My research occurs at the nexus of philosophy of science, environmental philosophy, and social and political thought. There are three themes to my current work, which I describe in more depth below. Still, certain features unite this work. The first is a concern with environmental science, particularly climate science. Second, my scholarship demonstrates a commitment to socially engaged research. My projects are often collaborations with other philosophers or practicing scientists, and several of my writings speak to non-philosophic audiences, including scientists and policy makers. The reason why my research is personally satisfying is that it furthers discussions in philosophy while simultaneously contributing to broader interdisciplinary conversations in a way that could have positive social impacts.

Theme 1: The Epistemology of Data Production and Computer Simulation
Scientists are thought of as central to scientific investigation and analysis. They go into the field, set up experiments, analyze data, etc. Computational technologies, however, have drastically changed the way scientists operate, and are beginning to displace the scientist from the center of scientific investigation. Computer simulations have made certain kinds of physical experiments obsolete. Machine learning has automated data analysis and phenomena detection, and cloud computing has enhanced the value of data by permitting its travel between research groups across the world. Yet, these new methods also raise new questions: When are computer simulations reliable enough for our purposes, and how would we know? What are the drawbacks of automated data analysis? Do the ways data travel favor certain kinds of research or researchers, and are those preferences justifiable?

My research answers the above questions in an effort to not only produce a better understanding of computer-assistant knowledge production, but also to help design better computational systems for scientific investigation. I have developed an account of empirical information that shows why, and in what circumstances, computer simulations can produce results on par with traditional physical experiments (Lusk 2016). I have applied this account to the case of remote sensing of atmospheric trace gases, demonstrating that explanations of data can play a crucial role in phenomenon identification, which has been denied by a longstanding and popular philosophic approach (Lusk forthcoming, cf. Bogen and Woodward 1988). My ongoing research in this area is currently supported by a National Science Foundation grant for “A Methodological Study of Big Data and Atmospheric Science” (Co-PI, award #1754740, $500,738). As part of this grant, my work is assessing the virtues and vices of adopting certain big data techniques in climate science, in part by comparing the computational methods used in climate science to other areas of science, like model organism research, that already widely employ big data techniques (see Lloyd, Lusk, Gluck, McGinnis forthcoming). In response to a critique of philosophical conceptions of data (Leonelli 2016), my next piece of work on this theme will be to develop a representational conception of data capable of accounting for the way data is used in big data and machine learning applications.

Theme 2: Science, Values, and Democracy 
In the current political moment, any account which takes scientific information seriously requires examining its social use. My research explicitly makes connections between data and its use through consideration of social (and epistemic) values. In short, social values pick out the desires or preferences of individuals or groups, where epistemic values pick out features of scientific objects (e.g. theories or models) thought to be indicators of truth or empirical adequacy. There is now broad consensus that social values can permissibly influence the “internal” aspects of science (e.g. hypothesis or theory testing), particularly when data underdetermine the selection of a preferred theory or model, or when there is uncertainty regarding the best way to collect data. However, no consensus exists regarding which set of social values (or whose values) should be employed in these decisions. This is an important question. The encroachment of social values on science can be pernicious: manipulations of science have been employed by political groups and corporations to resist regulations on smoking, climate change, and vaccines.

My work develops a novel approach to the question of whose values should be employed in scientific work by reframing the issue as one of political legitimacy. In broad strokes, I argue that the insertion of values into science that are not the result of democratic deliberation are normatively illegitimate to employ, that is, they have an undue influence on democratic decision-making that violates the fundamental principles of democracy (Lusk 2020, see also Parker and Lusk 2019). My work crafts a political and particularly deliberative democratic response to whose values are permissible in scientific reasoning. The novel political approach I am developing represents a significant departure from standard philosophical views: as of yet, few philosophers have looked to political theory to begin to answer questions about values in science. Connecting political theory and philosophy of science opens a new and interdisciplinary direction for analyzes, a direction I will further explore in the coming years.

Theme 3: Scientific Information in Climate Policy
This research theme unites the first two to produce socially engaged philosophical work with the potential for influencing climate policy. Climate science is a complex endeavor: the data that underwrite claims in climate science often come from sophisticated computer models whose reliability is difficult to assess. How can this data be responsibly used in climate policy in a way that maintains both scientific and political integrity? My work helps to address this question.

Much of my research on this theme examines scientists’ attempts to attribute extreme weather events to human action by evaluating their potential for promoting climate justice. Extreme weather attribution has been motivated by the desire to compensate or protect those harmed (or likely to be harmed) by anthropogenically induced events. However, I argue that scientists overstate the social utility of extreme weather attribution (Lusk 2017). The general difficulty is that extreme weather attribution does not provide information demanded by those it seeks to serve, often because it only examines meteorological risk and is silent about the intricate social factors at work in a community (Lusk 2017, 2020, Forthcoming). Hence, climate policies that seek to distribute funds for adaptation, or recognize the moral status of victims, based solely on weather attribution data, are insufficient to achieve climate justice. By reducing the impacts of extreme weather to merely the odds of the event occurring, such policies ignore the ethically important actions that made certain societies susceptible to extreme weather. More recently however, my work has shifted to examining the social costs of carbon. I’ve begun, as part of a transdisciplinary collaboration with geophysicists, to take seriously the notion of intergenerational justice by modeling the economic costs of current carbon emissions over a million-year timescale (Archer, Kite and Lusk 2020). When it comes to extreme event attribution, I’ve been credited as the first to robustly examine the legal utility of event attribution, and to my knowledge, my collaboration on the social cost of carbon is the first to examine the economic impacts of carbon use across geological time. My future work will continue this style of interdisciplinary research that targets scientific audiences and decision makers.

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