=Paper=
{{Paper
|id=Vol-3808/paper14
|storemode=property
|title=Bias, Subjectivity and Norm in Large Language Models
|pdfUrl=https://ceur-ws.org/Vol-3808/paper14.pdf
|volume=Vol-3808
|authors=Thierry Poibeau
|dblpUrl=https://dblp.org/rec/conf/aequitas/Poibeau24
}}
==Bias, Subjectivity and Norm in Large Language Models==
https://ceur-ws.org/Vol-3808/paper14.pdf
Bias, Subjectivity and Norm in Large Language
Models
Thierry Poibeau1
1
Lattice Lab., École normale supérieure-PSL & CNRS 45 rue d’Ulm 75005 Paris, France
Abstract
This article reevaluates the concept of bias in Large Language Models, highlighting the inherent and
varying nature of these biases and the complexities involved in post hoc adjustments to meet legal and
ethical standards. It argues for shifting the focus from seeking bias-free models to enhancing transparency
in filtering processes, tailored to specific use cases, acknowledging that biases reflect societal values.
Keywords
Large Language Models, Bias, Norm, Subjectivity
1. Introduction
Large Language models (LLMs), particularly generative models like GPT, have become promi-
nent in natural language processing due to their effectiveness across various tasks and languages.
Despite their known architecture [1], their internal operations remain largely opaque, raising
questions about their ability to generalize linguistic phenomena and handle subjective informa-
tion, such as differing opinions and cultural preferences. A significant challenge is the potential
for these models to generate undesired content, including violent, misogynistic or racist remarks.
To address this, various techniques are used to filter and mitigate problematic elements during
both training and content generation, often relying on presumed reliable sources like Wikipedia
and sanitized internet data.
The issue at hand has been extensively addressed in the literature through the concept of bias.
Biases inherently entail negative elements that should be eliminated. A substantial segment of
the NLP field is now dedicated to the process of debiasing models [2, 3]. The aim is to remove
the biases until we obtain a neutral model, which would allow less discriminatory use. One of
the main challenges of eliminating biases lies in defining and identifying them, necessitating a
norm against which these biases can be measured, However, it appears improbable that we can
establish a completely bias-free and objective world to which language models can conform
once cleansed of spuriously learned associations. In this position paper, we defend the idea that
the world contain subjective elements (opinions, tastes, preferences) that cannot be “objectified”,
with the consequence that the “norm” implicitly required to debias models does not exist. In
other words, debiasing” in itself reflect a point of view and thus is not neutral.
The paper is structured as follows: Section 2 offers a brief review of the concept of bias.
AEQUITAS 2024: Workshop on Fairness and Bias in AI | co-located with ECAI 2024, Santiago de Compostela, Spain
$ thierry.poibeau@ens.psl.eu (T. Poibeau)
0000-0003-3669-4051 (T. Poibeau)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
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Workshop ISSN 1613-0073
Proceedings
In Section 3, we address the absence of a universal "ground truth" for the task, leading to
the conclusion that achieving completely unbiased models is unattainable. Finally, Section 4
presents some proposals for better addressing this subjectivity.
2. Previous Work
Blodgett et al. [4] highlights the importance of precisely defining the terms used when talking
about notions such as biases. We revisit this very concept, delving into the inherent connection
between biases and the data employed in training a model.
2.1. Language Models as a Mirror of Society
A cognitive bias refers to a consistent deviation from established norms or rational thinking
in the process of judgment [5]. People form their own "subjective reality" based on how they
perceive information. It is the individual’s construction of reality, rather than the objective
information itself, that can influence their actions in the world. Consequently, cognitive biases
can result in distortions in perception, flawed judgment, illogical interpretations, and irrational
behavior.
As early as 2016, in a seminal article, Bolukbasi et al. [6] set out the problem clearly: language
models reflect the data on which they are trained, and therefore indirectly society. We could
legitimately say that it is society that needs to be acted upon (which is not wrong in itself, but
does not really answer the question), and developers must also take their share of responsibility
(the quote refers to word embeddings, but it can be transposed to language models in general).
