Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models

Introduction

In recent years, there has been a remarkable surge in the realm of artificial intelligence (AI), marked by the emergence of transformative technologies like ChatGPT and other generative language models (). These AI systems represent a class of sophisticated models designed to excel in generating human-like text and comprehending natural language. Using deep learning techniques and vast datasets, they have the ability to discern intricate patterns, make contextual inferences, and generate coherent and contextually relevant responses to a diverse range of inputs (). By mirroring human language capabilities, these models have unveiled a multitude of applications, ranging from chatbots and virtual assistants to translation services and content generation tools ().

Language models have ushered in a transformative era, underpinning the development of chatbots that emulate human interaction in conversations (). These chatbots have become vital tools, simplifying customer service, technical support, and information queries with human-like interaction. Furthermore, the integration of language models into virtual assistants has endowed them with the ability to provide precise and contextually appropriate responses to user inquiries (). Virtual assistants, thus enhanced, become indispensable aides, capable of managing tasks ranging from appointment scheduling to web searches and smart home device control ().

In the sphere of translation, harnessing the prowess of large language models facilitates markedly improved and fluent translations that span multiple languages, including those with limited resources (). Such capabilities not only foster enhanced cross-linguistic communication but can also enable timely solutions during emergencies and crises, especially in regions where low-resource languages or indigenous dialects are spoken ().

Moreover, the aptitude of language models to generate coherent and contextually pertinent text has rendered them invaluable in the realm of content creation. Acknowledged for their proficiency in producing various types of content, spanning articles, social media posts, and marketing materials, they have established a profound impact ().

These applications, among many others, underscore the transformative prowess of generative language models across an array of industries and sectors. However, as their adoption proliferates and their influence extends into ever-diverse domains (), it is imperative to confront the distinctive challenges posed by the potential biases that may be established within these models. These biases can have profound implications for users and society at large, highlighting the urgent need for comprehensive examination and mitigation of these issues .

Defining bias in generative language models

Bias, in the context of large language models such as GPT-4 () and predecessors (), or other state-of-the-art alternatives (; including multimodal variants, ), can be defined as the presence of systematic misrepresentations, attribution errors, or factual distortions that result in favoring certain groups or ideas, perpetuating stereotypes, or making incorrect assumptions based on learned patterns. Biases in such models can arise due to several factors (cf., Table 1).

One factor is the training data. If the data used to train a language model contain biases, either from the source material or through the selection process, these biases can be absorbed by the model and subsequently reflected in its behavior (). Biases can also be introduced through the algorithms used to process and learn from the data. For example, if an algorithm places more importance on certain features or data points, it may unintentionally introduce or amplify biases present in the data (). In (semi)supervised learning scenarios, where human annotators provide labels or annotations for the training data, biases may emerge from the subjective judgments of the annotators themselves, influencing the model’s understanding of the data ().

The choice of which use cases to prioritize or the design of user interfaces can also contribute to biases in large language models. For example, if a language model is primarily designed to generate content for a certain demographic or industry, it may inadvertently reinforce existing biases and exclude different perspectives (). Lastly, policy decisions can play a role in the manifestation of biases in language models. The developers of both commercial and openly available language models might implement policies that prevent (or encourage) a given model behavior. For example, both OpenAI and Microsoft have deliberate guardrails that modulate the behavior of ChatGPT and Bing-AI to mitigate unintended toxic model behaviors or prevent malicious abuse ().

2 Types of biases in Large Language Models

Large language models, which are commonly trained from vast amounts of text data present on the Internet, inevitably absorb the biases present in such data sources. These biases can take various forms (cf., Table 2).

Demographic biases arise when the training data over-represents or under-represents certain demographic groups, leading the model to exhibit biased behavior towards specific genders, races, ethnicities, or other social groups (). Cultural biases occur when large language models learn and perpetuate cultural stereotypes or biases, as they are often present in the data used for training. This can result in the model producing outputs that reinforce or exacerbate existing cultural prejudices (). Linguistic biases emerge since the majority of the internet’s content is in English or a few other dominant languages, making large language models more proficient in these languages. This can lead to biased performance and a lack of support for low-resource languages or minority dialects (). Temporal biases appear as the training data for these models are typically restricted to limited time periods or have temporal cutoffs. This may cause the model to be biased when reporting on current events, trends, and opinions. Similarly, the model’s understanding of historical contexts or outdated information may be limited due to a lack of temporally representative data (). Confirmation biases in the training data may result from individuals seeking out information that aligns with their pre-existing beliefs. Consequently, large language models may inadvertently reinforce these biases by providing outputs that confirm or support specific viewpoints (). Lastly, ideological and political biases can be learned and propagated by large language models due to the presence of such biases in their training data. This can lead to the model generating outputs that favor certain political perspectives or ideologies, thereby amplifying existing biases ().

