Important Q & A
Bias: “favoring of or against one person, group or thing compared with another, usually in a way considered to be unfair. Biases can be conscious or unconscious – explicit or implicit. In addition, bias can be institutionalized into policies, practices and structures.” (Brown University, 2021).
Bias Incident: “any hurtful, discriminatory or harassing act that targets individuals or groups based on perceived or actual identity.” (Brown University, 2020)
Please Note: To be considered a “bias incident,” the act is not required to violate College policy, nor is it required to be a crime under any federal, state or local laws.
Examples of Bias Incidents
The types of bias incidents that should be reported to Ȧ’s Office of Institutional Equity include, but are not limited to:
- issuing threats to physical, mental, and/or emotional safety
- biased policy or policy enforcement
- biased language
- other aggressions/ (e.g., refusing to use a person’s preferred gender pronouns, ridiculing a person’s language or accent, and using a racial, ethnic or another slur in a joke or to identify someone.)
Studies on campus climate continue to shed light on the negative experiences of students with historically marginalized identities, namely Black, Indigenous, and People of Color (BIPOC), females, and members of the LGBTQ+ community (Miller, Guida, Smith, Ferguson, & Medina, 2018).
Ȧ’s own 2019 Campus Climate Survey key findings* (Rankin & Associates, 2019) reveal that:
- Students, faculty, and staff experience exclusionary, intimidating, offensive, and hostile conduct based on racial and gender identity.
- Queer spectrum, trans-spectrum, multiracial, and first-generation survey respondents and survey respondents with one or more disabilities report feeling less comfort with campus, workplace, and classroom climates.
- Student perceptions of academic success are significantly lower for trans-spectrum graduates, multiracial undergraduates, and undergraduates with multiple disabilities.
*Based on these key findings, Rankin & Associates explicitly recommended Ȧ implement a bias reporting mechanism such as this.