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Listening to ShotSpotter: Acoustics and Predictive Policing in Chicago
No full textThis project hopes to highlight a moving, multidirectional story about acoustic surveillance tools and how they are used to predict and conduct the future. I focus on ShotSpotter Inc’s occupation in Chicago, which has grown omnipresent following the signature of the city’s current contract with ShotSpotter for $33 million in the summer of 2021, lasting through August 2023 (Del Vecchio and Chapman 2022).2 Chicago is now blanketed with microphones and Strategic Decision Support Centers (“SDSC’s”) installed to 21 of the city’s 22 police districts. (Ibid and CBS 2020)3. Chicago accounted for 14% of ShotSpotter’s total revenue in 2021, as the company’s second largest customer (MarketScreener 2022)4. Activists in the city have protested this relationship, especially after ShotSpotter acquired predictive policing company HunchLab in 2018.5 Legislation in Illinois has yet to adequately grapple with advanced acoustic surveillance and predictive policing techniques, further ripening this moment for this dialogue.6
This project is not a study of the borne and exacerbated ills of ShotSpotter’s collaborations with Chicago Police and their rise of their technocratic methodologies, nor is the project much concerned with ShotSpotter’s credence as a technology that is successful even by its own objectives.7 Rather, this project is an experimental inquiry into how our futures may be patrolled by means of acoustic capture and prediction. I explored this by placing ShotSpotter’s approach in contrast with other shapes and styles of listening that nurture the past, present, and future
Humanity Compatible: Aligning Autonomous AI with Kantian Respect for Humanity
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From HHI to HRI: Which Facets of Ethical Decision-Making Should Inform a Robot?
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Where Law and Ethics Meet: A Systematic Review of Ethics Guidelines and Proposed Legal Frameworks on AI
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Below Minimum Wage Pay for International Students at Illinois Tech : A Thematic Analysis of Student Interviews
No full textThe Illinois Institute of Technology (IIT) student population consists of approximately 34% international students(“Student Demographics Illinois Institute of Technology”). International students who are accepted at IIT confirm that they have the necessary financial resources to live in Chicago(“Students and Employment | USCIS”). These students come to the US on a student visa and many international students, after discovering the cost of living in Chicago, need to work to support basic expenses. On-campus work opportunities are limited and as a result, many international students work illegally off campus. It is common practice for employers who illegally hire non-US citizens on student visas to compensate them with below minimum wage and no benefits. These circumstances exacerbate the dis- advantages these students can face as international students who are often marginalized as members of racial, ethnic, or gender minorities. Exploitative working conditions affect their academic success, mental health and wellness, and career trajectories. However, because international students risk losing their visas if they make these working conditions known, they are poorly positioned to advocate for themselves or to seek institutional support.
The goal of the study is to explore and describe the experience of international students who are working illegally in Chicago. I conducted semi-structured interviews with international students who volunteered to be interviewed. Findings from the thematic analysis of the interview data suggest the need for changes to Illinois Tech university policy and also demonstrate potential means for the university to improve outcomes for students
Health Impacts of Environmental Inequalities in Redlined Areas
No full textChicago has a long history of redlining, a discriminatory housing process that has led to segregation in Chicago up to the modern day. This practice marginalized people of color and created a significant and still prevalent wealth gap between redlined and non-redlined communities. It is well known that poorer communities in Chicago are victims of negative environmental factors, such as industrial corridor proximity, and higher air pollution levels. There have been studies done on these things, but there are many overlapping factors that have led to Chicago’s prevalent inequality, creating a complex problem.
Our study aimed to tackle some of this complexity and to analyze how various environmental indicators impacted the health outcomes of people based on the HOLC (Home Owners’ Loan Corporation) grade they lived in. We conducted a literature review to see which health impacts were tied to which significant environmental indicators and designed our technical portion to account for the complexity and inter-relation of the data, but we wanted to do more with the communities as well, rather than just analyzing data. A major part of our project was inspired by the work of Phillip Boda of UIC in regards to designing research centered around communities rather than around data.
We came to the conclusion that the scope of our project was larger and more complex than any two people sufficiently cover and decided to design a coding tool alongside our analysis that will allow for communities to both interpret our findings and run their own analyses in the ways they deem most useful. We hope that this can both provide communities with a more thorough understanding of the complex relations between the environment and health. We also hope to provide a tool that can be more useful than existing resources we’ve used and found to have issues, such as the EPA’s Environmental Justice Screening tool (EJScreen).
We found that higher percentages of greenlined and bluelined areas had little significance to negative health outcomes, or lowered risk, while higher percentages of red and yellowlined areas had greater significance to the model, increasing risk of negative health outcomes with increasing area. The impact of environmental indicators on our model varied depending on the health impact being analyzed, but in the case of every health impact, there was a semi-linear positive trend when a model was created taking into account all of the independent variables and their coefficients