This paper aims to explore the question of whether language models like GPT-4 (), its prior versions (), or other commercial or open-source alternatives () that power applications like ChatGPT () (or similar) should be biased or unbiased, taking into account the implications and risks of both perspectives. By examining the ethical, practical, and societal consequences of each viewpoint, we hope to contribute to the ongoing discussion surrounding responsible language model development and use. Through this exploration, our goal is to provide insights that can help guide the future evolution of GPT-style and other generative language models toward more ethical, fair, and beneficial outcomes while minimizing potential harm.

Why are generative language models prone to bias?

ChatGPT and other applications based on large language models are trained using a process that primarily relies on unsupervised learning, a machine learning technique that enables models to learn patterns and structures from vast amounts of unlabelled data (). In most cases with these language models, the data consists of extensive text corpora available on the internet, which includes websites, articles, books, and other forms of written content ().

ChatGPT in particular is trained on a diverse range of internet text datasets that encompass various domains, genres, and languages. While the specifics of the dataset used for GPT-4 are proprietary (), the data sources utilized for training its predecessor, GPT-3, likely share similarities. For GPT-3 and predecessors, the primary dataset used was WebText (), which is an ever-growing large-scale collection of web pages (). WebText was created by crawling the internet and gathering text from web pages. The sources of data include, but are not limited to:

Websites: Text is extracted from a wide array of websites, covering topics such as news, blogs, forums, and informational websites like Wikipedia. This enables the model to learn from diverse sources and gain knowledge on various subjects.

Books: Text from books available online, including both fiction and non-fiction, contributes to the training data. This helps the model to learn different writing styles, narrative structures, and a wealth of knowledge from various fields.

Social media platforms: Content from social media platforms, like Twitter, Facebook, and Reddit, is incorporated to expose the model to colloquial language, slang, and contemporary topics of discussion.

Conversational data: To improve the model’s conversational abilities, text from chat logs, comment sections, and other conversational sources are also included in the training dataset.

The developers of ChatGPT note that the WebText data is preprocessed and filtered to remove low-quality content, explicit material, web and social spam , and other undesirable text before being fed into the model (). However, due to the vast scale of the data and the limitations of current filtering techniques, some undesirable or biased content may still seep into the training dataset, affecting the behavior of the resulting model. In addition to WebText, GPT-3 was further trained using a filtered version of the Common Crawl dataset (https://commoncrawl.org), a publicly available, massive web-crawled dataset that contains raw web page data, extracted metadata, and text content from billions of web pages in multiple languages ().

Another commonly-used dataset for language model training is The Pile () an extensive and diverse collection of 22 smaller datasets, combining various sources of scientific articles, books, and web content. It is designed for training large-scale language models, particularly in the domain of scientific research and understanding.

2 Biases from the models

During the training process, generative language models are exposed to billions of sentences and phrases, allowing them to learn the intricate relationships between words, grammar, context, and meaning (). As they process the text data, they gradually acquire natural language generation capabilities, enabling them to produce coherent and contextually relevant responses to various inputs. However, some capabilities of these models can lead to bias (cf., Table 3):

One crucial aspect of these models is their ability to generalize, which allows them to apply the knowledge gained from their training data to new and previously unseen inputs, providing contextually relevant responses and predictions even in unfamiliar situations. However, this ability also raises concerns about potential biases, as models may inadvertently learn and perpetuate biases present in their training data, even if the data has been filtered and cleaned to the extent possible ().

Propagation

As these models learn from the patterns and structures present in their training data, they may inadvertently absorb and propagate biases they encounter, such as adopting stereotypes, favoring certain groups or ideas, or making assumptions based on learned patterns that do not accurately represent the full spectrum of human experience. This propagation of biases during training poses significant challenges to the development of fair and equitable AI systems, as biased models can lead to unfair treatment, reinforce stereotypes, and marginalize certain groups ().

