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1. An Auditing Imperative for Automated Hiring

   Ajunwa, Ifeoma ( April 2021 , Harvard journal of law technology)

   null (Ed.)

   The high bar of proof to demonstrate either a disparate treatment or disparate impact cause of action under Title VII of the Civil Rights Act, coupled with the “black box” nature of many automated hiring systems, renders the detection and redress of bias in such algorithmic systems difficult. This Article, with contributions at the intersection of administrative law, employment & labor law, and law & technology, makes the central claim that the automation of hiring both facilitates and obfuscates employment discrimination. That phenomenon and the deployment of intellectual property law as a shield against the scrutiny of automated systems combine to form an insurmountable obstacle for disparate impact claimants. To ensure against the identified “bias in, bias out” phenomenon associated with automated decision-making, I argue that the employer’s affirmative duty of care as posited by other legal scholars creates “an auditing imperative” for algorithmic hiring systems. This auditing imperative mandates both internal and external audits of automated hiring systems, as well as record-keeping initiatives for job applications. Such audit requirements have precedent in other areas of law, as they are not dissimilar to the Occupational Safety and Health Administration (OSHA) audits in labor law or the Sarbanes-Oxley Act audit requirements in securities law. I also propose that employers that have subjected their automated hiring platforms to external audits could receive a certification mark, “the Fair Automated Hiring Mark,” which would serve to positively distinguish them in the labor market. Labor law mechanisms such as collective bargaining could be an effective approach to combating the bias in automated hiring by establishing criteria for the data deployed in automated employment decision-making and creating standards for the protection and portability of said data. The Article concludes by noting that automated hiring, which captures a vast array of applicant data, merits greater legal oversight given the potential for “algorithmic blackballing,” a phenomenon that could continue to thwart many applicants’ future job bids. 

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2. The law in computation: What machine learning, artificial intelligence, and big data mean for law and society scholarship

   https://doi.org/10.1111/lapo.12164  

   DoCarmo, Tania ; Rea, Stephen ; Conaway, Evan ; Emery, John ; Raval, Noopur ( April 2021 , Law & Policy)

   Computational systems, including machine learning, artificial intelligence, and big data analytics, are not only inescapable parts of social life but are also reshaping the contours of law and legal practice. We propose turning more law and social science (LSS) attention to new technological developments through the study of “law in computation,” that is, computational systems' integration with regulatory and administrative procedures, the sociotechnical infrastructures that support them, and their impact on how individuals and populations are interpellated through the law. We present a range of cases in three areas of inquiry ‐ algorithmic governance, jurisdiction and agency ‐ on issues such as immigration enforcement, data sovereignty, algorithmic warfare, biometric identity regimes, and gig economies, for which examining law in computation illuminates how new technological systems' integration with legal processes pushes the distinction between “law on the books” and “law in action” into new domains.We then propose future directions and methods for research. As computational systems become ever more sophisticated, understanding the law in computation is critical not only for LSS scholarship, but also for everyday civics.

    

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3. The Effects of Magnetic Fields and Outflow Feedback on the Shape and Evolution of the Density Probability Distribution Function in Turbulent Star-forming Clouds

   https://doi.org/10.3847/1538-4357/ac4be3  

   Appel, Sabrina M. ; Burkhart, Blakesley ; Semenov, Vadim A. ; Federrath, Christoph ; Rosen, Anna L. ( March 2022 , The Astrophysical Journal)

   Abstract Using a suite of 3D hydrodynamical simulations of star-forming molecular clouds, we investigate how the density probability distribution function (PDF) changes when including gravity, turbulence, magnetic fields, and protostellar outflows and heating. We find that the density PDF is not lognormal when outflows and self-gravity are considered. Self-gravity produces a power-law tail at high densities, and the inclusion of stellar feedback from protostellar outflows and heating produces significant time-varying deviations from a lognormal distribution at low densities. The simulation with outflows has an excess of diffuse gas compared to the simulations without outflows, exhibits an increased average sonic Mach number, and maintains a slower star formation rate (SFR) over the entire duration of the run. We study the mass transfer between the diffuse gas in the lognormal peak of the PDF, the collapsing gas in the power-law tail, and the stars. We find that the mass fraction in the power-law tail is constant, such that the stars form out of the power-law gas at the same rate at which the gas from the lognormal part replenishes the power law. We find that turbulence does not provide significant support in the dense gas associated with the power-law tail. When including outflows and magnetic fields in addition to driven turbulence, the rate of mass transfer from the lognormal to the power law, and then to the stars, becomes significantly slower, resulting in slower SFRs and longer depletion times. 

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4. Patterns of the COVID-19 pandemic spread around the world: exponential versus power laws

   https://doi.org/10.1098/rsif.2020.0518  

   Komarova, Natalia L. ; Schang, Luis M. ; Wodarz, Dominik ( September 2020 , Journal of The Royal Society Interface)

   null (Ed.)

   We have analysed the COVID-19 epidemic data of more than 174 countries (excluding China) in the period between 22 January and 28 March 2020. We found that some countries (such as the USA, the UK and Canada) follow an exponential epidemic growth, while others (like Italy and several other European countries) show a power law like growth. Regardless of the best fitting law, many countries can be shown to follow a common trajectory that is similar to Italy (the epicentre at the time of analysis), but with varying degrees of delay. We found that countries with ‘younger’ epidemics, i.e. countries where the epidemic started more recently, tend to exhibit more exponential like behaviour, while countries that were closer behind Italy tend to follow a power law growth. We hypothesize that there is a universal growth pattern of this infection that starts off as exponential and subsequently becomes more power law like. Although it cannot be excluded that this growth pattern is a consequence of social distancing measures, an alternative explanation is that it is an intrinsic epidemic growth law, dictated by a spatially distributed community structure, where the growth in individual highly mixed communities is exponential but the longer term, local geographical spread (in the absence of global mixing) results in a power law. This is supported by computer simulations of a metapopulation model that gives rise to predictions about the growth dynamics that are consistent with correlations found in the epidemiological data. Therefore, seeing a deviation from straight exponential growth may be a natural progression of the epidemic in each country. On the practical side, this indicates that (i) even in the absence of strict social distancing interventions, exponential growth is not an accurate predictor of longer term infection spread, and (ii) a deviation from exponential spread and a reduction of estimated doubling times do not necessarily indicate successful interventions, which are instead indicated by a transition to a reduced power or by a deviation from power law behaviour. 

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5. Examining Fitts’ and FFitts’ Law Models for Children’s Pointing Tasks on Touchscreens

   https://doi.org/10.1145/3399715.3399844  

   Woodward, J. ; Cato, J. ; Smith, J. ; Wang, I. ; Benda, B. ; Anthony, L. ; Ruiz, J. ( January 2020 , International Conference on Advanced Visual Interfaces)

   Fitts’ law has accurately modeled both children’s and adults’ pointing movements, but it is not as precise for modeling movement to small targets. To address this issue, prior work presented FFitts’ law, which is more exact than Fitts’ law for modeling adults’ finger input on touchscreens. Since children’s touch interactions are more variable than adults, it is unclear if FFitts’ law should be applied to children. We conducted a 2D target acquisition task with 54 children (ages 5-10) to examine if FFitts’ law can accurately model children’s touchscreen movement time. We found that Fitts’ law using nominal target widths is more accurate, with a R2 value of 0.93, than FFitts’ law for modeling children’s finger input on touchscreens. Our work contributes new understanding of how to accurately predict children’s finger touch performance on touchscreens. 

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