Will Science Remain Human in the Era of Technological Innovation?

12 Mar 2018

Can machines substitute scientists in the crucial aspects of scientific practice? Scientific practice takes place in a context increasingly populated and executed by machines, and the objects of study are themselves increasingly constructed and identified by means of technology.

STI Experts Meetings

Technology is pervading known disciplines, and technology-based fields that weren’t recognized as sciences (e.g., robotics) now are. This current explosion of technological tools for scientific research (and of technological drivers thereof) seems to call for a renewed understanding of the human character of science.

While technically advanced analyses attempt to tackle the issue of unreliable or unelaborable data, we want to pose a deeper question having to do with science as a human activity: scientific knowledge runs the risk of being represented in simplified way, hiding the human responsibility, freedom, creativity and choice of observables and explananda that has always characterized it. Critical thinking about the reliability and meaningfulness of data and information, if tackled from this point of view, acquire renewed urgency, depth and complexity.

At the Experts Meeting “Will Science Remain Human? Frontiers of the Incorporation of Technological Innovations in the Bio-Medical Sciences,” at the Campus Bio-Medico University of Rome (March 5-6, 2018), a dozen top scholars delved into these questions. Their presentations, summarized here, focused on four macro areas.

  1. Can Discovery Be Automated?

Paul Humphreys examined three concerns about science’s infiltration by technology – concerns about understanding, error, and applications.  He used supervised and unsupervised machine learning methods applied to deep neural nets as an example of how these concerns can and cannot be addressed.

Emanuele Ratti talked about Machine Learning as a tool that does not generate new knowledge but rather identifies instances already present and codified knowledge. Machine Learning is not independent from human beings and cannot form the basis of automated science.

Fridolin Gross focused on formal/computational and informal/non- computational approaches. He claims they do not represent mutually exclusive approaches to science, but are often combined in practice, and may support each other in various ways. Any account of the impact of computational methods in biology must therefore also investigate the interactions between those different ‘cognitive styles.’

Mieke Boon questioned if human-made scientific knowledge - and the scientist’s role in developing it - remain crucial. Mightn’t arbitrary algorithms - provided by machine-learning technologies that construct relationships between data-input-output - replace humans once they outperform humans in crucial epistemic criteria such as empirical adequacy, reliability and relevance? Empiricism gives reason to believe that machines will ultimately make scientists superfluous. Yet empiricism is flawed, since it does not account for why humans need knowledge.

  1.  Knowledge Justification and Trust-building.

Sandra D. Mitchell argued that increasing use of artificial intelligence technologies that “extend” beyond human cognitive capacities has generated new questions for philosophers of science.  She investigated AI from two stances: as instrument, and as perspective. Does AI provide just another instrument for humans to use in gaining scientific knowledge?  Or if AI technologies produce results that are not mere extensions of human abilities but substantially different ways of reasoning, can we treat them as additional perspectives on a given scientific problem, as we do with different experimental protocols or different modelling assumptions?

Giuseppe Longo focused on current Internet technologies as the result of an original assembly of old and new technologies, whose networking interaction produces novelties. An analysis of some key aspects of this technological blend may help to understand the emergence of unpredictable features and their effects on communicating human communities. Yet, these phenomena are not uniquely determined by the technological infrastructure, but also depend on the underlying social (and political) trends.

Eric Winsberg claimed that when we consider the work of those who model highly complex non- linear systems, the best we can hope for is to arrive at a situation where “a simulation modeler could explain to his peers why it was legitimate and rational to use a certain approximation technique to solve a particular problem” by appealing to “very context-specific reasons and particular features.” If this is the case, it suggests the prospects of science ‘remaining human’ are bleak.

Barbara Osimani explored the potential of formal epistemology. It can provide a higher order and normative perspective on methodological issues (such as replication, reliability, bias), which permits tracking the interplay of various dimensions of evidence (coherence, strength, structure) among themselves and with respect to the characteristics of the measuring instrument (reliability, dependency of measurements). Furthermore, it can incorporate meta-­‚Äźevidential considerations such as the trustworthiness of the source as a function of possible conflict of interests or other features.

  1.  Human Values in Science.

Christopher Tollefsen looked more closely at the relationship between science — “good science” – and morality. This relationship exists, he argues, on at least three axes that he calls the external ethics of science, the social ethics of science, and the internal ethics of science. Of these, only the first – the external ethics of science – suggests that good science may not necessarily be good morally. And if his accounts of the social and internal ethics of science are correct, then science is a fully human action, to be governed by sound practical reasoning, both individually and communally; so understood, its ‘human character’ is essential to its continued existence and a professionalized set of practices.

  1. What Human Science Is Possible?

Francesco Bianchini focused on how digital tools and devices extend our bodies. The virtual body, participated in by institutions and statistically connected with other virtual bodies, would combine with the real body to form a whole extended body, subject to a new kind of manipulation and control. What changes should we expect to arise from such developments? Would they be able to influence our bodily responses to external stimuli, both environmental and social? Most likely, biomedical scientific research will benefit from the rise of such frameworks, which are in turn allowed by parallel research in cognitive science and epistemology, where human and non-human elements closely coexist.

Benjamin Hurlbut explored how biosciences have configured biotechnology as aligning with rather than violating the human. He examined three registers in which these modes have played out in the history of biotechnology: in characterizing and governing risk, in programmatic conceptual reorienting of biological knowledge’s purpose (from ontological description to forms of control); and in debating the appropriate conceptual and discursive starting-point for the evaluation of the potential of biologically transformative techniques to serve rather than violate human integrity. These sites are consequential not only for the practical and normative character of the biosciences, but as loci in which socially shared imaginations of human integrity are at stake—and with them the capacity to ask whether and in what ways science remains human.

Alfredo Marcos asserted that ideologizing and dehumanizing techno-science rely on an oversimplified ontology and a misguided anthropology. Marcos champions reversing the process and stressing the indispensable role of the person in the production of techno-science.  Techno-science, he claims, makes sense and becomes valuable within a wider human horizon. Its dehumanization, on the contrary, condemns it to stupidity and sterility, and probably in the end to its own decadence or demise.

The final discussion revealed the need for humble, self-reflexivity training. Mariachiara Tallacchini suggested constructing public discursive ‘routes’ to create conditions for a public debate about transparency, openness and participatory design in order to gain knowledge relevant to decision making and the democratization of science. So, to keep science-and-society human does not necessarily require redefining ‘human nature,’ but rather cultivating the human capabilities we wish to maintain.