Emergence

In large language models, the phenomenon of emergence, which refers to the spontaneous appearance of unanticipated capabilities despite these functionalities not being explicitly encoded within the model’s architecture or training data, can also result in unexpected biases due to the intricate interplay between model parameters and biased training data (). The high-dimensional representations and non-linear interactions in these models make it difficult to predict or control these emergent biases, which may manifest in various ways, such as stereotyping, offensive language, or misinformation. To address this challenge, researchers are exploring bias mitigation strategies during training, fine-tuning with curated datasets, and post-hoc emergent bias analyses ().

Non-linearity

The non-linear relationships between biases in the system or data and their real-world impact imply that small biases may have massive negative effects, and large biases might not result in significant consequences. This disproportionality arises due to the complex interdependencies between the model parameters and the high-dimensional representations learned during training . Randomized controlled trials could be used to draw causal relationships between the extent of each bias and their effects. In the absence of that, due to ethical reasons, multifaceted approaches involving in-depth analysis of model behavior, rigorous evaluation with diverse benchmarks, and the application of mitigation techniques that account for the nonlinear nature of emergent biases are needed .

Alignment

To address these issues, a strategy known as Reinforcement Learning with Human Feedback (RLHF) () was developed to fine-tune large language models like ChatGPT to reduce their biases and align them with human values. This approach involves collecting a dataset of human demonstrations, comparisons, and preferences to create a reward model that guides the fine-tuning process . InstructGPT (ChatGPT’s default model) () is trained using RLHF and then fine-tuned using Proximal Policy Optimization (PPO), a policy optimization algorithm (). It is paramount to understand if the same principles could be exploited to deliberately misalign a model.

3 Can bias be mitigated with human-in-the-loop approaches?

Bias in generative language models can be mitigated to some extent with human-in-the-loop (HITL) approaches. These approaches involve incorporating human input, feedback, or oversight throughout the development and deployment of the language model, which can help address issues related to biases and other limitations. Here are some ways to integrate human-in-the-loop approaches to mitigate bias:

Training data curation: Humans can be involved in curating and annotating high-quality and diverse training data. This may include identifying and correcting biases, ensuring a balance of perspectives, and reducing the influence of controversial or offensive content ().

Model fine-tuning: Subject matter experts can guide the model fine-tuning process by providing feedback on the model’s outputs, helping the model generalize better and avoid biased or incorrect responses ().

Evaluation and feedback: Human reviewers can evaluate the model’s performance and provide feedback to developers, who can then iteratively improve the model. This feedback loop is essential for identifying and addressing bias-related issues. ().

Real-time moderation: Human moderators can monitor and review the model’s outputs in real-time, intervening when necessary to correct biased or inappropriate responses. This approach can be especially useful in high-stakes or sensitive applications ().

Customization and control: Users can be provided with options to customize the model’s behavior, adjusting the output according to their preferences or requirements. This approach can help users mitigate bias in the model’s responses by tailoring it to specific contexts or domains ().

While human-in-the-loop approaches can help mitigate bias, it is essential to recognize that they may not be able to eliminate it entirely. Bias can stem from various sources, such as the training data, fine-tuning process, or even the human reviewers themselves. However, combining machine learning techniques with human expertise can be a promising way to address some of the challenges posed by biases in generative language models.

The inevitability of some forms of bias

Completely eliminating bias from large language models is a complex and challenging task due to the inherent nature of language and cultural norms. Since these models learn from vast amounts of text data available on the internet, they are exposed to the biases present within human language and culture. Addressing bias in these models involves tackling several key challenges (cf., Table 4).

First, human language is a reflection of society and as such, it contains various biases, stereotypes, and assumptions. Separating useful patterns from these biases can be challenging, as they are often deeply ingrained in the way people express themselves and the structures of language itself (). Second, cultural norms and values can vary significantly across different communities and regions. What is considered acceptable or appropriate in one context may be seen as biased or harmful in another. Determining which norms should be encoded in AI models and which should be filtered out is a complex task that requires careful consideration and a nuanced understanding of diverse cultural perspectives ().

Furthermore, fairness is a subjective concept that can be interpreted in various ways. Completely eliminating bias from AI models would require developers to define what “fair” means in the context of their applications, which can be a challenging task, given the diverse range of stakeholders and perspectives involved (). Lastly, language and culture are constantly evolving, with new expressions, norms, and biases emerging over time. Keeping AI models up-to-date with these changes and ensuring that they remain unbiased is an ongoing challenge that requires continuous monitoring and adaptation ().

