As a nonprofit professional with over a decade of experience working in homelessness programs and currently working in homelessness prevention, I’ve often heard coworkers describe how a person in one of these programs reminded them of a close relative or friend. Of course, there’s nothing necessarily wrong with these kinds of empathetic connections. Indeed, a sense of familiarity can lead to increased trust and attachment. Yet it can also create space for bias: familiarity can be derived from a variety of factors—the words someone uses, their background, conjugation, or even eye color—but it’s often connected to culture, ethnicity, and/or traditional access to social capital. In this sense, the phrase “case by case basis” can indicate situations where a case manager’s individual discretion can show bias by that exact sense of familiarity and comfort.

To offer the best and equitable service, practitioners should develop a heightened self-awareness. Supervisory evaluations in the sector generally entail reviews of Objectives and Key Results (OKRs) and key performance indicators (KPIs); by the same token, practitioner self-evaluations usually focus on outcomes and outputs. Neither tend to provide the space for self-reflection on the role a practitioner’s discernment plays in the outcomes for program participants. For this reason, it is imperative that practitioners critically examine who they go above and beyond for, and why.

Consider two hypothetical applicants for a financial assistance program geared toward homelessness prevention. First, imagine a woman impacted by the COVID-19 pandemic, college-educated, laid off by a tech company, and running out of savings. Now, imagine a woman escaping domestic violence, with a baby on breathing tubes in a hospital, only a few days away from homelessness. Both might qualify for the program, and so, service providers might ask who deserves it more, who has a more easily solvable situation, or who advocated for themselves more articulately and painted a better picture. Without an appropriate framework for the decision, bias could creep into the decision. Particularly in such circumstances, inexperienced staff members might fail to consider who is more vulnerable or in a more precarious situation. Likewise, a case manager may succumb to “availability bias,” favoring actions that require less effort on the case manager’s part and prioritizing the low-hanging fruit of easily solvable success.

Without diagnostic controls, nuanced biases can skew a case manager’s priorities. This can range from the “Halo Effect”—which means overestimating the positive attributes a program participant can have, such as someone trying to get back into gainful employment—to its opposite, the “Horn Effect,” which means overestimating a subjectively negative attribute. Further, without systems in place, referrals from an influential source (whether it be a funder, a friend, or a political presence) may create expectations to streamline a new program participant to the top of the list. Cultural biases may influence how a case manager views situations: Cultures with multigenerational households might view "couch surfing" as having secure housing, while other cultures may see this as homelessness. At its most egregious, the absence of a systematic prioritization system can create space for quid pro quo service: Without adequate expectations around tracking, case notes, or case conferencing, a case manager can develop offline arrangements that can result in favors, kickbacks, and manipulation.

Weighted Prioritization

Implementing weighted prioritization tools allows assistance programs to quantify specific attributes, which collectively creates a “score” translating to overall need, based on many factors. This approach is not always common in homelessness prevention or financial assistance programs, but without a weighted prioritization tool—and appropriate screening controls—all well-meaning assistance programs may have severely inequitable practices.

Read the full article about bias in financial assistance programs by Logan McDonnell at Stanford Social Innovation Review.