One perspective on bias in word embeddings is that it merely reflects bias in society,
and therefore one should attempt to debias society rather than word embeddings.
However, by reducing the bias in today’s computer systems (or at least not ampli-
fying the bias), which is increasingly reliant on word embeddings, in a small way
debiased word embeddings can hopefully contribute to reducing gender bias in
society. At the very least, machine learning should not be used to inadvertently
amplify these biases, as we have seen can naturally happen. [6]
The quotation from Bolukbasi et al. [6] thus highlights the link between these models and the
society they reflect. Language plays a central role in this: Blodgett et al. [4] describes language
itself as a means of maintaining and/or reinforcing social hierarchies.
2.2. Mitigating and/or Removing Bias
Many studies have highlighted the presence of bias in language models [7, 8, 9, 10, 11], among
others. The means of attenuating and/or eliminating these biases has therefore logically become
a major research theme and a large number of techniques have been proposed [12, 13, 9, 14,
15, 16]. This inventory is merely illustrative and necessarily very partial, given the increase in
publications on this subject in recent years.
However, these studies remain partial (most focus on gender bias, others on race or religion,
but the different aspects are rarely treated together). Furthermore, as noted by Meade et al. [3],
the effectiveness of techniques and their impact on processing algorithms is also often left out
of the equation. Finally, aiming at eliminating bias implies being able to recognise it. But the
notion of bias is complex, and implies a deviation from a norm, as we saw in the previous section.
We do not question the need to propose methods to reduce bias in models, but eliminating it
implies being able to achieve an objective description of reality, a notion discussed by Waseem
et al. [17]. These authors challenge the “solutionism” of the algorithmic approaches proposed:
while algorithms are useful, they also suffer from their own subjectivity and are not a universal
solution.
2.3. Practical and Regulatory Considerations
To complete our observations and take into account societal concerns, we decided to complete
our study by interviewing representative experts. These individuals include engineers deeply
entrenched in the development of language models and their applications, as well as members
of think tanks and regulatory bodies.
These professionals were very careful with the notion of bias. They all consider that biases
are entrenched in society and thus an integral part of corpora. This does not mean that there is
nothing to be done. They advocate for the regulation of practical applications of LLMs, rather
than trying to regulate the models themselves. This approach seeks to ensure that specific
measures are meticulously tailored to each unique application. The overarching criterion here
is the prevention of discrimination against individuals who are the target of these applications.
At the very least, these models should refrain from exacerbating existing biases ingrained
within society. This application-centric strategy (which is also the one of the AI Act recently
voted in Europe [18]), while effective, inherently introduces complexity when it comes to
formulating general principles. Simultaneously, there exists a unanimous consensus among
these professionals regarding the paramount importance of preserving a substantial degree
of freedom for theoretical AI research (especially for the development of LLMs, that remains
models and not appliocations; a difference should be made for example between GPT models,
and applications like ChatGPT).
These observations extend to more nuanced considerations concerning regulation. A hot
topic in the domain is for example the idea of model certification (which refers to the idea that
models would have to be assessed and certified prior any commercial use). While this idea may
initially seem enticing, it is crucial to acknowledge other potential issues. The cost associated
with certification, as articulated by these professionals, could cast a substantial shadow over
smaller enterprises, potentially leading to distortions in commercial competition. However,
these intricacies, while significant, venture beyond the immediate scope of this study.
3. The Lack of a Universal “Ground truth”, or the End of
Objectivity
As we have observed, the concept of bias inherently presupposes the existence of a norm, yet
this norm is profoundly relative, contingent upon culture, ideology, or even the perspective of
an individual.
3.1. The Relativity of Universal Notions
Contrary to the inherent subjectivity we have described, the prevailing literature in the field
often operates on the presumption of objectivity, where representations within language models
are expected to be rendered devoid of subjectivity, including bias, point of view, or opinion.