Despite these challenges, it is essential for developers, researchers, and stakeholders to continue working towards reducing bias in large language models. By developing strategies for identifying and mitigating biases, collaborating with diverse communities, and engaging in ongoing evaluation and improvement, we can strive to create AI systems that are more equitable, fair, and beneficial for all users.

2 Utility despite bias?

Biased AI models can still be useful in certain contexts or applications, as long as users are aware of their limitations and take them into account when making decisions. In some cases, the biases present in these models may even be representative of the real-world context in which they are being used, providing valuable insights in surfacing societal inequalities that need to be tackled at their root.

The key to leveraging biased AI models responsibly is to ensure that users have a clear understanding of the potential biases and limitations associated with these models, so they can make informed decisions about whether and how to use them in different contexts. Some strategies for addressing this issue include:

Transparency: Developers should be transparent about the methodologies, data sources, and potential biases of their AI models, providing users with the necessary information to understand the factors that may influence the model’s predictions and decisions. Best practices about the documentation of models and data have been advanced by the AI community ().

Education and awareness: Providing resources, training, and support to help users better understand the potential biases in AI models and how to account for them when making decisions. This may involve creating guidelines, best practices, or other educational materials that explain the implications of bias in AI and how to navigate it responsibly.

Context-specific applications: In some limited cases, biased AI models may be viable for specific applications or contexts where their biases align with the relevant factors or considerations. Experts should be employed to carefully evaluate the appropriateness of using biased models in these situations, taking into account the potential risks and benefits associated with their use, and actionable plans to recognize, quantify, and mitigate biases.

Continuous monitoring and evaluation: Regularly assessing the performance of AI models in real-world contexts, monitoring their impact on users and affected communities, and making adjustments as needed to address any biases or unintended consequences that emerge over time ().

By acknowledging that biased models can still be useful in limited contexts and taking steps to ensure that users are aware of their limitations and qualified to recognize and mitigate them, we can promote the responsible use of AI technologies and harness their potential benefits while minimizing the risks associated with bias.

The broader risks of generative AI bias

Ethical considerations of fairness and equality play a crucial role in the development and deployment of generative AI applications. As these models integrate increasingly into various aspects of our lives, their potential impact on individuals and society as a whole becomes a matter of significant concern. The responsibility lies with developers, researchers, and stakeholders to ensure that AI models treat all users and groups equitably, avoiding the perpetuation of existing biases or the creation of new ones (cf., Table 5).

One key ethical consideration is representation. It is essential to ensure that the training data used to develop generative AI models are representative of the diverse range of perspectives, experiences, and backgrounds that exist within society (). This helps to reduce the risk that biases are absorbed and propagated by models, leading to more equitable outcomes. Transparency is another important aspect. Developers should be transparent about the methodologies, data sources, and potential limitations of their generative AI models (). This enables users to better understand the factors that may influence the model’s predictions and decisions. Accountability is also crucial for responsible generative AI development. Developers and stakeholders must establish a clear framework for accountability, which may include monitoring the performance of AI models, addressing biases and errors, and responding to users and communities affected (). An unique aspect of generative AI, compared to traditional machine learning, is its ability to possibly replace human artistic expression or to plagiarize the style and uniqueness of human work: as such, preservation of intellectual property, copyright protection and prevention of plagiarism are paramount . Inclusivity is another key ethical consideration. Generative AI applications should be designed to be inclusive and accessible to all users, taking into account factors such as language, culture, and accessibility needs (). This ensures that the benefits of AI are shared equitably across society.

Lastly, continuous improvement is vital to achieve fairness and equality in generative AI applications. Developers must commit to an ongoing process of evaluating, refining, and improving their AI models to address biases and ensure fairness over time (). This may involve collaborating with researchers, policymakers, and affected communities to gain insight and feedback that can help guide the development of more equitable AI systems.

By prioritizing ethical considerations of fairness and equality, AI developers can create applications that not only harness the power of advanced technologies such as large language models, but also promote a more just and inclusive society, where the benefits and opportunities of AI are accessible to all.

