3 Types of GenAI Risks and Harms in Foundational LLM Models

The rapid emergence of generative AI foundational large language models (LLMs) has ushered in a range of complex ethical, social, and environmental challenges along with significant advancements. As these technologies evolve, they pose unique risks that merit serious consideration. A recent paper titled “Mapping the Individual, Social, and Biospheric Impacts of Foundation Models” emphasizes the urgent need to address these multifaceted challenges and outlines a framework for understanding the associated risks.

Since the debut of ChatGPT in late 2022, discussions around foundational LLM models have exploded across policy, academic, and public domains. These models are defined by their enormous scale and pervasive nature, consisting of hundreds of billions of parameters and trained on vast datasets, leading to extensive resource consumption. This scale magnifies both the capabilities and the risks associated with these technologies, transcending national and political boundaries and necessitating a concerted, transnational response.

The embeddedness of foundation models—designed as foundational platforms for a myriad of downstream applications—makes them both powerful and elusive. While they enable innovative applications, their negative impacts can be obscured, complicating the challenges they present. Thus, it is crucial to address the potential risks and harms posed by these models proactively.

### 3 Types of GenAI Model Risks and Harms

The paper categorizes risks associated with generative AI foundational LLM models into three main types: individual, social, and biospheric. Understanding these risks is essential for guiding the responsible development and deployment of AI technologies.

#### 1. Individual Risks and Harms

One of the primary concerns surrounding foundation models is their tendency to perpetuate and amplify biases and harmful stereotypes. Notably, around 40% of the reviewed literature points out that these models can reinforce hegemonic views and societal biases on an unprecedented scale. Such biases can adversely affect individual safety, health, and well-being, undermining fundamental rights and liberties.

Moreover, the reliability of these models often varies, resulting in inconsistent outcomes. This inconsistency is particularly alarming in sensitive areas like healthcare, legal systems, and education, where the stakes are significantly high. The potential harm that may arise from flawed or biased outputs calls for a cautious approach to the deployment of these technologies.

#### 2. Social Risks and Harms

On a social level, foundation models can exacerbate the spread of misinformation and disinformation. Approximately 20% of the literature reviewed identifies this issue as a significant concern, indicating that these models can destabilize societal trust and undermine democratic processes. The potential for these technologies to facilitate cybersecurity threats and fraudulent activities further complicates the issue.

The socio-economic implications are profound as well. The reliance on proprietary software and the lack of transparency in AI development can lead to market monopolization and perpetuate existing inequalities. A limited number of organizations with the resources to develop and maintain such technologies could entrench power dynamics, marginalizing less-resourced communities.

#### 3. Biospheric Risks and Harms

The environmental impact of foundational models represents another critical area of concern. The training of these large models consumes vast amounts of computational power, resulting in significant carbon emissions. For instance, the carbon footprint of training Google’s BERT model is comparable to that of a transatlantic flight, underscoring the environmental implications of these technologies.

Additionally, the extraction processes for the rare earth elements used in AI development not only degrade the environment but also disrupt local communities, particularly in the Global South. This “slow violence” disproportionately affects marginalized groups, highlighting patterns of environmental injustice and the need for ethical considerations in technology development.

### Need for Holistic GenAI Governance

Given the extensive and interconnected risks associated with generative AI foundational LLM models, the paper calls for a comprehensive and holistic approach to AI governance. Current frameworks, particularly in regions like Europe and the United States, often emphasize technical safety and catastrophic risks while neglecting broader social and ethical implications.

The authors advocate for an integrative perspective that considers socio-technical interdependencies inherent in foundation models. This approach must address not only the immediate effects on individuals but also the cascading impacts on social structures and environmental health.

### Implications for Humanitarian Organizations

For humanitarian organizations working with generative AI systems, the findings of this research hold critical importance. The risks associated with foundation models emphasize the necessity of developing AI technologies that are not only technically effective but also ethically sound and socially responsible. Prioritizing transparency, equity, and sustainability in AI initiatives will be essential.

Humanitarian efforts often confront the very issues exacerbated by foundation models—like inequality, misinformation, and environmental degradation. Organizations in this sector can play a pivotal role in advocating for and implementing responsible AI practices. This involves pushing for transparency in AI development, promoting policies that address environmental impacts, and ensuring that AI applications do not perpetuate harmful biases or deepen existing inequalities.

In conclusion, as generative AI foundational LLM models continue to impact our lives, the need for informed and responsible governance has never been more apparent. Acknowledging and addressing the risks and harms associated with these technologies can pave the way for more equitable, effective, and sustainable AI applications that genuinely benefit society as a whole.

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