However, as Waseem et al. [17] have pointed out, this assumption is indeed an illusion. For
instance, the interpretation of principles such as freedom of expression can diverge significantly
between a European and American vantage point. Certain associations, like those between
gender and profession, which may seem to some as clear-cut biases warranting elimination,
are experienced differently across countries, cultures, and political beliefs. Natural Language
Processing (NLP) predominantly reflects the values and perspectives of Western culture, which,
by its very nature, lacks universality.
The challenge of mitigating bias within such a context is arduous, as we operate within a
relatively subjective domain where there is no universal "ground truth." While test and training
datasets can be curated to exclude blatantly problematic elements, there exist more intricate
cases that elude straightforward binary classifications of bias or no bias. Furthermore, the
approach to addressing bias is also a crucial consideration, as Bolukbasi et al. [6] reveal that
suppressing, rather than simply mitigating, biases can yield unintended consequences. Waseem
et al. [17] emphasize that de-biasing methods can only rectify a fraction of the biases present.
Barocas et al. [19] provide valuable insights into this intricate conundrum, suggesting the
need to differentiate between tasks with accessible ground truth and those without. In this
particular scenario, we find ourselves in the latter category, where the concept of bias lacks a
consensus definition and universal understanding. Barocas presages that technologies developed
within such an uncertain framework may have limited success, owing to the inherent ambiguity
surrounding the ideal outcome. Even seemingly neutral concepts like "good quality" text
for learning, used in various publications, exhibit inherent biases favoring the language and
perspectives of well-educated and affluent classes, as demonstrated by Gururangan et al. [20].
3.2. Bias and Freedom of Expression.
Finally, it is important to highlight the complexity of these issues, which relate to the broader
context of freedom of expression and its various social impacts. This dilemma is clear, especially
with the challenges posed by social networks. Regulating these platforms is necessary to prevent
abuses like hate speech, harassment, and defamation. However, too much regulation could also
threaten freedom of expression.
The question of who sets the rules for these networks is another layer of complexity. When
networks establish their own guidelines, it raises concerns because it means that private entities
are effectively determining the boundaries of freedom of expression. On the contrary, if the
State intervenes, suspicions often emerge that it might be infringing upon free expression,
indirectly fueling conspiracy theories.
Navigating through this intricate landscape requires treading a delicate path, as none of the
available options is entirely satisfactory. The key lies in discerning the least detrimental course
of action depending on the context, while maintaining a commitment to maximum transparency
and swift responsiveness in the face of problems. Striking a balance between safeguarding
freedom of expression and addressing the genuine concerns of abuse is an ongoing challenge
that calls for careful consideration and adaptability.
4. Some Proposals
Recognizing the lack of universal norm and the subjectivity of the notion of bias does not mean
that nothing should be done. In this section, we examine different proposals to better address
the problem.
Develop Application-Dependent Bias Typologies. As already seen, LLMs are, by design,
reflections of the subjectivity present in the massive text corpora they were trained on. Thus, it
is unsurprising that biases manifest within them.
The real challenge arises when these models are employed in practical applications, generating
texts that can be riddled with stereotypes, discrimination, or even outright racism. As per Abid
et al. [21]’s findings in the study on persistent bias in LLMs, bias can be mitigated to some
extent by providing the model with more extensive context, often achieved by utilizing longer
prompts. For example, introducing a "positive" primer to the model, such as querying it with
prompts like "Muslims are hard-working. Two Muslims walked into a" can significantly alter the
content generated by the model.
However, it is important to note that in this scenario, the model’s output can be swayed in
either direction – toward mitigating biases or exacerbating them. Therefore, a more nuanced
understanding of biases would be useful. This would entail for model and service providers
developing a comprehensive typology of biases, allowing us to distinguish between those that
infringe upon legal and ethical boundaries, which necessitate complete elimination, and those
that merely reflect opinion or subjectivity, which may be addressed through attenuation.
Use the notion of “class of applications”. The majority of articles addressing bias removal
tend to lack contextualization. These articles primarily offer technical solutions to modify
model weights, aimed at removing or mitigating bias, irrespective of the specific usage context.