2 The risks of exacerbating existing societal biases

Bias in widely-adopted AI models, including ChatGPT and other generative language models, can have far-reaching consequences that extend beyond the immediate context of their applications. When these models absorb and propagate biases, including those present in their training data, they may inadvertently reinforce stereotypes, marginalize certain groups, and lead to unfair treatment across various domains. Some examples of how biased AI models can adversely impact different areas include:

Hiring: AI-driven hiring tools that use biased models may exhibit unfair treatment towards applicants from underrepresented groups or those with non-traditional backgrounds. This could lead to the perpetuation of existing inequalities in the job market, limiting opportunities for affected individuals and reducing diversity in the workforce (). Large language models can be used to automate the screening of job applicants, such as by analyzing resumes and cover letters. Since these models are trained on vast amounts of text data, they may have internalized biases present in the data, such as gender or racial biases. As a result, they could unintentionally favor certain applicants or disqualify others based on factors unrelated to their qualifications, reinforcing existing inequalities in the job market.

Lending: Financial institutions increasingly rely on AI models for credit scoring and lending decisions. Biased models may unfairly penalize certain groups, such as minority communities or individuals with lower socio-economic status, by assigning them lower credit scores or denying them access to loans and financial services based on biased assumptions (). In lending, large language models can be used to assess creditworthiness or predict loan default risk, e.g., based on automated analysis of application or support documents. If the data used to train these models contain historical biases or discriminatory lending practices, the models may learn to replicate these patterns. Consequently, they could deny loans to certain demographics or offer unfavorable terms based on factors like race, gender, or socioeconomic status, perpetuating financial inequality ().

Content moderation: AI-powered content moderation systems help manage and filter user-generated content on social media platforms and other online communities. If these systems are trained on biased data, they may disproportionately censor or suppress the voices of certain groups, while allowing harmful content or misinformation from other sources to proliferate (). Language models can be employed to automatically moderate and filter content on social media platforms or online forums. However, these models may struggle to understand the nuances of language, context, and cultural differences. They might over-moderate or under-moderate certain types of content, disproportionately affecting certain groups or topics. This could lead to censorship or the amplification of harmful content, perpetuating biases and misinformation ().

Healthcare: AI models are increasingly used to support medical decision-making and resource allocation. Biased models may result in unfair treatment for certain patient groups, leading to disparities in healthcare access and quality, and potentially exacerbating existing health inequalities (). Large language models can be employed for tasks such as diagnosing diseases, recommending treatments, or analyzing patient data (). If the training data includes biased or unrepresentative information, the models may produce biased outcomes. Data used to train these models might be predominantly collected from specific populations, leading to less accurate predictions or recommendations for underrepresented groups. This can result in misdiagnoses, inadequate treatment plans, or unequal access to care. Models might unintentionally learn to associate certain diseases or conditions with specific demographic factors, perpetuating stereotypes and potentially influencing healthcare professionals’ decision-making. Finally, biases in healthcare data could lead to models that prioritize certain types of treatments or interventions over others, disproportionately benefiting certain groups and disadvantaging others ().

Education: AI-driven educational tools and platforms can help personalize learning experiences and improve educational outcomes. However, biased models may perpetuate disparities by favoring certain learning styles or cultural backgrounds, disadvantaging students from underrepresented or marginalized communities (). Large language models can be used in education for tasks such as personalized learning, grading, or content creation. If the models are trained on biased data, they may exacerbate existing biases in educational settings. Furthermore, if used for grading or assessing student work, language models might internalize biases in historical grading practices, leading to unfair evaluation of students based on factors like race, gender, or socioeconomic status. Finally, access in itself to ChatGPT or other AI tools for education can exacerbate preexisting inequalities.

3 Paths to AI transparency

Transparency and trust are essential components in the development and deployment of AI systems. As these models become more integrated into various aspects of our lives, it is increasingly important for users and regulators to understand how they make decisions and predictions, ensuring that they operate fairly, ethically, and responsibly.

Emphasizing transparency in AI systems can provide several benefits:

Informed decision-making: When users and regulators have a clear understanding of how AI models make decisions and predictions, they can make more informed choices about whether to use or rely on these systems in different contexts. Transparency can empower users to evaluate the potential risks and benefits of AI systems and make decisions that align with their values and priorities ().

Public trust: Fostering transparency can help build public trust in AI systems, as it demonstrates a commitment to ethical development and responsible deployment. When users and regulators can see that developers are taking steps to ensure the fairness and equity of their models, they may be more likely to trust and adopt these technologies ().

Ethical compliance: By promoting transparency, developers can demonstrate their compliance with ethical guidelines and regulations, showcasing their commitment to the responsible development of AI systems. This can help to establish a strong reputation and foster positive relationships with users, regulators, and other stakeholders ().

