gov3009-l November 2020

gov3009-l@lists.fas.harvard.edu

1 participants
4 discussions

Gov 3009 Applied Statistics Workshop (12/2) -- Kosuke Imai

by Yamauchi, Soichiro

Hi all, Our final virtual meeting of this semester will be at 12pm (EST) Wednesday, December 2, where we will hear Kosuke Imai (Harvard University) presents research on "Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment." *Abstract*: Despite an increasing reliance on fully-automated algorithmic decision making in our day-to-day lives, human beings still make highly consequential decisions. As frequently seen in business, healthcare, and public policy, recommendations produced by algorithms are provided to human decision-makers in order to guide their decisions. While there exists a fast growing literature evaluating the bias and fairness of such algorithmic recommendations, an overlooked question is whether they help humans make better decisions. We develop a statistical methodology for experimentally evaluating the causal impacts of algorithmic recommendations on human decisions. We also show how to examine whether algorithmic recommendations improve the fairness of human decisions and derive the optimal decisions under various settings. We apply the proposed methodology to the first-ever randomized controlled trial that evaluates the pretrial public safety assessment (PSA) in the criminal justice system. A goal of the PSA is to help judges decide which arrested individuals should be released. We find that the PSA provision has little overall impact on the judge’s decisions and subsequent arrestee behavior. However, our analysis suggests that the PSA may help avoid unnecessarily harsh decisions for female arrestees regardless of their risk levels while it encourages the judge to make stricter decisions for male arrestees who are deemed to be risky. In terms of fairness, the PSA appears to increase the gender bias against males while having little effect on the existing racial biases of the judge’s decisions against non-white males. Finally, we show that PSA’s recommendations are often too severe and can only be justified if the societal cost of a new crime is much higher than the cost of an unnecessarily harsh decision. *Zoom link*: https://harvard.zoom.us/j/99424949004?pwd=aWtPNFM3ZzFYbWxIMXNoZDlyUElVZz09 *Schedule of the workshop*: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Best, Soichiro -- Soichiro Yamauchi PhD candidate Harvard University URL: https://soichiroy.github.io/

3 years, 4 months

Gov 3009 Applied Statistics Workshop (11/18) -- Tyler VanderWeele

by Yamauchi, Soichiro

Hi all, Our next virtual meeting will be at 12pm (EST) Wednesday, November 18, where we will hear Tyler VanderWeele (Harvard University) presents research on "Revisiting Psychometric Theory and Factor Analysis." *Abstract*: The presentation will revisit some of the conceptual and statistical foundations of psychometric measurement theory and factor analysis, specifically addressing the questions: (i) What happens to “factors” when they causally affect one another?, (ii) Is an underlying univariate latent variable a reasonable model for psycho-social constructs?, (iii) What are the testable empirical implications of such a model? and (iv) What alternative interpretations of analyses with constructed measures might be possible? The presentation will be based upon the following three preprints: - VanderWeele, T.J. and Batty, C.J.K. (2020). On the dimensional indeterminacy of one-wave factor analysis under causal effects. <https://arxiv.org/abs/2001.10352> Technical Report. - VanderWeele, T.J. and Vansteelandt, S. (2020). A statistical test to reject the structural interpretation of a latent factor model <https://arxiv.org/abs/2006.15899>. Technical Report. - VanderWeele, T.J. (2020). Causal inference and constructed measures: towards a new model of measurement for psychosocial constructs. <https://arxiv.org/abs/2007.00520> Technical Report. *Zoom link*: https://harvard.zoom.us/j/99424949004?pwd=aWtPNFM3ZzFYbWxIMXNoZDlyUElVZz09 *Schedule of the workshop*: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Best, Soichiro -- Soichiro Yamauchi PhD candidate Harvard University URL: https://soichiroy.github.io/

3 years, 5 months

Gov 3009 Applied Statistics Workshop (11/11) -- Cory McCartan

by Yamauchi, Soichiro

Hi all, Our next virtual meeting will be at 12pm (EST) Wednesday, November 11, where we will hear Cory McCartan (Harvard University) presents research on "Sequential Monte Carlo for Sampling Balanced and Compact Redistricting Plans." *Abstract*: Random sampling of graph partitions under constraints has become a popular tool for evaluating legislative redistricting plans. Analysts detect partisan gerrymandering by comparing a proposed redistricting plan with an ensemble of sampled alternative plans. For successful application, sampling methods must scale to large maps with many districts, incorporate realistic legal constraints, and accurately sample from a selected target distribution. Unfortunately, most existing methods struggle in at least one of these three areas. We present a new Sequential Monte Carlo (SMC) algorithm that draws representative redistricting plans from a realistic target distribution of choice. Because it yields nearly independent samples, the SMC algorithm can efficiently explore the relevant space of redistricting plans than the existing Markov chain Monte Carlo algorithms that yield dependent samples. Our algorithm can simultaneously incorporate several constraints commonly imposed in real-world redistricting problems, including equal population, compactness, and preservation of administrative boundaries. We validate the accuracy of the proposed algorithm by using a small map where all redistricting plans can be enumerated. We then apply the SMC algorithm to evaluate the partisan implications of several maps submitted by relevant parties in a recent high-profile redistricting case in the state of Pennsylvania. Open-source software is available for implementing the proposed methodology. Paper is available from here <https://arxiv.org/pdf/2008.06131.pdf>. *Zoom link*: https://harvard.zoom.us/j/99424949004?pwd=aWtPNFM3ZzFYbWxIMXNoZDlyUElVZz09 *Schedule of the workshop*: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Best, Soichiro -- Soichiro Yamauchi PhD candidate Harvard University URL: https://soichiroy.github.io/

3 years, 5 months

Gov 3009 Applied Statistics Workshop (11/4) -- Yiling Chen

by Yamauchi, Soichiro

Hi all, Our next virtual meeting will be at 12pm (EST) Wednesday, November 4, where we will hear Yiling Chen (Harvard University) presents research on "Unexpected Consequences of Algorithm-in-the-Loop Decision Making." *Abstract*: The rise of machine learning has fundamentally altered decision making: rather than being made solely by people, many important decisions are now made through an “algorithm-in-the-loop” process where machine learning models inform people. Yet insufficient research has considered how the interactions between people and models actually influence human decision making. In this talk, I’ll discuss results from a set of controlled experiments on algorithm-in-the-loop human decision making in two contexts (pretrial release and financial lending). For example, when presented with algorithmic risk assessments, our study participants exhibited additional bias in their decisions and showed a change in their decision-making process by increasing risk aversion. These results highlight the urgent need to expand our analyses of algorithmic decision making aids beyond evaluating the models themselves to investigating the full sociotechnical contexts in which people and algorithms interact. Papers of the talk are available from here <https://scholar.harvard.edu/files/bgreen/files/19-cscw.pdf> and here <https://scholar.harvard.edu/files/19-fat.pdf>. *Zoom link*: https://harvard.zoom.us/j/99424949004?pwd=aWtPNFM3ZzFYbWxIMXNoZDlyUElVZz09 *Schedule of the workshop*: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Best, Soichiro -- Soichiro Yamauchi PhD candidate Harvard University URL: https://soichiroy.github.io/

3 years, 5 months

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gov3009-l November 2020