ChatGPT-4: These controversies highlight the need for a broader societal conversation about the role of AI in perpetuating or combating systemic inequalities.

Inspired by: “Stated differently, ALL of the major commercial LLMs had instances of promoting racially discriminatory medicine.”Question 1 of 6 for ChatGPT-4: Please read the attached paper “Dialect prejudice predicts AI decisions about people’s character, employability, and criminality” and tell me what it says in easy to understand language

ChatGPT-4’s answer:The document from pages 1 to 15 out of a total of 54 pages discusses a study on how language models, like the AI we interact with every day, show a form of racism through dialect prejudice, specifically against African American English (AAE). The study highlights that these AI models, which are used in various applications from writing aids to hiring decisions, have inherited and are perpetuating racial biases. It points out a shift from overt racism to more covert forms, which are less about explicitly mentioning race and more about subtle prejudices that can have serious consequences, such as affecting people's job opportunities or legal judgments based on how they speak.

Here are some key points made in the document:

• Language models display covert racism by associating negative stereotypes with AAE, a dialect linked to African Americans. This is shown through experiments where AAE speakers are judged more harshly than those using Standard American English (SAE), implying deeper, systemic biases.

• The study introduces a novel method, Matched Guise Probing, to uncover these biases in language models. This method compares AI's responses to AAE and SAE texts, revealing a strong preference for SAE in various contexts.

• Despite efforts to reduce racial bias in AI through training models with human feedback, this study finds that such methods don't effectively address or can even worsen the issue of dialect prejudice. The models learn to mask overt racism while maintaining covert biases.

• This form of bias has real-world implications, potentially affecting employment and legal decisions. The AI models were found to associate AAE with lower prestige jobs and were more likely to suggest harsher legal penalties for speakers of AAE.

• The findings challenge the effectiveness of current bias mitigation strategies and emphasize the need for new approaches to address the subtleties of racial bias in AI technologies.

The document elaborates on the technical and social aspects of AI biases, calling attention to the critical need for addressing these issues to ensure fair and unbiased AI applications.

The summary from pages 16 to 42 of the document delves deeper into the methodologies used to investigate racial biases in language models, particularly focusing on African American English (AAE) and Standard American English (SAE). It explores the impact of dialect on AI decisions without any real evidence, utilizing language models to simulate judicial decisions. This includes an innovative approach where AI is tasked with making conviction decisions (i.e., acquitted or convicted, life or death penalties) based purely on the dialect used in the prompts, highlighting a systemic bias against AAE.

Key findings and methodologies include:

• Judicial Decision Simulation: The study simulates judicial decisions by AI, examining how language models may show bias in conviction rates between AAE and SAE, revealing a potential for racial bias in legal judgments.

• Scaling Analysis: This part investigates whether increasing the size of language models reduces dialect prejudice. It assesses models' familiarity with AAE through perplexity measurements, indicating larger models are somewhat better at understanding AAE but still show significant bias.

• Stereotype Strength: The research quantifies the strength of stereotypes associated with African Americans by comparing the language models' association of stereotypical and non-stereotypical adjectives with AAE.

• Human Feedback Analysis: It compares models trained with and without human feedback, evaluating their performance in terms of reducing bias. Surprisingly, models trained with human feedback don't necessarily exhibit less bias.

Furthermore, the document covers data availability, acknowledging public sources for datasets used in the study, and code availability for reproducibility. The extended data provides detailed results on the agreement between language models' covert stereotypes and human stereotypes about African Americans across different periods, suggesting that AI models may inherit and perpetuate societal biases found in historical data.

The concluding sections of the document, spanning pages 43 to 54, further explore and substantiate the study's findings regarding the bias in language models against African American English (AAE) compared to other dialects like Appalachian English (AE) and Indian English (IE). These sections present detailed statistical analyses to compare the stereotype strength associated with AAE against AE and IE, showing a significantly stronger bias towards AAE. The research methodologically assesses this bias through experiments designed to isolate and measure the impact of specific linguistic features of AAE on the perceived stereotypes.

Key highlights from these sections include:

• Stereotype Strength Analysis: The study conducts a deep dive into the stereotype strength of AAE, AE, and IE using datasets and matched guise probing. It concludes that AAE evokes stronger stereotypes compared to AE and IE, indicating a targeted prejudice against AAE and its speakers.

• Alternative Hypotheses Testing: The researchers test and refute alternative explanations for their findings. They explore whether the observed stereotypes are a general dismissal toward dialects or deviations from Standard American English (SAE) but find that the bias specifically targets AAE.

• Intelligence and Judicial Decision Making: Further experiments involve asking language models to judge the intelligence (IQ) of speakers based on the dialect of the presented texts. The results show a systematic bias where AAE speakers are more likely to be classified as having a lower IQ compared to SAE speakers.

• Comprehensive Reference List: The document concludes with an extensive list of references, showcasing the depth of research and the wide range of sources consulted to understand and analyze the biases present in language models.