Collaborative improvement: Transparency can facilitate collaboration between developers, researchers, policymakers, and affected communities, enabling them to share insights and feedback that can help guide the development of more equitable and ethical AI systems ().

In summary, emphasizing transparency and trust in AI systems, including generative language models, is crucial for ensuring that users and regulators have a clear understanding of how these models make decisions and predictions. By promoting transparency, developers can demonstrate their commitment to ethical development and responsible deployment, fostering public trust and paving the way for more equitable and beneficial AI applications.

4 Regulatory efforts, industry standards, and ethical guidelines

As concerns about bias in AI systems continue to grow, several ongoing regulatory efforts and industry standards have emerged to address these challenges. AI ethics guidelines and fairness frameworks aim to provide guidance and best practices for developers, organizations, and policymakers to reduce bias and ensure responsible development and deployment of AI systems ().

Some notable efforts are summarized in Table 6. These ongoing regulatory efforts and industry standards represent important steps in addressing bias and promoting the responsible development of AI systems. By adhering to these guidelines and frameworks, developers can contribute to the creation of AI technologies that are more equitable, fair, and beneficial for all users and communities.

The role of human oversight and intervention

Identifying and mitigating bias in AI models is essential for ensuring their responsible and equitable use. Various methods can be employed to address bias in AI systems:

Regular audits: Conducting regular audits of AI models can help identify potential biases, errors, or unintended consequences in their outputs. These audits involve evaluating the model’s performance against a set of predefined metrics and criteria, which may include fairness, accuracy, and representativeness. By monitoring AI models on an ongoing basis, developers can detect and address biases before they become problematic ().

Retraining with curated data: Retraining AI models with curated data can help reduce biases in their predictions and decisions. By carefully selecting and preparing training data that is more diverse, balanced, and representative of different perspectives, developers can ensure that AI models learn from a broader range of inputs and experiences, which may help mitigate the influence of biases present in the original training data ().

Applying fairness metrics: Fairness metrics can be used to evaluate the performance of AI models with respect to different user groups or populations (). By analyzing AI model outputs based on these metrics, developers can identify potential disparities or biases in the model’s treatment of different users and take steps to address them. Examples of fairness metrics include demographic parity, equalized odds, and equal opportunity ().

Algorithmic debiasing techniques: Various algorithmic techniques have been developed to mitigate bias in AI models during training or post-processing. Some of these techniques include adversarial training, re-sampling, and re-weighting, which aim to minimize the influence of biased patterns and features on the model’s predictions and decisions ().

Inclusion of diverse perspectives: Ensuring that AI development teams are diverse and inclusive can help bring a wide range of perspectives and experiences to the table, which can contribute to the identification and mitigation of biases in AI models. By involving individuals from different backgrounds, cultures, and disciplines, developers can create more robust, fair, and representative AI systems ().

Human-in-the-loop approaches: Incorporating human experts into the AI model development and decision-making processes can help provide valuable contextual understanding and ethical judgment that AI models may lack. Humans can serve as an additional layer of quality control, identifying biases, errors, or unintended consequences in AI model outputs and providing feedback to improve the model’s performance and fairness.

Next, we dive deeper into the role that human experts can play in the responsible design and development of AI systems, including large language models, and their continuous oversight.

2 The importance of humans in the AI loop

Emphasizing the importance of involving human experts in AI system development, monitoring, and decision-making is crucial to ensuring the responsible and ethical deployment of AI technologies. Humans possess the ability to provide context and ethical judgment that AI models may lack, and their involvement can help address potential biases, errors, and unintended consequences that may arise from the use of these systems. Some key benefits of involving human experts in AI system development include:

Contextual understanding: Human experts can provide valuable insights into the cultural, social, and historical contexts that shape language and communication, helping to guide AI models in generating more appropriate and sensitive responses ().

Ethical judgment: Human experts possess the moral and ethical reasoning skills needed to evaluate the potential impacts of AI systems on users and affected communities. By involving human experts in decision-making, we can ensure that AI models align with ethical principles and values, such as fairness, transparency, and accountability ().

Bias identification and mitigation: Human experts can help identify and address biases in AI models, working alongside developers to implement strategies for mitigating or eliminating harmful biases and promoting more equitable and representative AI systems ().

Quality assurance and validation: Human experts can serve as a vital layer of quality control, evaluating AI model outputs for coherence, relevance, and potential biases, and providing feedback to improve the model’s performance, accuracy, regulatory compliance, and trustworthiness ().

