Mapping the Ethics of Generative AI: A Comprehensive Scoping Review (2402.08323v1)

Abstract: The advent of generative artificial intelligence and the widespread adoption of it in society engendered intensive debates about its ethical implications and risks. These risks often differ from those associated with traditional discriminative machine learning. To synthesize the recent discourse and map its normative concepts, we conducted a scoping review on the ethics of generative artificial intelligence, including especially LLMs and text-to-image models. Our analysis provides a taxonomy of 378 normative issues in 19 topic areas and ranks them according to their prevalence in the literature. The study offers a comprehensive overview for scholars, practitioners, or policymakers, condensing the ethical debates surrounding fairness, safety, harmful content, hallucinations, privacy, interaction risks, security, alignment, societal impacts, and others. We discuss the results, evaluate imbalances in the literature, and explore unsubstantiated risk scenarios.

Ethical Dimensions of Generative AI: Mapping Complexities and Gaps

The paper "Mapping the Ethics of Generative AI: A Comprehensive Scoping Review" by Thilo Hagendorff offers an extensive scoping review of recent discussions regarding the ethical implications associated with generative AI technologies. These technologies, particularly LLMs and text-to-image models, have brought forth novel ethical considerations distinct from those posed by traditional discriminative machine learning.

Methodological Approach

This review employs a systematic procedure articulated through the PRISMA protocol to map and categorize 378 normative issues into 19 distinct clusters. The authors seek to fill a void in the literature by presenting an organized framework for navigating the ethical landscape of generative AI technologies. A meticulously executed literature search led to the identification of 179 documents, encompassing relevant scholarly works post-2021, following the initial excitement incited by the release of models such as DALL-E and ChatGPT.

Findings and Taxonomy

The paper introduces a taxonomy of ethical concerns, highlighting areas of increased attention and revealing shifts in the ethical discourse due to advancements in generative AI. Notable domains include:

1. Fairness and Bias: The literature consistently underscores the perpetuation of biases within generative models, emphasizing the propagation of societal stereotypes and marginalization of minority groups through biased training data. The centralization of AI development by a limited number of large laboratories and accessibility disparities are also critical points of concern.
2. Safety: Safety considerations dominate discussions, particularly the need for robust measures to mitigate existential risks posed by highly autonomous generative models. Strategies like red teaming, ensuring controllability, and promoting AI safety cultures are actively debated.
3. Harmful Content and Hallucinations: Studies indicate a significant focus on the generation of toxic, false, or misleading content, often discussed in the context of LLMs. The tendency of these models to produce output with confident falsehoods, known as "hallucinations," underlines the need for rigorous validation of AI-generated content.
4. Privacy and Security: Privacy risks, particularly the potential for models to leak sensitive information, are highlighted. The paper emphasizes the critical need for data protection measures and guards against model exploitations, such as jailbreaking and adversarial attacks.
5. Economic and Societal Impacts: The ongoing discourse addresses concerns related to labor displacement, economic inequalities, and challenges in education due to the proliferation of generative AI tools.

Critical Discourse and Imbalances

Hagendorff critiques the ethical discourse for its prominent negativity bias, which prioritizes risks and harms over potential benefits. This focus may skew perceptions and inadvertently contribute to suboptimal decision-making. The review points out that much of the research may exaggerate certain risks due to citation chains and lacks empirical substantiation. For instance, fears of LLMs aiding in the creation of pathogens and the routine citing of privacy violations by LLMs have been critically examined against empirical evidence, suggesting a potential overrepresentation of these threats in the academic discourse.

Implications and Future Directions

The paper underscores the urgency of integrating more empirical data into ethical evaluations of generative AI to ensure a balanced understanding of risks and opportunities. Further examination is required to address overlooked perspectives, including non-human stakeholder impacts and the ethics of multi-modal and agent-based models. Moving forward, the discourse could benefit from a more diverse representation of potential benefits and innovative applications of generative AI that align with ethical considerations.

Conclusion

Hagendorff's work provides a structured pathway for scholars and policymakers to navigate the complex ethical landscape of generative AI. By cataloging the ongoing debates and identifying areas requiring further scrutiny, this review serves as a vital guide for understanding the scope and evolution of ethical considerations in the era of generative AI. The paper calls for a recalibration of the ethical discourse to foster responsible and balanced technological governance.