Taxonomy of Risks posed by Language Models
Abstrak
Responsible innovation on large-scale Language Models (LMs) requires foresight into and in-depth understanding of the risks these models may pose. This paper develops a comprehensive taxonomy of ethical and social risks associated with LMs. We identify twenty-one risks, drawing on expertise and literature from computer science, linguistics, and the social sciences. We situate these risks in our taxonomy of six risk areas: I. Discrimination, Hate speech and Exclusion, II. Information Hazards, III. Misinformation Harms, IV. Malicious Uses, V. Human-Computer Interaction Harms, and VI. Environmental and Socioeconomic harms. For risks that have already been observed in LMs, the causal mechanism leading to harm, evidence of the risk, and approaches to risk mitigation are discussed. We further describe and analyse risks that have not yet been observed but are anticipated based on assessments of other language technologies, and situate these in the same taxonomy. We underscore that it is the responsibility of organizations to engage with the mitigations we discuss throughout the paper. We close by highlighting challenges and directions for further research on risk evaluation and mitigation with the goal of ensuring that language models are developed responsibly.
Topik & Kata Kunci
Penulis (23)
Laura Weidinger
Jonathan Uesato
Maribeth Rauh
Conor Griffin
Po-Sen Huang
John F. J. Mellor
Amelia Glaese
Myra Cheng
Borja Balle
Atoosa Kasirzadeh
Courtney Biles
S. Brown
Zachary Kenton
W. Hawkins
T. Stepleton
Abeba Birhane
Lisa Anne Hendricks
Laura Rimell
William S. Isaac
Julia Haas
Sean Legassick
G. Irving
Iason Gabriel
Akses Cepat
- Tahun Terbit
- 2022
- Bahasa
- en
- Total Sitasi
- 884×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1145/3531146.3533088
- Akses
- Open Access ✓