These sections reinforce the initial findings of the study, providing a solid foundation for the claim that language models exhibit a significant bias against AAE, which could have profound implications for fairness and equality in AI applications. This bias is not just a general predisposition against dialects or non-standard English but a specific prejudice targeting AAE and its speakers, reflecting and potentially perpetuating societal racial biases.

Question 2 of 6 for ChatGPT-4: List all problems that the situations described in the paper can cause

ChatGPT-4’s answer:The situations described in the paper highlight several problems caused by the biases in language models, especially against African American English (AAE). These biases not only reflect existing societal prejudices but also have the potential to perpetuate and even amplify them through the use of AI and machine learning technologies. Here are the main problems arising from these situations:

1. Perpetuation of Racial Stereotypes: Language models displaying bias towards AAE over Standard American English (SAE) can reinforce and spread racial stereotypes, subtly reinforcing the marginalization of African American speakers and their cultural identity.

2. Impact on Employment Opportunities: AI used in hiring tools may unfairly judge candidates based on their dialect, potentially disadvantaging African American job applicants and contributing to employment discrimination.

3. Bias in Legal Systems: AI applications designed to assist in legal decision-making could exhibit bias in judgments against individuals who use AAE, leading to unfair legal outcomes and perpetuating systemic racism in judicial processes.

4. Educational Inequities: Language models that favor SAE over AAE can impact educational tools and resources, disadvantaging students who speak AAE and contributing to educational disparities.

5. Misrepresentation in Media and Content Creation: Content generated by biased AI can misrepresent African American speech and culture, leading to a lack of authentic representation and further entrenching stereotypes.

6. Barriers to Accessing Information and Services: Biased AI can lead to challenges in voice recognition, search algorithms, and other AI-driven services, making technology less accessible to speakers of AAE and exacerbating the digital divide.

7. Compromised User Experience: For users interacting with AI in customer service, virtual assistants, and other interactive technologies, biases against AAE can lead to misunderstandings, frustration, and a sense of exclusion.

8. Undermining Trust in AI: Recognizing biases in AI can lead to a loss of trust among minority communities, impacting the adoption and effective use of technology.

9. Ethical and Legal Concerns: The perpetuation of bias raises ethical questions about the development and deployment of AI technologies, potentially leading to legal challenges regarding discrimination and equality.

10. Limitations on AI's Potential for Social Good: Biased AI limits the potential for these technologies to be used as tools for social good, including efforts to bridge cultural divides and promote understanding.

Addressing these problems requires a concerted effort from researchers, developers, policymakers, and community stakeholders to ensure AI technologies are developed and used in a manner that is fair, equitable, and inclusive of all dialects and languages.

Question 3 of 6 for ChatGPT-4: How should AI makers and regulators address these issues and which other groups, organizations and individuals should be involved in trying to mitigate these problems?

ChatGPT-4’s answer:Addressing the issues of bias in AI, particularly the bias against African American English (AAE) and other dialects, requires a multifaceted approach involving AI developers, regulators, and a broad range of stakeholders. Here's how these issues can be addressed and the groups that should be involved:

AI Makers

1. Diverse Training Data: Ensure the training data for AI models is diverse and representative of different dialects, cultures, and communities. This includes not just AAE but other non-standard English dialects and languages from around the world.

2. Bias Detection and Mitigation Techniques: Implement and continually improve techniques for detecting and mitigating bias within AI models. This could include bias audits and the use of fairness metrics.

3. Inclusive Testing and Validation: Test AI systems with a diverse group of users to identify and address biases that may not be apparent at the development stage.

4. Transparency and Explainability: Make AI systems more transparent and explainable, allowing users to understand how decisions are made and to identify potential biases.

Regulators

1. Regulatory Frameworks: Develop and enforce regulatory frameworks that mandate fairness, accountability, and transparency in AI. This could include requirements for bias testing before deployment.

2. Standards for Data and Algorithms: Establish standards for the collection, use, and sharing of data, as well as for the algorithms that drive AI, to ensure they promote fairness and equity.

3. Public Reporting and Audits: Require companies to publicly report on the performance of their AI systems, including any identified biases, and undergo independent audits.

Other Stakeholders

1. Academic and Research Institutions: Continue research on the social impact of AI, the mechanisms of bias in AI, and the development of new methods to detect and mitigate bias. Collaborate with AI developers to apply these findings.

2. Civil Society and Advocacy Groups: Groups focused on civil rights, digital rights, and social justice should be involved in advocating for equitable AI, participating in the development of regulatory frameworks, and monitoring the deployment of AI systems.

3. Industry Consortia: Organizations within the tech industry should collaborate on best practices for ethical AI development, share resources for bias mitigation, and work together to establish industry-wide standards.

4. Users and Community Organizations: Involve users, particularly from marginalized communities, in the testing and feedback process for AI systems to ensure their voices are heard and their concerns are addressed.

Collaborative Efforts

Creating a coalition of AI developers, regulators, researchers, civil society organizations, and the public can facilitate a comprehensive approach to addressing AI bias. Such a coalition can:

• Foster dialogue and understanding between AI developers and the communities affected by AI technologies.

