Data Feminism

A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed

The Fitness Cost of Antibiotic Resistance in Streptococcus pneumoniae: Insight from the Field

Laboratory studies have suggested that antibiotic resistance may result in decreased fitness in the bacteria that harbor it. Observational studies have supported this, but due to ethical and practical considerations, it is rare to have experimental control over antibiotic prescription rates.We analyze data from a 54-month longitudinal trial that monitored pneumococcal drug resistance during and after biannual mass distribution of azithromycin for the elimination of the blinding eye disease, trachoma. Prescription of azithromycin and antibiotics that can create cross-resistance to it is rare in this part of the world. As a result, we were able to follow trends in resistance with minimal influence from unmeasured antibiotic use. Using these data, we fit a probabilistic disease transmission model that included two resistant strains, corresponding to the two dominant modes of resistance to macrolide antibiotics. We estimated the relative fitness of these two strains to be 0.86 (95% CI 0.80 to 0.90), and 0.88 (95% CI 0.82 to 0.93), relative to antibiotic-sensitive strains. We then used these estimates to predict that, within 5 years of the last antibiotic treatment, there would be a 95% chance of elimination of macrolide resistance by intra-species competition alone.Although it is quite possible that the fitness cost of macrolide resistance is sufficient to ensure its eventual elimination in the absence of antibiotic selection, this process takes time, and prevention is likely the best policy in the fight against resistance

Creatio ex Materia

Creatio ex Materia is a two-movement composition for chamber ensemble that attempts to uncover unique timbres and rhythmic consequences through the use of contemporary concepts and techniques. It employs nontraditional combinations of instruments and blending of extended techniques to craft innovative sounds, while trying to express abstract principles such as the transfer of energy and the illusion of parts influencing and ???learning from??? one another. Additionally, this piece pursues the implications of joining aleatoric methods and graphic notation with more stringent approaches like mathematical process and standard notation. \ud Inception\ud Endeavoring to achieve new sounds with traditional instruments, Creatio ex Materia began as an unlikely trio of oboe, cello, and chimes. This odd combination alone would make for a bold musical statement, however there were obvious balance issues from the outset. An experimental session with a set of chimes and various striking implements led to the use of knitting needles in place of traditional mallets throughout most of the piece. In this manner, the inherent power of the chimes was decreased significantly, resulting in a more uniform mix. \ud The initial sketch included a list of possible extended techniques to use, a proposed instrumentation that built upon the original trio, ideas for creating random aspects and graphic notation, and a pitch set from which to build upon, which was based on the decision to use harmonic partials of the pitch E. Also included within the plan was a double-stop idea for the cello, a skeleton of a melody line for the oboe, and specific adjectives that could describe ways to perform certain random rhythms. Once the piece began to conglomerate, choices were made by sticking close to the guidelines in the sketch. (See more in text.

Seven intersectional feminist principles for equitable and actionable COVID-19 data

This essay offers seven intersectional feminist principles for equitable and actionable COVID-19 data, drawing from the authors' prior work on data feminism. Our book, Data Feminism (D'Ignazio and Klein, 2020), offers seven principles which suggest possible points of entry for challenging and changing power imbalances in data science. In this essay, we offer seven sets of examples, one inspired by each of our principles, for both identifying existing power imbalances with respect to the impact of the novel coronavirus and its response, and for beginning the work of change

Machine learning and monte carlo sampling for the probabilistic orienteering problem

The Probabilistic Orienteering Problem is a stochastic optimization problem about the delivery or goods to customers. Only a subset of the customer can be served in the given time, so the problem consists in the selection of the customers providing more revenues and in the optimization of a truck tour to serve them. The presence of the customers is however stochastic, and this has to be taken into account while evaluating the objective function of each solution. Due to the high computational complexity of such an objective function, Monte Carlo sampling method is used to estimate it in a fast way. There is one crucial parameter in a Monte Carlo sampling evaluator which is the number of samples to be used. More samples mean high precision, less samples mean high speed. An instance-dependent trade-off has to be found. The topic of this paper is a Machine Learning-based method to estimate the best number of samples, given the characteristics of an instance. Two methods are presented and compared from an experimental point of view. In particular, it is shown that a less intuitive and slightly more complex method is able to provide more precise estimations