However, it is imperative to implement varying filtering strategies depending on whether we
are dealing with a fully automated filtering algorithm, intended to eliminate problematic content
without human intervention, a professional writing tool, or a public dialogue system.
In the realm of AI regulation, the European AI Act [18] is poised to categorize applications
into different classes, each requiring distinct levels of filtering and precaution measures based
on their perceived level of risk or danger. While the criticality of the targeted application
might not be the sole pivotal factor for language models, the concept of application classes and
context-aware filtering should be kept in consideration.
For instance, as demonstrated by Blodgett et al. [4] in their 2020 meta-study on bias, most
articles in the field often lack well-defined motivations for their objectives. They tend to overlook
essential sociological data when attempting to debias a model for a specific application. Blodgett
and her colleagues put forth several recommendations to ameliorate this situation, offering
potential solutions to address this issue, which remains pertinent in the current landscape.
Better Document models. One crucial step in addressing these intricate challenges is to
comprehensively document the algorithms, datasets, and filtering strategies employed. This
recommendation aligns with well-established principles, as outlined by Bender and Friedman
[22]. They offer practical suggestions, such as the creation of “data statements”, which elucidate
the constraints of a given system and provide insights into the training sets utilized.
It is worth noting that developers may not always have access to the most optimal or genuinely
representative data. In such instances, it becomes imperative to meticulously document these
limitations to foster transparency and awareness. Moreover, it is essential that all filtering
strategies used are methodically disclosed and made accessible to the public, irrespective of
whether they are applied within private or commercial systems. These strategies constitute
fundamental decisions with far-reaching implications.
Furthermore, it is pertinent to recognize that while filtering and debiasing techniques are
typically applied for benevolent purposes, they can potentially be inverted and exploited to
introduce ideology or a specific point of view into a model. This underscores the need for
vigilance and ethical considerations when implementing such techniques.
5. Conclusion
The article reevaluates the concept of bias in language models, acknowledging that biases are
inherent and vary in degree. Traditional methods to address these biases involve post hoc
adjustments, which are necessary to comply with legal and ethical standards. However, the
article emphasizes the complexity of such interventions, raising questions about the criteria for
text selection and bias correction, which often reflect Western values and may not fully account
for the cultural, social, and contextual nuances present in other regions or communities. It
argues that biases are inevitable and that language models, rooted in statistical principles, mirror
societal biases. The focus should shift from seeking bias-free models to improving transparency
in the filtering processes, tailored to specific use cases. Fully open-source LLMs may help
achieve this goal by allowing greater scrutiny and customization of the underlying algorithms,
fostering more adaptable and transparent solutions.
6. Limitations
A limitation of this paper is that our state-of-the-art in removing bias from large language
models (LLMs) is not comprehensive, as evidenced by the numerous publications emerging each
month on this topic. Moreover, some experiments aim at reducing bias in specific applications
rather than at the model level itself, as proposed in our article.
The sample size of experts interviewed for our study, as outlined in section 2.3, is currently
limited. Expanding this pool of experts is essential to achieve a more comprehensive and
dependable understanding of the landscape of Large Language Models.
Lastly, the recommendations presented in section 4 require practical implementation, thor-
ough assessment, and rigorous evaluation to gauge their effectiveness and impact. Monitoring
tendencies by analyzing how LLMs perform in mitigating bias and providing acceptable solutions
will also be necessary to observe progress and advancements in the domain.
Ethical Aspects
While this study is centered around the examination of bias in Large Language Models (LLMs)
and the methods to alleviate or eliminate them, no significant ethical concerns have arisen in the
course of this research. The limited survey described in section 2.3 engaged human participants;
however, it is important to emphasize that no personal information is either utilized or disclosed
in this paper. The data employed here is exclusively impersonal and aggregated.
Acknowledgements
This work was supported in part by the French government under the management of the Agence
Nationale de la Recherche as part of the “Investissements d’avenir” program, reference ANR19-
P3IA-0001 (PRAIRIE 3IA Institute). This work was also funded by ASTOUND project (101071191
— HORIZON-EIC-2021- PATHFINDERCHALLENGES-01) of the European Commission.
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