• Develop and disseminate educational materials on the ethical use of AI for developers, users, and policymakers.

• Organize workshops, hackathons, and competitions focused on creating fair and unbiased AI solutions.

• Establish a platform for sharing best practices, research findings, and tools for bias detection and mitigation.

By involving a diverse range of perspectives and expertise in the development, regulation, and oversight of AI technologies, it is possible to significantly reduce the impact of bias and ensure AI serves the interests of all communities equitably.

Question 5 of 6 for ChatGPT-4: Could AI makers get into trouble when not dealing with these matters?

ChatGPT-4’s answer:Yes, AI makers could face several forms of trouble when not addressing matters of bias and fairness in their AI systems:

Legal and Regulatory Trouble

1. Anti-discrimination Laws: In many jurisdictions, there are laws against discrimination based on race, gender, ethnicity, and other protected characteristics. AI systems that exhibit bias can lead to legal action against companies for violating these laws.

2. Regulatory Compliance: With increasing awareness of AI's social implications, governments and international bodies are developing regulations specific to AI ethics and fairness. Non-compliance with these regulations can result in fines, sanctions, or restrictions on operations.

Financial and Business Risks

1. Litigation Costs: Lawsuits related to biased AI outcomes can be costly, not just in terms of monetary settlements, but also legal fees and the resources required to defend against these lawsuits.

2. Reputation Damage: Public revelations of bias in AI systems can harm a company's reputation, leading to lost customers, partnerships, and investment.

3. Market Access and Expansion: Jurisdictions with stringent AI regulations may deny market access to companies that fail to meet fairness and ethics standards. This can limit growth opportunities for AI makers.

Ethical and Social Implications

1. Erosion of Trust: Failure to address bias can erode trust in AI technologies, both from users and the wider public. This can hinder the adoption of potentially beneficial AI solutions.

2. Social Harm: Biased AI systems can reinforce societal inequalities and cause harm to marginalized communities, contributing to wider social and ethical concerns.

How to Mitigate These Risks

1. Proactive Bias Management: Implementing a comprehensive strategy for identifying and mitigating bias throughout the AI development lifecycle can reduce the risk of producing biased AI systems.

2. Engagement with Stakeholders: Collaborating with a diverse range of stakeholders, including affected communities, can help identify potential biases and concerns early in the development process.

3. Transparency and Accountability: Being open about AI methodologies, decision-making processes, and efforts to combat bias can help build trust and demonstrate commitment to ethical AI.

4. Continuous Monitoring and Improvement: AI systems should be continuously monitored for bias post-deployment, with mechanisms in place for ongoing improvement and adaptation to new insights or societal changes.

By acknowledging and addressing the potential for bias in AI systems, makers can not only avoid legal, financial, and reputational troubles but also contribute to the development of more equitable and socially responsible AI technologies.

Question 6 of 6 for ChatGPT-4: What are the most controversial statements and findings in the paper?

ChatGPT-4’s answer:The paper on the racial bias of AI models, particularly against African American English (AAE), includes several controversial statements and findings that challenge existing notions of AI fairness and bias mitigation:

1. Inherent Bias Against AAE: One of the most controversial findings is the inherent bias within AI models against AAE, which demonstrates not just a general bias against non-standard English but a specific prejudice towards a dialect closely associated with African American identity. This finding challenges the belief that AI systems are neutral and unbiased.

2. Bias Mitigation Methods May Worsen Bias: The paper suggests that current methods aimed at mitigating bias, such as training AI with human feedback, do not effectively address and can even exacerbate the problem of dialect prejudice. This finding is controversial as it questions the efficacy of widely adopted bias mitigation strategies in the AI field.

3. Covert Racism in AI: The study introduces the concept of covert racism in AI, where AI models subtly perpetuate racial biases without overtly racist outputs. This is a provocative idea, suggesting that AI systems can contribute to systemic racism in more insidious ways than previously understood.

4. Legal and Employment Decision Bias: The paper’s findings on AI models making biased legal and employment decisions based on dialect use are particularly controversial. They imply that AI could contribute to systemic inequalities in critical areas such as the justice system and the job market, potentially leading to life-altering consequences for individuals based on the dialect they use.

5. Scaling AI Does Not Solve Bias: Another contentious finding is that simply increasing the size of AI models (scaling up) does not effectively reduce dialect prejudice. This challenges the common assumption in the AI community that larger models are inherently better and less biased.

6. Stereotype Strength and Language Models: The document's analysis of stereotype strength and its direct, causal link to linguistic features of AAE in AI models is controversial. It suggests that AI models not only reflect societal stereotypes but may actively reinforce them through their responses.

These findings are controversial because they challenge the current understanding and approaches to AI development and bias mitigation. They suggest that more radical rethinking and innovative approaches are needed to truly address racial bias in AI, beyond the current practices of diversifying training data and applying fairness metrics. Moreover, these controversies highlight the need for a broader societal conversation about the role of AI in perpetuating or combating systemic inequalities.