Not My Voice! A Taxonomy of Ethical and Safety Harms of Speech
  Generators
Wiebke Hutiri Oresiti Papakyriakopoulos Alice Xiang
The rapid and wide-scale adoption of AI to generate human speech poses a
range of significant ethical and safety risks to society that need to be
addressed. For example, a growing number of speech generation incidents are
associated with swatting attacks in the United States, where anonymous
perpetrators create synthetic voices that call police officers to close down
schools and hospitals, or to violently gain access to innocent citizens' homes.
Incidents like this demonstrate that multimodal generative AI risks and harms
do not exist in isolation, but arise from the interactions of multiple
stakeholders and technical AI systems. In this paper we analyse speech
generation incidents to study how patterns of specific harms arise. We find
that specific harms can be categorised according to the exposure of affected
individuals, that is to say whether they are a subject of, interact with,
suffer due to, or are excluded from speech generation systems. Similarly,
specific harms are also a consequence of the motives of the creators and
deployers of the systems. Based on these insights we propose a conceptual
framework for modelling pathways to ethical and safety harms of AI, which we
use to develop a taxonomy of harms of speech generators. Our relational
approach captures the complexity of risks and harms in sociotechnical AI
systems, and yields an extensible taxonomy that can support appropriate policy
interventions and decision making for responsible multimodal model development
and release of speech generators.