Designing Human-Centered Algorithms for the Public Sector: A Case Study of the U.S. Child-Welfare System

The U.S. Child Welfare System (CWS) is increasingly seeking to emulate business models of the private sector centered in efficiency, cost reduction, and innovation through the adoption of algorithms. These data-driven systems purportedly improve decision-making, however, the public sector poses its own set of challenges with respect to the technical, theoretical, cultural, and societal implications of algorithmic decision-making. To fill these gaps, my dissertation comprises four studies that examine: 1) how caseworkers interact with algorithms in their day-to-day discretionary work, 2) the impact of algorithmic decision-making on the nature of practice, organization, and street-level decision-making, 3) how casenotes can help unpack patterns of invisible labor and contextualize decision-making processes, and 4) how casenotes can help uncover deeper systemic constraints and risk factors that are hard to quantify but directly impact families and street-level decision-making. My goal for this research is to investigate systemic disparities and design and develop algorithmic systems that are centered in the theory of practice and improve the quality of human discretionary work. These studies have provided actionable steps for human-centered algorithm design in the public sector

An Efficient and Cost Effective FPGA Based Implementation of the Viola-Jones Face Detection Algorithm

We present an field programmable gate arrays (FPGA) based implementation of the popular Viola-Jones face detection algorithm, which is an essential building block in many applications such as video surveillance and tracking. Our implementation is a complete system level hardware design described in a hardware description language and validated on the affordable DE2-115 evaluation board. Our primary objective is to study the achievable performance with a low-end FPGA chip based implementation. In addition, we release to the public domain the entire project. We hope that this will enable other researchers to easily replicate and compare their results to ours and that it will encourage and facilitate further research and educational ideas in the areas of image processing, computer vision, and advanced digital design and FPGA prototyping

Robot navigation and target capturing using nature-inspired approaches in a dynamic environment

Path Planning and target searching in a three-dimensional environment is a challenging task in the field of robotics. It is an optimization problem as the path from source to destination has to be optimal. This paper aims to generate a collision-free trajectory in a dynamic environment. The path planning problem has sought to be of extreme importance in the military, search and rescue missions and in life-saving tasks. During its operation, the unmanned air vehicle operates in a hostile environment, and faster replanning is needed to reach the target as optimally as possible. This paper presents a novel approach of hierarchical planning using multiresolution abstract levels for faster replanning. Economic constraints like path length, total path planning time and the number of turns are taken into consideration that mandate the use of cost functions. Experimental results show that the hierarchical version of GSO gives better performance compared to the BBO, IWO and their hierarchical versions.Comment: 8 pages, 8 figure

Rethinking "Risk" in Algorithmic Systems Through A Computational Narrative Analysis of Casenotes in Child-Welfare

Risk assessment algorithms are being adopted by public sector agencies to make high-stakes decisions about human lives. Algorithms model "risk" based on individual client characteristics to identify clients most in need. However, this understanding of risk is primarily based on easily quantifiable risk factors that present an incomplete and biased perspective of clients. We conducted a computational narrative analysis of child-welfare casenotes and draw attention to deeper systemic risk factors that are hard to quantify but directly impact families and street-level decision-making. We found that beyond individual risk factors, the system itself poses a significant amount of risk where parents are over-surveilled by caseworkers and lack agency in decision-making processes. We also problematize the notion of risk as a static construct by highlighting the temporality and mediating effects of different risk, protective, systemic, and procedural factors. Finally, we draw caution against using casenotes in NLP-based systems by unpacking their limitations and biases embedded within them

Methods for Generating Typologies of Non/use

Prior studies of technology non-use demonstrate the need for approaches that go beyond a simple binary distinction between users and non-users. This paper proposes a set of two different methods by which researchers can identify types of non/use relevant to the particular sociotechnical settings they are studying. These methods are demonstrated by applying them to survey data about Facebook non/use. The results demonstrate that the different methods proposed here identify fairly comparable types of non/use. They also illustrate how the two methods make different trade offs between the granularity of the resulting typology and the total sample size. The paper also demonstrates how the different typologies resulting from these methods can be used in predictive modeling, allowing for the two methods to corroborate or disconfirm results from one another. The discussion considers implications and applications of these methods, both for research on technology non/use and for studying social computing more broadly

Child Welfare System: Interaction of Policy, Practice and Algorithms

This paper focuses on understanding the collaborative work of multi-disciplinary teams in the child welfare system (CWS). CWS workers participate in meetings mediated by policies in place, current child-welfare practice, as well as algorithms that offer recommendations. We conducted 25 observations of these meetings to assess how algorithms aid decision-making in a domain where decisions often come down to the policies and practices in place. Our findings suggest that the algorithm works fairly well at recommending placement settings, however, these recommendations are often overridden because of policy or legal requirements. Moreover, re-appropriation of the placement algorithm to prescribe the rates for foster parents has led to unintended consequences. This poster identifies uses cases of the algorithm in place, scenarios where conflicts arise between the algorithm and policy/practice, as well as how these conflicts are addressed. Our work identifies a need for human-centered algorithms that can better support child welfare practice