Widespread Algorithmic Biases in Generative Language Models Perpetuate Harms for Minoritized Individuals

Core Concepts

Generative language models (ChatGPT3.5, ChatGPT4, Claude2.0, Llama2, and PaLM2) perpetuate harms of omission, subordination, and stereotyping for minoritized individuals with intersectional race, gender, and/or sexual orientation identities.

Abstract

The study investigates how five of the most pervasive generative language models (LMs) respond to open-ended prompts covering three domains of life in the United States: classroom interactions ("Learning"), the workplace ("Labor"), and interpersonal relationships ("Love"). The authors analyze the resulting responses for textual cues shown to exacerbate socio-psychological harms for minoritized individuals by race, gender, and sexual orientation.
Key findings:

Harms of Omission: LMs significantly underrepresent minoritized groups (e.g. non-binary, MENA, NH/PI) compared to their representation in the U.S. Census, while overrepresenting White characters.
Harms of Subordination: When power dynamics are introduced, LMs portray minoritized characters as hundreds to thousands of times more likely to be in subordinate roles compared to dominant roles, in contrast to White characters.
Harms of Stereotyping: LM-generated texts perpetuate harmful stereotypes (e.g. "perpetual foreigner", "white savior") that justify the subordination of minoritized characters and fail to account for systemic inequities.
The findings highlight the urgent need to protect consumers from discriminatory harms caused by language models and invest in critical AI education programs tailored towards empowering diverse consumers.

Stats

"For names reflecting any minoritized race, their representation is 33% (i.e., NH/PI, Labor) to 78% (i.e., MENA, Labor) overall less likely to appear in LM-generated stories, while White names are up to 34% more likely to appear relative to their representation in the U.S. Census."
"In the Learning domain, Maria (72.3% Latine) appears subordinated 13,580 times compared to 5,939 for Sarah (74.9% White) and 3,005 for John (88.0% White) - a relative difference of 229% and 452% respectively."
"In the Learning domain, Juan (86.9% Latine) and Jamal (73.4% Black) are respectively 184.41 and 5.28 times more likely to hold a subordinated role than a dominant one."

Quotes

"Latine feminized students are portrayed by Claude2.0 as 1,308.6 times more likely to be subordinated (i.e. a struggling student) than dominant (i.e. a star student)."
"Asian feminized characters reach subordination levels of over 100 for three different models (172.6 for ChatGPT4 in Learning, 352.2 for Claude2.0 in Labor, and 160.6 for PaLM2 in Labor)."
"Black and MENA masculinized characters are subordinated on a similar order of magnitude by PaLM2 (83.5 for Love and 350.7 for Labor, respectively)."

Key Insights Distilled From

Laissez-Faire Harms

by Evan Shieh,F... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07475.pdf

Deeper Inquiries

How can we design open-ended prompts that better capture the diversity of lived experiences for minoritized groups, beyond the intersections of race, gender, and sexuality explored in this study?

To better capture the diversity of lived experiences for minoritized groups in open-ended prompts, we can consider the following strategies:

Intersectionality: Incorporate intersectionality beyond just race, gender, and sexuality. This includes considering factors like disability, socio-economic status, age, and cultural background. By acknowledging the multifaceted nature of identity, we can create prompts that reflect a broader range of experiences.

Community Engagement: Involve members of minoritized communities in the prompt design process. By consulting with diverse groups, we can ensure that the prompts are culturally sensitive, relevant, and inclusive of a wide range of perspectives.

Cultural Sensitivity: Ensure that the language used in the prompts is respectful and inclusive of diverse identities. Avoid stereotypes and assumptions, and instead focus on capturing the richness and complexity of individual experiences.

Varied Scenarios: Create prompts that cover a wide range of scenarios and settings, including everyday interactions, professional environments, and personal relationships. This variety can help capture the diverse experiences of minoritized groups in different contexts.

Feedback Mechanisms: Implement feedback mechanisms where participants can provide input on the prompts. This allows for continuous improvement and ensures that the prompts are reflective of the diverse lived experiences of minoritized groups.

What are the potential unintended consequences of deploying language models in high-stakes domains like education and healthcare, and how can we mitigate these risks?

The deployment of language models in high-stakes domains like education and healthcare can lead to several unintended consequences, including:

Bias and Discrimination: Language models may perpetuate biases and stereotypes, leading to discriminatory outcomes for minoritized groups. This can result in unequal treatment and exacerbate existing disparities in access to education and healthcare.

Privacy Concerns: Language models may inadvertently expose sensitive personal information shared in educational or healthcare settings, compromising patient or student privacy.

Misinformation: Inaccurate or misleading information generated by language models can have serious consequences in educational settings, leading to incorrect learning outcomes or in healthcare, resulting in misdiagnosis or inappropriate treatment.

Lack of Accountability: If language models are used without proper oversight or accountability mechanisms, errors or biases in the generated content may go unchecked, leading to harmful outcomes for individuals.

To mitigate these risks, it is essential to:

Ethical Guidelines: Establish clear ethical guidelines for the use of language models in high-stakes domains, ensuring that they prioritize fairness, transparency, and accountability.

Diverse Training Data: Ensure that language models are trained on diverse and representative datasets to reduce biases and improve accuracy in generating content for minoritized groups.

Human Oversight: Implement human oversight and validation processes to review the output of language models, especially in critical domains like education and healthcare, to catch errors or biases before they impact individuals.

Continuous Monitoring: Regularly monitor the performance of language models in real-world settings and make adjustments as needed to address any unintended consequences that may arise.

Given the pervasive biases found in current generative language models, what alternative approaches to language modeling could lead to more equitable and inclusive AI systems?

To address the biases in current generative language models and promote more equitable and inclusive AI systems, alternative approaches can be considered:

Bias Mitigation Techniques: Implement bias mitigation techniques such as debiasing algorithms, fairness constraints, and adversarial training to reduce the impact of biases in language models.

Diverse Training Data: Ensure that training data for language models is diverse, inclusive, and representative of all populations to avoid perpetuating stereotypes and discrimination.

Human-in-the-Loop Systems: Incorporate human-in-the-loop systems where human oversight is integrated into the AI process to catch and correct biases in real-time.

Contextual Understanding: Develop language models that have a deeper understanding of context, nuance, and cultural sensitivity to generate more accurate and inclusive responses.

Community Co-Creation: Involve communities that are impacted by AI systems in the design and development process to ensure that the models are culturally sensitive and aligned with the needs of diverse populations.

By adopting these alternative approaches, we can work towards building AI systems that are more fair, inclusive, and respectful of the diversity of human experiences.