AI generates covertly racist decisions about people based on their dialect.

Hofmann, V., et al. (2024).

Nature, 633(8028), 147–154.

https://doi.org/10.1038/s41586-024-07856-5

Abstract

Hundreds of millions of people now interact with language models, with uses ranging from help with writing to informing hiring decisions. However, these language models are known to perpetuate systematic racial prejudices, making their judgements biased in problematic ways about groups such as African Americans. Although previous research has focused on overt racism in language models, social scientists have argued that racism with a more subtle character has developed over time, particularly in the United States after the civil rights movement. It is unknown whether this covert racism manifests in language models. Here, we demonstrate that language models embody covert racism in the form of dialect prejudice, exhibiting raciolinguistic stereotypes about speakers of African American English (AAE) that are more negative than any human stereotypes about African Americans ever experimentally recorded. By contrast, the language models’ overt stereotypes about African Americans are more positive. Dialect prejudice has the potential for harmful consequences: language models are more likely to suggest that speakers of AAE be assigned less-prestigious jobs, be convicted of crimes and be sentenced to death. Finally, we show that current practices of alleviating racial bias in language models, such as human preference alignment, exacerbate the discrepancy between covert and overt stereotypes, by superficially obscuring the racism that language models maintain on a deeper level. Our findings have far-reaching implications for the fair and safe use of language technology.

Here are some thoughts:

This research article demonstrates that artificial intelligence language models exhibit covert racism through deep-seated dialect prejudice against speakers of African American English. By evaluating language variations, the authors found that these models attach negative stereotypes to African American English that are more severe than any human stereotypes ever recorded experimentally, even while their overt statements about Black individuals appear positive. For psychologists, this study is highly important because it reveals how systemic racism and implicit bias can be stealthily automated and amplified within technology. It underscores that human preference training merely masks superficial bias while leaving harmful, underlying prejudices fully intact. Insightfully, the findings warn that relying on artificial intelligence for clinical diagnostics, forensic evaluations, or employment screening can lead to discriminatory outcomes, such as harsher legal judgments or lower prestige job recommendations. Psychologists must therefore spearhead critical evaluations of these tools to ensure digital assessments do not reinforce historical inequities.

The moon, the ghetto and artificial intelligence: Reducing systemic racism in computational algorithms.

Fountain, J. (2022).

Government Information Quarterly, 39(2), 101645.

https://doi.org/10.1016/j.giq.2021.101645 

Abstract

Computational algorithms and automated decision making systems that include them offer potential to improve public policy and organizations. But computational algorithms based on biased data encode those biases into algorithms, models and their outputs. Systemic racism is institutionalized bias with respect to race, ethnicity and related attributes. Such bias is located in data that encode the results and outputs of decisions that have been discriminatory, in procedures and processes that may intentionally or unintentionally disadvantage people based on race, and in policies that may discriminate by race. Computational algorithms may exacerbate systemic racism if they are not designed, developed, and used–that is, enacted–with attention to identifying and remedying bias specific to race. Advancing social equity in digital governance requires systematic, ongoing efforts to assure that automated decision making systems, and their enactment in complex public organizational arrangements, are free from bias.

Highlights

• Computational algorithms are powerful tools but may replicate biases.

• Biases, including systemic racism, in underlying data bias algorithms

• Automated decision making systems that discriminate harm people.

• Careful scrutiny of data, processes, variables and algorithms may reduce bias.

Black Americans and racial conspiracy theories about the news media

Kiana Cox

Pew Research Center

Originally posted 10 June 24

Some of the most enduring stereotypes about Black people have their roots in images created during and immediately after slavery. From the docile Mammy and Uncle Tom characters that appeared in newspaper ads and on food containers to the threatening Mandingo in the film “Birth of a Nation” and the more recent controversy about whether television character Olivia Pope was a modern-day Jezebel, Black Americans’ relationship with media has been contentious at best.

Black Americans have also said the news media specifically characterizes them as disproportionately poor, welfare-dependent and criminal. This history of stereotypical imagery provides some context for Black Americans’ beliefs in racial conspiracy theories about the media.

