cite authors in the same group (homophily) or the other group (heterophily), highly cited or active authors (preferential attachment), as well as the size of the group and how frequently new authors join. We analyze the model theoretically and show that its predictions align well with real-world observations. Our theoretical and empirical analysis sheds light on potential strategies to mitigate structural inequalities in science. In particular, we find that merely making group sizes equal does little to narrow the disparities. Instead, reducing the homophily of each group, frequently adding new authors to a research field while providing them an accessible platform among existing, established authors, together with balanced group sizes can have the largest impact on reducing inequality. Our work highlights additional complexities of mitigating structural disparities stemming from asymmetric relations (e.g., directed citations) compared to symmetric relations (e.g., collaborations).

Read the full article at: arxiv.org

Joshua Bongard and Michael Levin

Front. Ecol. Evol., 16 March 2021

One of the most useful metaphors for driving scientific and engineering progress has been that of the “machine.” Much controversy exists about the applicability of this concept in the life sciences. Advances in molecular biology have revealed numerous design principles that can be harnessed to understand cells from an engineering perspective, and build novel devices to rationally exploit the laws of chemistry, physics, and computation. At the same time, organicists point to the many unique features of life, especially at larger scales of organization, which have resisted decomposition analysis and artificial implementation. Here, we argue that much of this debate has focused on inessential aspects of machines – classical properties which have been surpassed by advances in modern Machine Behavior and no longer apply. This emerging multidisciplinary field, at the interface of artificial life, machine learning, and synthetic bioengineering, is highlighting the inadequacy of existing definitions. Key terms such as machine, robot, program, software, evolved, designed, etc., need to be revised in light of technological and theoretical advances that have moved past the dated philosophical conceptions that have limited our understanding of both evolved and designed systems. Moving beyond contingent aspects of historical and current machines will enable conceptual tools that embrace inevitable advances in synthetic and hybrid bioengineering and computer science, toward a framework that identifies essential distinctions between fundamental concepts of devices and living agents. Progress in both theory and practical applications requires the establishment of a novel conception of “machines as they could be,” based on the profound lessons of biology at all scales. We sketch a perspective that acknowledges the remarkable, unique aspects of life to help re-define key terms, and identify deep, essential features of concepts for a future in which sharp boundaries between evolved and designed systems will not exist.

Read the full article at: www.frontiersin.org

A remarkable AI can write like humans — but with no understanding of what it’s saying.

Read the full article at: www.nature.com