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Algorithms and Computer Systems: Lessons for Legal Aid Professionals

This presentation explores the use of algorithms and computer systems in the legal profession. It covers topics such as statistical models, traditional software, and the impact of technology on legal service providers. The session includes tutorials, stories, data analysis, lessons, and reflections on code, technology, AI, and more.

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Algorithms and Computer Systems: Lessons for Legal Aid Professionals

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  1. Algorithms and Computer SystemsLessons for Legal Aid Professionals October 9, 2019 Aaron Rieke and Emma Weil2019 Legal Service Providers of Arkansas Conference

  2. Roadmap • technology tutorial • stories and analysis • lessons and reflections • discussion

  3. code / tech / AI / etc. Statistical Models Traditional Software

  4. Computer programs structured and defined by human programmers. • Typically composed by hand using logical statements. Traditional Software

  5. Traditional Software

  6. Computers learning from data. • Not declarative steps, but rather an expression of statistical relationships between variables. Statistical Models

  7. weight = -122.6 + 3.5*height

  8. Predicting Pneumonia Risk Study (mid-90s) LOW or HIGH risk 1M + patients, 1000s of clinical features

  9. Statistical Models • Learning algorithms are just recipes (think “draw a line through the points”). • Modeling helps us see relationships and patterns in data. • Data is rarely perfect: • Relevant? • Correlation v. Causation • Representative? • Incomplete? • Selection bias? • Real patterns in data can mislead. • Machine learning works great a lot of the time, especially for problems for which lots of representative data is known. • But in social contexts, we need to think carefully.

  10. Traditional Software Statistical Models • Source code can help. • Source code unlikely to help. • Ask about training data and what the model is supposed to predict.

  11. goal translation data implementation outcome

  12. Arkansas Medicaid Work Reporting Goal: enforce strict rules on Medicaid eligibility and access Data: client information, including reported work hours Translation: codify eligibility regulations and qualifications Implementation: online portal that needlessly “closes” Outcome: enrollment numbers plummet

  13. Colorado Benefits Management System Goal: modernize and integrate existing public benefits enrollment systems Data: variety of information from caseworkers and clients Translation: codify eligibility regulations and qualifications Implementation: client-server software contracted to large private firms Outcome: cases took months to process, benefits withheld or given incorrectly

  14. Supplemental Security Income (SSI) Means Testing Goal: enforce eligibility rules for income in SSI Data: bank account information and other financial data Translation: codify income rules and payment schedules Implementation: sample of automatic income checks at the beginning of month Outcome: erroneously failing income checks

  15. Missouri Level of Care (LOC) Transformation Project Goal: reduce "low-need" populations' use of nursing services Data: InterRAI assessments and statewide Medicaid patient information Translation: model symptoms with outcomes to create algorithm Implementation: word document algorithm posted for comments Outcome: the potential for a dramatic drop in service

  16. SNAP Fraud Prevention Goal: enforce rules on retailers who accept SNAP Data: transaction information from grocers and SNAP recipients Translation: determination of “acceptable” vs “suspicious” activity Implementation: software analyzes and flags transactions Outcome: merchants cut off with no effective recourse

  17. Allegheny Family Screening Tool Goal: screen child abuse hotline calls for kids at severe risk Data: family’s government records, casefile (including unsubstantiated referrals) Translation: statistical model based on correlations Implementation: risk score based on model Outcome: poverty itself is often confused with neglect

  18. Northpointe’s COMPAS Risk and Needs Assessment System Goal: determine a defendant's risk of recidivism Data: survey data from defendant and data from prison and jail populations Translation: statistical model based on correlations Implementation: risk score based on model Outcome: black defendants more likely to be falsely flagged high-risk than white defendants

  19. Starter Interrupt Devices Goal: secure subprime auto loan payments from borrowers Data: payment schedule, location from GPS tracker Translation: rules for late payments and allowable travel location Implementation: software automatically interrupts car ignition when rules are broken Outcome: poor borrowers lose control of their cars and are at risk of physical harm

  20. Takeaways • Many different kinds of "technology" challenges. • Don't let jargon get in your way - demand simple answers. • Computer systems don’t remove discretion, they just displace it.

  21. Questions? aaron@upturn.org emma@upturn.org

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