Human override: Incorporating human experts into AI system workflows can help strike a balance between automation and human judgment, allowing humans to intervene and override AI model decisions when necessary to ensure fairness, accountability, and ethical compliance ().

By involving human experts in the development, monitoring, and decision-making processes of AI systems, we can leverage their contextual understanding and ethical judgment to complement the capabilities of AI models. This collaborative approach can help us create AI systems that are more responsible, equitable, and beneficial for all users, while also addressing the potential risks and challenges associated with bias in AI.

3 Possible strategies and best practices to address bias in generative AI

Addressing and mitigating potential biases in generative AI models requires a collaborative effort between AI developers, users, and affected communities. Fostering a more inclusive and fair AI ecosystem involves engaging various stakeholders in the development, evaluation, and deployment of AI technologies. This collaboration can oversee that AI models are designed to be more equitable, representative, and beneficial to all users.

Some key aspects of fostering collaboration in the AI ecosystem are shown in Table 7. By fostering collaboration between AI developers, users, and affected communities, we can work towards creating a more inclusive and fair AI ecosystem that respects and values diverse perspectives and experiences. This collaborative approach can help ensure that AI technologies are developed and deployed in a way that is equitable, responsible, and beneficial for all users, while also addressing the potential risks and challenges associated with bias in AI.

Conclusions

This paper highlights the challenges and risks associated with biases in generative language models like ChatGPT, emphasizing the need for a multi-disciplinary, collaborative effort to develop more equitable, transparent, and responsible AI systems that enhance a wide array of applications while minimizing unintended consequences.

Various methods for identifying and mitigating bias in AI models were presented, including regular audits, retraining with curated data, applying fairness metrics, and incorporating human experts in AI system development, monitoring, and decision-making. To achieve this goal, the development and deployment of AI technologies should prioritize ethical principles, such as fairness and equality, ensuring that all users and groups are treated equitably. Human oversight plays a vital role in providing context and ethical judgment that AI models may lack, helping to identify and address potential biases, errors, or unintended consequences. Collaboration between AI developers, users, and affected communities is essential for fostering a more inclusive and fair AI ecosystem, ensuring that diverse perspectives and experiences are considered and valued.

Continued research into methods for identifying, addressing, and mitigating biases in AI models will be critical to advancing the state of the art and promoting more equitable and inclusive AI systems. By bringing together experts from various disciplines, including computer science, social sciences, humanities, and ethics, we can foster a more comprehensive understanding of the potential biases and ethical challenges associated with AI applications.

Fostering an open and ongoing dialogue between stakeholders is crucial for sharing knowledge, best practices, and lessons learned from the development and use of AI applications. This dialogue can help to raise awareness of the potential risks and challenges associated with biases in AI models and promote the development of strategies and guidelines for mitigating their negative impacts.

As the development of large language models continues, several essential aspects of research are necessary to advance their understanding and ensure their responsible deployment (cf., Table 8). Among these are understanding their inner workings, addressing ethical concerns, ensuring controllability and safety, and developing more robust evaluation methods.

One critical area of research is fairness, bias, and ethics, which involves detecting, mitigating, and preventing biases in AI models to minimize their impact on various sectors of society. Another important research question is interpretability and explainability, as understanding the internal workings of large language models remains a significant challenge. Researchers are working to make models more interpretable and explain their predictions. Additionally, large language models can influence decision-making and shape public discourse, making auditability and accountability essential for fostering trust and maintaining ethical standards in the AI community and beyond.

Controllable and safe AI is also important to ensure that AI models can generate outputs that align with human intentions and values. Finally, observing, characterizing, quantifying, and understanding the broader effects that deploying AI systems has on society is critical for addressing societal effects, which encompass a wide range of concerns, including labor markets, privacy, bias, access to technology, public discourse, security, ethics, and regulation. The need for a multi-disciplinary, collaborative effort to develop more equitable, transparent, and responsible AI systems is clear.

This paper emphasized the challenges and risks associated with biases in generative language models like ChatGPT and highlights the importance of continued research to develop responsible AI systems that enhance a wide array of applications while minimizing unintended consequences.

Acknowledgements

The author is grateful to all current and past members of his lab at USC, and the numerous colleagues and students at USC Viterbi and Annenberg, who engaged in stimulating discussions and provided invaluable feedback about this study.

References