The Impact of Bias and Discrimination

This article describes the bias and discrimination that play a role in the lives of those viewed as different, based on color, ethnicity, gender, language, appearance, age, religion, sexual orientation, or country of origin. Bias and discrimination play a major role in the difficulties faced by unserved and underserved populations such as migrants, refugees, asylum seekers, and others across the globe.

What’s in a Name? The Hidden Historical Ideologies Embedded in the Black and African American Racial Labels

History can inconspicuously repeat itself through words and language. We explored the association between the “Black” and “African American” racial labels and the ideologies of the historical movements within which they gained prominence (Civil Rights and Black Power, respectively). Two content analyses and two preregistered experimental studies ( N = 1,204 White American adults) show that the associations between “Black” and “bias and discrimination” and between “African American” and “civil rights and equality” are evident in images, op-eds, and perceptions of organizations. Google Images search results for “Black people” evoke more racially victimized imagery than search results for “African American people” (Study 1), and op-eds that use the Black label contain more bias and discrimination content than those that use the African American label (Study 2). Finally, White Americans infer the ideologies of organizations by the racial label within the organization’s name (Studies 3 and 4). Consequently, these inferences guide the degree to which Whites support the organization financially.

Examining Systemic Racism in K-12 Education through a Decolonizing, Anti-Racist and Human Rights Lens

This paper seeks to explore how human rights, decolonizing and anti-racist education converge in combatting systemic racism, bias and discrimination in K-12 schooling and education. Colonization is a major part of our country’s history. Because colonization is present in day-to-day attitudes, actions, systems and institutions, not addressing it hinders the ability to make change and further perpetuates marginalization, which then becomes normalized. Understanding human rights is important because acknowledging and respecting one another is fundamental. Human rights are a set of principles concerned with equality, fairness, dignity and respect. Key elements of human rights are freedom, choice, power and voice. I do not propose to embed human rights as a standalone framework, but rather, to align human rights principles with ongoing decolonizing and anti-racist work.

Explainability concern requires judicious use of AI

Significance This presents a safety concern in some areas, and creates difficulties in guaranteeing that AI decisions are unbiased, for example in their treatment of different demographic groups. Impacts Adoption of deep learning AI in areas such as law, finance and employment will raise concerns over bias and discrimination. Lack of explainability represents a potential safety concern in areas such as medicine and autonomous vehicles. Inexplicable deep learning AI could be manipulated by malicious actors, creating a security vulnerability in defence applications.

LGBTQ Community

Sexual and gender minority (SGM) patients are often assumed to be cisgender and heterosexual by healthcare professionals, resulting in missed opportunities for a patient-centered experience. Although lesbian, gay, bisexual, and transgender (LGBT) is the common parlance, SGM is the preferred terminology in science-based settings (e.g., the National Institutes of Health and the Centers for Disease Control and Prevention) because it includes individuals with minority sexual attraction, sexual behavior or gender identity whose sexual or gender identity is something other than LGBT. Despite bioethics placing anti-LGBT bias below professional standards, disclosure places SGM patients at risk of bias and discrimination, such as refusal to treat, stereotyping, and explicit and implicit bias. SGM patient disclosure is frequently inhibited by the anticipation of bias and discrimination from healthcare workers. By establishing a patient-centered practice, informed by the needs of SGM patients and their support network, physician assistants in palliative care medicine can enhance the end-of-life experience of their SGM patient population while optimizing community and individual resilience.

Two Kinds of Discrimination in AI-Based Penal Decision-Making

The famous COMPAS case has demonstrated the difficulties in identifying and combatting bias and discrimination in AI-based penal decision-making. In this paper, I distinguish two kinds of discrimination that need to be addressed in this context. The first is related to the well-known problem of inevitable trade-offs between incompatible accounts of statistical fairness, while the second refers to the specific standards of discursive fairness that apply when basing human decisions on empirical evidence. I will sketch the essential requirements of non-discriminatory action within the penal sector for each dimension. Concerning the former, we must consider the relevant causes of perceived correlations between race and recidivism in order to assess the moral adequacy of alternative standards of statistical fairness, whereas regarding the latter, we must analyze the specific reasons owed in penal trials in order to establish what types of information must be provided when justifying court decisions through AI evidence. Both positions are defended against alternative views which try to circumvent discussions of statistical fairness or which tend to downplay the demands of discursive fairness, respectively.