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Mel Andrews
<!doctype html> <html lang="en"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1"> <link rel="shortcut icon" type="image/png" href="images/favicon.png"/> <link href="css/bootstrap.min.css" rel="stylesheet"> <link href="css/styles.css" rel="stylesheet"> <title>Mel Andrews</title> </head> <body> <nav class="navbar navbar-expand-lg navbar-light fixed-top"> <div class="container-fluid"> <button class="navbar-toggler bg-left ms-auto" type="button" data-bs-toggle="collapse" data-bs-target="#mobile-nav" aria-controls="mobile-nav" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="collapse navbar-collapse" id="mobile-nav"> <ul class="navbar-nav ms-auto mt-5 mb-lg-0 me-1"> <li class="nav-item"> <a class="nav-link active" aria-current="#page" href="#about">about</a> </li> <li class="nav-item"> <a class="nav-link" href="#research">research</a> </li> <li class="nav-item"> <a class="nav-link" href="#teaching">teaching</a> </li> <li class="nav-item"> <a class="nav-link" href="https://github.com/melandrews/mel-andrews.com/blob/master/Andrews_CV.pdf">cv</a> </li> <li class="nav-item"> <a class="nav-link" href="#contact">contact</a> </li> </ul> </div> </div> </nav> <div class="spacer"></div> <div class="container-fluid"> <div class="row"> <a id="about" class="anchor"></a> <div class="col-12 col-lg-6 bg-left"> <div class="content mx-auto mt-5"> <h1 class="brand-text">MEL ANDREWS</h1> <div class="subtitle"> University of Cincinnati Carnegie Mellon University </div> <hr /> <p> As an interdisciplinary scholar grounded in AI ethics and philosophy of science, I aim to characterise the role of mathematical and computational tools---particularly, machine learning---in science and society towards the ends of promoting ethically and epistemically sound use. How the world works is a direct (if non-exclusive) function of how the mathematical and computational tools we use to understand and intervene upon the world work. How we utilize mathematical and computational tools to understand, predict, and control the world depends, in turn, on what we make of these tools; what we take their epistemic status and purchase over nature to be. Getting the characterisation of machine learning (ML) based systems, as epistemic technologies, right is a requisite first step in ensuring that their use is to the benefit, not the detriment, of the human populations they interface with. </div> </div> <div class="col-12 col-lg-6 d-flex align-items-center justify-content-center green-background"> <div class="content"> <img class="rounded-circle m-5" src="images/portrait.jpg" alt="Mel Andrews" /> </div> </div> </div> <div class="row"> <a id="research" class="anchor"></a> <div class="col-12 col-lg-6 bg-left"> <div class="content mx-auto mt-5"> <div class="subtitle"> RESEARCH QUESTIONS </div> <hr /> <div class="subtitle"> Making AI Beneficial </div> <hr /> <p> The methods of ML are rapidly taking over consequential real-world decision-making. The use of these tools in socially-sensitive contexts can, and often does, result in social harms, prompting the emergence of the field of ML ethics. There is, however, a gaping need for philosophical clarity within this new scholarship. Some of this must come from normative theorists, social and political philosophers, and, in particular, philosophers literate in legal doctrine. Equally important, and often passed over, is the need for philosophers conversant in the technical details of the ML-based tools utilised in consequential social decision-making; philosophers who are capable of clarifying the epistemic status of the methods of ML and differentiating between those issues which are irreducibly normative in nature and those which concern the functionality of these tools as means of securing knowledge about the world---facets often conflated in these discussions. Disputes currently being waged in the sphere of ML/AI ethics over predicting and apportioning risks and opportunities under conditions of uncertainty are not new. Many of them unwittingly recapitulate debates that took place within the quantitative social sciences in the mid-century, debates that occurred in the 19th century over the foundational assumptions governing social welfare and insurance, and debates dating back to the very origins of statistics and probability theory---domains which, since their incipience, have fundamentally concerned precisely the socially-oriented deliberations which currently animate ML ethics. The field of ML ethics therefore stands to benefit in critical ways from the perspective of history and philosophy of science and mathematics. </p> <div class="subtitle"> Machine Learning in Science </div> <hr /> <p> My research projects in this vein include 1. evaluating the supposed atheoreticity of machine learning methods when compared to the scientific use of classical mathematical or statistical tools, 2. investigating the ability of unsupervised learning (UL) methods, especially clustering techniques, to carve at joints of nature, 3. characterising DL methods as an applied mathematics singularly equipped to countenance complex phenomena, and 4. critiquing and offering alternatives to proposed epistemic norms for ML/DL (including explainability and interpretability). The first of these projects has resulted in a paper, <a href="https://philsci-archive.pitt.edu/22690/1/ML_Atheoreticity.pdf">The Devil in the Data: Machine Learning & the Theory-Free Ideal</a>, currently archived and under review. The remaining projects are avenues of ongoing research. </p> <div class="subtitle"> The Use of Mathematics in Science </div> <hr /> <p> Traditional philosophical accounts of models and applied mathematics treat worldly phenomena and mathematical representations thereof as though they were, fundamentally, the same sort of thing---or sufficiently alike as to render subjecting them to straightforward comparison a sensible activity. From the confines