More Like this

1. 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)

   Abstract 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. 

   more » « less
2. 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. 

   more » « less
3. Misunderstanding Law: Undergraduates’ Analysis of Campus Title IX Policies

   https://doi.org/10.3102/01623737221113576  

   Albrecht, Kat; Nielsen, Laura Beth; Wuorinen, Lydia (October 2022, Educational Evaluation and Policy Analysis)

   Colleges and universities are legally required to attempt to prevent and redress sexual violations on campus. Neo-institutional theory suggests that the implementation of law by compliance professionals rarely achieves law’s goals. It is critical in claims-based systems that those who are potential claimants understand the law. This article demonstrates that (a) intended subjects of the law (colleges and universities) interpret and frame the law in very similar ways; (b) resultant policies are complex and difficult to navigate; and (c) university undergraduates in an experimental setting are not able to comprehend the Title IX policies designed to protect them. These findings suggest that current implementations of Title IX policies leave them structurally ineffective to combat sexual assaults on campus. 

   more » « less
4. Path Properties of a Generalized Fractional Brownian Motion

   https://doi.org/10.1007/s10959-020-01066-1  

   Ichiba, Tomoyuki; Pang, Guodong; Taqqu, Murad S. (January 2021, Journal of Theoretical Probability)

   null (Ed.)

   The generalized fractional Brownian motion is a Gaussian self-similar process whose increments are not necessarily stationary. It appears in applications as the scaling limit of a shot noise process with a power-law shape function and non-stationary noises with a power-law variance function. In this paper, we study sample path properties of the generalized fractional Brownian motion, including Hölder continuity, path differentiability/non-differentiability, and functional and local law of the iterated logarithms. 

   more » « less
5. Issues Related to Using Finger-Fitts law to Model One-Dimensional Touch Pointing Tasks

   https://doi.org/10.1145/3490355.3490360  

   Ko, Yu-Jung; Zhao, Hang; Ramakrishnan, IV; Zhai, Shumin; Bi, Xiaojun (October 2021, Ninth International Symposium of Chinese CHI (Chinese CHI 2021))

   Finger-Fitts law [6] is a variant of Fitts’ law which accounts for the finger ambiguity in touch pointing. In this paper we investigated two research questions related to Finger-Fitts law: (1) Should Finger-Fitts law use nominal target width W or effect target width We to model MT? and (2) should Finger-Fitts law use a pre-defined value (denoted by σa) or a free parameter (denoted by c) to represent the absolute ambiguity caused by finger touch? Our investigation on two touch pointing datasets showed that there are cases where using nominal width has stronger model fitness, and also cases where using effective width is better. Regarding the representation of finger ambiguity, using a free parameter c to represent the ambiguity of finger touch always leads to stronger model fitness than using the pre-defined σa, after controlling for overfitting. It indicates that viewing the finger ambiguity as an empirically determined parameter has more flexibility to capture the ambiguity of finger touch involved in the study. Overall, our research advances the understanding on how to model Finger touch input with Finger-Fitts law. 

   more » « less