What is a ‘racial conspiracy theory’?

In this report, the phrase “racial conspiracy theories” refers to the suspicions that Black adults might have about the actions of U.S. institutions based on their personal and collective historical experiences with racial discrimination.

A Pew Research Center survey from early 2023 shows that 63% of Black Americans say the news about Black people is often more negative than news about other racial and ethnic groups. Over half (57%) say the news only covers certain segments of Black communities, and 43% say the coverage significantly stereotypes Black people.

The information is here.

Here are some thoughts:

The Pew study on Black Americans' beliefs in racial conspiracy theories about the news media sheds light on the deep-rooted mistrust stemming from a long history of systemic racism and negative portrayals. Harmful stereotypes like the docile "Mammy" or threatening "Mandingo" have perpetuated biases. Black Americans perceive disproportionate coverage depicting them as poor, criminal, and welfare-dependent, reinforcing negative stereotypes.

This mistrust is rooted in well-documented acts of discrimination and harm against Black communities, such as the Tuskegee Syphilis Study and Tulsa Race Massacre. The racial conspiracy theories are not mere paranoia but valid beliefs shaped by these collective experiences of intentional and negligent harm by institutions like the media.

To rebuild trust, the news media must actively dismantle negative stereotypes through increased newsroom diversity, cultural competency training, amplifying Black voices, and accountability for biased coverage. Acknowledging their role in shaping public perceptions and taking proactive steps towards ethical, inclusive reporting is crucial for promoting racial equity and justice.

Systemic racism and U.S. health care

J. Feagin & Z. Bennefield
Social Science & Medicine
Volume 103, February 2014, Pages 7-14

Abstract

This article draws upon a major social science theoretical approach–systemic racism theory–to assess decades of empirical research on racial dimensions of U.S. health care and public health institutions. From the 1600s, the oppression of Americans of color has been systemic and rationalized using a white racial framing–with its constituent racist stereotypes, ideologies, images, narratives, and emotions. We review historical literature on racially exploitative medical and public health practices that helped generate and sustain this racial framing and related structural discrimination targeting Americans of color. We examine contemporary research on racial differentials in medical practices, white clinicians' racial framing, and views of patients and physicians of color to demonstrate the continuing reality of systemic racism throughout health care and public health institutions. We conclude from research that institutionalized white socioeconomic resources, discrimination, and racialized framing from centuries of slavery, segregation, and contemporary white oppression severely limit and restrict access of many Americans of color to adequate socioeconomic resources–and to adequate health care and health outcomes. Dealing justly with continuing racial “disparities” in health and health care requires a conceptual paradigm that realistically assesses U.S. society's white-racist roots and contemporary racist realities. We conclude briefly with examples of successful public policies that have brought structural changes in racial and class differentials in health care and public health in the U.S. and other countries.

Highlights

• A full-fledged theory of structural (systemic) racism for interpreting health care data.

• A full-fledged developed theory of structural (systemic) racism for interpreting public health data.

• Focus on powerful white decision makers central to health-related institutions.

• Importance of listening to patients and physicians of color on health issues.

• Implications of systemic racism theory and data for public policies regarding medical care and public health.

The info is here.

7 Ways We Know Systemic Racism Is Real

benjerry.com

Here is an excerpt:

Racism at Every Level of Society

Systemic racism is about the way racism is built right into every level of our society. Many people point to what they see as less in-your-face prejudice and bias these days, compared to decades past, but as Archbishop Desmond Tutu said, “If you are neutral in situations of injustice, you have chosen the side of the oppressor. If an elephant has its foot on the tail of a mouse and you say that you are neutral, the mouse will not appreciate your neutrality.”

While fewer people may consider themselves racist, racism itself persists in our schools, offices, court system, police departments, and elsewhere. Think about it: when white people occupy most positions of decision-making power, people of color have a difficult time getting a fair shake, let alone getting ahead. Bottom line: we have a lot of work to do.

The blog post is here.