Benefits Tech Reading List
The following list of external resources is not an exhaustive list of all relevant resources, but is a list we have gathered together of resources that may be of interest as advocates dig deeper into public benefits tech issues. These documents are in addition to those available on the Benefits Tech Advocacy Hub’s Resource List page and news reports connected to cases in the Case Study Library.
- Guides & Reports
- Algorithmic Bias Playbook, Center for Applied Artificial Intelligence
- Public Scrutiny of Automated Decisions: Early Lessons and Emerging Methods, Upturn
- Disability Bias and AI, AI Now
- Fact Versus Fiction: Clinical Decision Support Tools and the (Mis)use of Race, U.S. House Committee on Ways & Means (2021)
- Quality Adjusted Life Years and the Devaluation of Life with Disability, National Council on Disability (Nov. 6, 2019)
- Articles on Tech, Bias, and Related Issues
- Anupam Datta et al., Proxy Discrimination in Data Driven Systems (2017),.
- Robert Bartlet et al., Consumer-Lending Discrimination in the FinTech Era (2019),
- Caroline Mimbs Nyce, How Government Learned to Waste Your Time, The Atlantic (2021)
- Annie Lowrey, The Time Tax: Why is so much American bureaucracy left to average citizens?, The Atlantic ( 2021)
- Law Review Articles
- Rebecca Wexler, Life, Liberty, and Trade Secrets: Intellectual Property in the Criminal Justice System, Stanford Law Review (2018)
- Kate Crawford & Jason Schultz, AI Systems as State Actors, Columbia Law Review (2019)
- Mikella Hurley & Julius Adebayo, Credit Scoring in the Era of Big Data, Yale Journal of Law & Technology (2016)
- Sarah Valentine, Impoverished Algorithms (2019)
- Danielle Keats Citron, A Poor Mother’s Right to Privacy: A Review, Boston University Law Review (2018)
- Ryan Calo & Danielle Keats Citron, The Automated Administrative State: A Crisis of Legitimacy, Emory Law Journal (2021).
- Danielle Keats Citron, The Scored Society: Due Process for Automated Predictions, Washington Law Review (2014)
- Danielle Keats Citron, Technological Due Process, Washington University Law Review (2008)
- Related NY Times article, Big Data Should Be Regulated by “Technological Due Process” (2016)
- Studies/Journal Articles about Bias in Healthcare
- Kendra Albert & Maggie Delano, Sex Trouble: Sex/gender slippage, sex confusion, and sex obsession in machine learning using electronic health records, Perspective (Aug. 12, 2022)
- Taylor M. Cruz, Perils of data-driven equity: Safety-net care and big data’s elusive grasp on health inequality, Big Data & Society – SAGE Journals, (2020)
- Rachel R. Hardeman, Eduardo M. Medina & Katy Kozhimannil, Structural Racism and Supporting Black Lives—The Role of Health Professionals, New Eng. J. Med. (Dec. 1, 2016)
- Ibram x. Kendi, There is No Such Thing as Race in Health-care Algorithms, The Lancet (Dec. 2019)
- Hannah E Knight et al., Challenging Racism in the Use of Health Data,The Lancet (Feb. 3, 2021).
- Melissa McCradden et al., Ethical Limitations of Algorithmic fairness solutions in health care machine learning, The Lancet (May 1, 2020).
- William Dwight Miller et al., Accuracy of the Sequential Organ Failure Assessment Score for In-Hospital Mortality by Race and Relevance to Crisis Standards of Care, JAMA Network (June 18, 2021)
- Ziad Obermeyer, Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations, Science (Oct. 25, 2019)
- Jessica Paulus & David Kent, Predictably Unequal: Understanding and addressing concerns that algorithmic clinical prediction may increase health disparities, NPJ Digital Medicine (July 30, 2020)
- Darshali A. Vyas et al., Challenging the Use of Race in the Vaginal Birth After Cesarean Section Calculator, Women’s Health Issues (2019)
- Medicaid: Resources on LTSS Assessment Tools
- HCBS Instrument Database, Rehabilitation Research and Training Center on HCBS Outcome Measurement, University of Minnesota Institute on Community Integration
- MACPAC Report on Assessment Tools in LTSS
- National Health Law Program (NHeLP) Resources on LTSS Assessment Tools:
- Medicaid Assessments for Long-Term Supports & Services
- Evaluating Functional Assessments for Older Adults
- Opportunities for Public Comment on HCBS Assessment Tools
- Q&A: Using Assessment Tools to Decide Medicaid Coverage
- Ensuring that Assessment Tools are Available to Enrollees
- Demanding Ascertainable Standards: Medicaid as a Case Study
- NHeLP has various other resources on benefits tech in Medicaid, including blogs, comments, case examples, etc. This blog on Preventing Harm from Automated Decision-Making Systems in Medicaid discusses much of this work and links to other resources.
- Frequently Used LTSS Assessment Tools: