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Johnson will continue her research as an affiliate of McCourt\u2019s <\/span>Massive Data Institute<\/span><\/a> (MDI), concentrating on how predictive algorithms \u2014 a mathematical process used to predict future events or outcomes by analyzing patterns in a given data set \u2014 might impact inequality across K-12 schools.\u00a0<\/span><\/p>\n In the wake of COVID-19\u2019s impact on learning, the federal government provided funding to school districts across the country for high-dosage tutoring, defined as one-on-one tutoring or tutoring in very small groups. While school districts are trying to assign tutors to the students who need help most urgently, there\u2019s a lot of variation in how districts try to identify \u201chigh-need\u201d students. Johnson and collaborators are studying the relative fairness of both schools\u2019 existing methods, such as relying on school staff or parents to identify which students need a tutor most urgently, and newer predictive algorithms.<\/span><\/p>\n \u201cDistricts might use a predictive model to try to determine which student attributes, from learning disabilities to repeating a grade, are most predictive of need,\u201d said Johnson.\u00a0<\/span><\/p>\n \u201cThere\u2019s growing concern about the rise of algorithms in contexts like the criminal justice system, child welfare investigations and tenant screening,\u201d said Johnson. \u201cResearch shows that algorithms can exhibit bias in who they judge as posing a risk to society. However, some of my recent work focuses on what happens when governments use algorithms to decide on whom to help.\u201d<\/span><\/p>\n In a <\/span>new paper<\/span><\/a>, co-written by Notre Dame Assistant Professor Simone Zhang, Johnson explores the benefits and drawbacks of governments using predictive algorithms to allocate scarce resources. Professors Johnson and Zhang studied two cases where governments try to judge the need for help: the waitlist policies that thousands of local governments use to distribute housing vouchers and the formulas that states and K-12 school districts use to distribute financing.\u00a0<\/span><\/p>\n In both cases, policymakers face the same challenge in deciding whom to assist: how to allocate resources when the supply of help falls short of demand. In most of the United State\u2019s existing social policies, policymakers use categorical prioritization, a series of categories, such as age, income and disability, to determine deservingness and identify who gets priority over whom. One benefit of categorical prioritization is that it \u201callows for a high degree of value pluralism \u2014 the idea that there are several values surrounding who is worthy of government assistance which may be equally valid,\u201d said Johnson.<\/span><\/p>\n However, categorical prioritization also has drawbacks that are less recognized. Johnson and Zhang argue that categorical prioritization has fallen short in capturing the complexity of need. \u201cPeople are multidimensional and have many attributes that could indicate deservingness,\u201d said Johnson.\u00a0<\/span><\/p>\n She and Zhang contend that forces other than values play a role in defining the traditional categories that policymakers use to determine deservingness, such as power and stigma. \u201cHomelessness, for example, is often ranked below age and disability in points systems for housing vouchers, suggesting that groups advocating for the elderly or disabled may have more political power,\u201d said Johnson. \u201cWith algorithms, social stigma may play less of a role in excluding people than with traditional categories.\u201d\u00a0<\/span><\/p>\nUsing predictive modeling to improve public policy<\/span><\/h3>\n
How policymakers determine deservingness\u00a0<\/span><\/h3>\n