Rising Stars Speaker Series


Tuesday, March 30, 2021, 12:00pm to 1:30pm



Virtual (See registration links below)





All events will take place 12–1:30pm ET

View videos from the 2021 Rising Stars series here.


Public Health: Tuesday, March 30


Sonali Parbhoo (Harvard University)

LinkedIn: sonaliparbhoo

Image of Sonali Parbhoo

Talk Title:  Robust Machine Learning Methods for Targeted Healthcare




Paidamoyo Chapfuwa (Duke University)



Talk Title:  Counterfactual Survival Analysis with Balanced Representations




Irene Chen (MIT)







Charles C Onu (MILA)







Conservation: Thursday, April 8


Esther Rolf (UC Berkeley)








Elizabeth Bondi (Harvard University)









Sasha Luccioni (MILA)



Talk title: Visualizing the Future Impacts of Climate Change with GANs




Shiva R Iyer (New York University)







Fairness: Tuesday, April 20


Lily Hu (Harvard University)



Talk Title:  Fair Classification and Social Welfare

Abstract:  In this talk, I will connect work in fair classification with some concepts in welfare economics. I take the connection to be rather natural: Now that machine learning algorithms lie at the center of many important resource allocation pipelines, computer scientists have been unwittingly cast as partial social planners. Given this state of affairs, important questions follow. How do leading notions of fairness as defined by computer scientists map onto longer-standing notions of social welfare? Our main findings on the relationship between fairness criteria and welfare center on sensitivity analyses of fairness-constrained empirical risk minimization programs. Most notably, we find that always preferring "more fair" classifiers is in tension with a commitment to the Pareto Principle—a fundamental axiom of social choice theory and welfare economics, which states that we should always prefer allocations that make everyone weakly better-off. Recent work in machine learning has rallied around these notions of fairness as critical to ensuring that algorithmic systems do not have disparate negative impact on disadvantaged social groups. By showing that these constraints often fail to translate into improved outcomes for these groups, we cast doubt on their effectiveness as a means to ensure justice.



Angela Zhou (Cornell University)



Talk Title:  Fairness, Welfare and Equity in Personalized Pricing

Abstract:  We study the interplay of fairness, welfare, and equity considerations in personalized pricing based on customer features. Sellers are increasingly able to conduct price personalization based on predictive modeling of demand conditional on covariates: setting customized interest rates, targeted discounts of consumer goods, and personalized subsidies of scarce resources with positive externalities like vaccines and bed nets. These different application areas may lead to different concerns around fairness, welfare, and equity on different objectives: price burdens on consumers, price envy, firm revenue, access to a good, equal access, and distributional consequences when the good in question further impacts downstream outcomes of interest. We conduct a comprehensive literature review in order to disentangle these different normative considerations and propose a taxonomy of different objectives with mathematical definitions. We focus on observational metrics that do not assume access to an underlying valuation distribution which is either unobserved due to binary feedback or ill-defined due to overriding behavioral concerns regarding interpreting revealed preferences. In the setting of personalized pricing for the provision of goods with positive benefits, we discuss how price optimization may provide unambiguous benefit by achieving a "triple bottom line": personalized pricing enables expanding access, which in turn may lead to gains in welfare due to heterogeneous utility, and improve revenue or budget utilization. We empirically demonstrate the potential benefits of personalized pricing in two settings: pricing subsidies for an elective vaccine, and the effects of personalized interest rates on downstream outcomes in microcredit.



Ana-Andrea Stoica (Columbia University)



Talk Title:  Diversity and inequality in social networks

Abstract:  Online social networks often mirror inequality in real-world networks, from historical prejudice, economic or social factors. Such disparities are often picked up and amplified by algorithms that leverage social data for the purpose of providing recommendations, diffusing information, or forming groups. In this talk, I discuss an overview of my research, involving explanations for algorithmic bias in social networks, briefly describing my work in recommendation algorithms and information diffusion. Using network models that reproduce inequality seen in online networks, we'll characterize the relationship between pre-existing bias and algorithms in creating inequality, discussing different algorithmic solutions for mitigating bias.



Paul Gölz (CMU)



Talk Title:  Fair Algorithms for Selecting Citizens' Assemblies

Abstract:  Globally, there has been a recent surge in citizens’ assemblies, a form of civic participation in which a panel of randomly-selected citizens weighs in on policy questions. Citizens would ideally be selected to serve on this panel with equal probability. In practice, however, this is impossible due to demographic quotas, which are imposed to ensure that panels are representative despite unequal participation rates across subpopulations. The selection algorithms currently used for choosing panels have received little attention, and we find that the one we examine as a benchmark tends to give pool members highly unequal selection probabilities. Here, we develop selection algorithms that satisfy quotas while choosing pool members with probabilities as close to equal as mathematically possible, for a range of fairness metrics that quantify “closeness to equality”. We have also implemented one such algorithm, which has been adopted by a number of organizations around the world. We demonstrate on multiple real-world datasets that our algorithm is substantially fairer than the benchmark.By contributing a fairer, more principled, and deployable algorithm, our work puts the practice of sortition on firmer foundations. Our algorithm is also one of the first applications of ideas from the field of fair division in practice.
Joint work with Bailey Flanigan, Anupam Gupta, Brett Hennig, and Ariel Procaccia


Tech + Society: Thursday, April 29


Amber M. Hamilton (University of Minnesota)



Talk Title:  Platforming Race: Examining racial ideologies on social media platforms

Abstract:  Though social media platforms such as Facebook and Twitter draw billions of daily users, it has been difficult to explore the relationship between the services that platforms provide and their corporate beliefs about race and racism. Indeed, rather than talking about race and racism, these companies prefer to gesture to vague ideals of diversity and inclusion instead. However, the killing of George Floyd by Minneapolis Police on May 25, 2020 led to an outpouring of racial justice solidarity statements, policy changes, and racial justice donations from social media companies. Though it's unclear why these companies felt compelled to respond to this moment of racial upheaval, the cascade of statements provide a unique opportunity to explore the beliefs about race as expressed by social media companies — beliefs that are enacted on their platforms — and provide important context for the bias within technological systems that we've seen in recent years. In this talk, I describe my analysis of these public statements and how these companies can work to design more equitable and fair AI systems.



Brooklyne Gipson (University of Illinois Urbana-Champaign)


brooklyne gipson

Talk Title: TBA

Abstract: TBA



Randi Williams (MIT)



Talk Title:  How to Train Your Robot: Envisioning the future of education

Abstract:  Today Artificial Intelligence (AI) permeates almost every area of society, impacting how many of us learn, seek employment, and relate to one another. As such, we must consider how the automation revolution will impact individuals’ abilities to participate in the future depending on their level of technological literacy. AI Education, particularly in primary through secondary school, presents an opportunity to prepare a diverse citizenry to flourish in an AI-powered society. In the Personal Robots Group at the MIT Media Lab, I create curricula and tools to teach primary and middle school students about AI. Through lessons that introduce AI systems as fundamentally sociotechnical, students develop the skills to imagine and implement personally meaningful AI projects. Along the way, they also build 21st century skills including digital literacy, critical thinking, and applying knowledge. In this talk, I will share the motivations, design principles, and outcomes of K-12 AI education projects I have been involved with.



Dora Demszky (Stanford University)



Talk Title:  Content Analysis of Textbooks via Natural Language Processing: Findings on Gender, Race, and Ethnicity in Texas U.S. History Textbooks

Abstract:  Cutting-edge data science techniques can shed new light on fundamental questions in educational research. We apply techniques from natural language processing (lexicons, word embeddings, topic models) to 15 U.S. history textbooks widely used in Texas between 2015 and 2017, studying their depiction of historically marginalized groups. We find that Latinx people are rarely discussed, and the most common famous figures are nearly all White men. Lexicon-based approaches show that Black people are described as performing actions associated with low agency and power. Word embeddings reveal that women tend to be discussed in the contexts of work and the home. Topic modeling highlights the higher prominence of political topics compared with social ones. We also find that more conservative counties tend to purchase textbooks with less representation of women and Black people. Building on a rich tradition of textbook analysis, we release our computational toolkit to support new research directions.