of such a framework, philosophy renders itself incapable of differentiating between aspects of the reality science aims to approximate and features of the conceptual tools scientists wield towards these epistemic ends. The most prevalent and insidious errors made in applying and interpreting mathematics, errors of reification, flow from the inability to appropriately distinguish representational content from representational medium. And yet, from the vantage point of most going philosophical accounts of the math-territory relation, no such distinction can be drawn. Armed with traditional accounts of modelling, philosophers of science are therefore unable to weigh-in on the misuse of mathematical and computational tools. In contrast, the <i>cognitive prosthesis</i> account of applied mathematics I offer renders such epistemic errors visible and elucidates modelling best-practices. I develop and apply this account to many methodological disputes concerning the use of formalism in science in ongoing work. A chapter exploring the implications of my account for both first-order debates in philosophy of science and methodological considerations for philosophers engaging mathematical practice is solicited and in-preparation for a book entitled <i>Philosophy of Science: A User’s Guide</i>, edited by Sophie Veigl and Adrian Currie (MIT Press). An application of this view to a dispute in neuroscientific practice was put forward in a paper published in <i>Biology & Philosophy</i>: <a href="https://link.springer.com/article/10.1007/s10539-021-09807-0">The Math is Not The Territory: Navigating the Free Energy Principle.</a> </p> </div> </div> <div class="col-12 col-lg-6 green-background"> <div class="subtitle mx-auto mt-5"> RECORDED TALKS </div> <hr /> <div class="content mt-5 mx-auto"> <a href="https://youtu.be/u3fUXAD1tuo" target="_blank"> <img class="img-fluid mt-3" src="images/television.jpg" alt="Research Picture" /> </a> <a href="https://youtu.be/gQQyd8Hd3AA" target="_blank"> <img class="img-fluid mt-5" src="images/television_2.jpg" alt="Other Picture" /> </a> </div> </div> </div> <div class="row"> <a id="teaching" class="anchor"></a> <div class="col-12 col-lg-6 bg-left"> <div class="content mx-auto mt-5"> <div class="subtitle"> TEACHING </div> <hr /> <p> <br />2022, Spring Term – Co-instructor <br />Philosophical Foundations of Machine Intelligence <br />Carnegie Mellon University </p> <p> <br />2021, Fall Term – Primary Instructor <br />Introduction to Cognitive Studies <br />University of Cincinnati </p> <p> <br />2021, Summer Term – Primary Instructor <br />Introduction to Cognitive Studies <br />University of Cincinnati </p> <p> <br />2021, Spring Term – Primary Instructor <br />Medical Ethics, focus on algorithmic injustice <br />& machine learning in medicine <br />University of Cincinnati </p> <p> <br />2020, Fall Term – Teaching Assistant <br />Contemporary Moral Issues, focus on Bioethics <br />University of Cincinnati </p> <p> <br />2020, Spring Term – Teaching Assistant <br />Introduction to Cognitive Studies <br />University of Cincinnati </p> <p> <br />2019, Fall Term – Teaching Assistant <br />Introduction to Philosophy <br />University of Cincinnati </p> <p> <br />2019, Summer Term – Instructor <br />EVOS Seminar Series <br />Focus on Natural History and the Tree of Life <br />Binghamton University </p> <p> <br />2017, Spring Term – Teaching Assistant <br />Child Development with David Henry Feldman <br />Tufts University </p> <p> <br />2018, Winter Term – Co-instructor <br />EVOS Seminar Series, focus on Science Communication <br />Binghamton University </p> <p> <br />2017, Summer Term – Co-instructor <br />EVOS Seminar Series, <br />focus on Biological Individuality & Identity <br />Binghamton University, </p> </div> </div> <div class="col-12 col-lg-6 green-background"> <div class="content"> </div> </div> </div> <div class="row"> <a id="cv" class="anchor"></a> <div class="col-12 col-lg-6 bg-left"> <div class="content mx-auto mt-5"> <div class="subtitle"> CURRICULUM VITAE <iframe style="height: 30%" src="Andrews_CV_2023.pdf" frameborder="0"></iframe> </div> </div> </div> <div class="col-12 col-lg-6 green-background"> <div class="content"> </div> </div> </div> <div class="row"> <a id="contact" class="anchor"></a> <div class="col-12 col-lg-6 bg-left"> <div class="content mx-auto mt-5"> <div class="subtitle"> CONTACT </div> <hr /> <div class="row links mb-3 mt-5"> <div class="col-3 text-center"> <img src="images/email.png" alt="Email" /> </div> <div class="col mt-3"> <p><a href="mailto:mel.andrews@tufts.edu">mel.andrews@tufts.edu</a></p> </div> </div> <div class="row my-3"> <div class="col-3 text-center"> <img src="images/google-scholar.png" alt="Google Scholar" /> </div> <div class="col mt-3"> <p><a href="https://scholar.google.de/citations?user=aEh_jKcAAAAJ&hl=en" target="_blank">Google Scholar</a> </p> </div> </div> <div class="row my-3"> <div class="col-3 text-center"> <img src="images/research-gate.png" alt="Researchgate" /> </div> <div class="col mt-3"> <p><a href="https://www.researchgate.net/profile/Mel-Andrews" target="_blank">Researchgate</a></p> </div> </div> <div class="row my-3"> <div class="col-3 text-center"> <img src="images/twitter.png" alt="Twitter" /> </div> <div class="col mt-3"> <p><a href="https://twitter.com/bayesianboy" target="_blank">@bayesianboy</a></p> </div> </div> <div class="row my-3"> <div class="col-3 text-center"> <img src="images/building.png" alt="Address" /> </div> <div class="col mt-3"> <p>Mel Andrews <br />Department of Philosophy <br />McMicken Hall, <br />2700 Campus Way, <br />Cincinnati, Ohio 45221 </p> </div> </div> <div class="row mt-5 mb-3"> <div class="col footer"> © <script>document.write((new Date).getFullYear());</script> Mel Andrews </div> </div> </div> </div> <div class="col-12 col-lg-6 green-background"> <div class="content"> </div> </div> </div> </div> <script src="js/bootstrap.bundle.min.js"></script> </body> </html>