Responsibility Gaps, Value Alignment, and Meaningful Human Control over Artificial Intelligence
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This chapter distinguishes among four different kinds of responsibility gaps and briefly compares them to four kinds recently identified by Filippo Santoni de Sio and Giulio Mecacci. A responsibility gap occurs when some event/outcome seems to be such that it would be fitting to hold somebody responsible for it, but where there is no person or persons who could fittingly be held responsible. The four responsibility gaps discussed relate to the ideas of forward-looking and backward-looking responsibility, and positive and negative responsibility. This chapter focuses specifically on so-called forward-looking positive responsibility gaps and relates such gaps to the so-called value alignment problem in artificial intelligence (AI) ethics. This is the problem of making sure that advanced AI aligns with human values, interests, or aims, so that risks related to advanced AI are mitigated. Lastly, this chapter compares two attempts, one by Nyholm and one by Santoni de Sio and Mecacci, to fill responsibility gaps. Both solutions can be viewed as attempts to specify what it is to have meaningful human control over AI. Both attempted solutions, this chapter argues, face important problems. In particular, these theories both work best when we think of them as idealized models for how to fill responsibility gaps. But both theories become more problematic when it comes to the issue of how to actually put them to work in practice in relation to real-world risks (e.g., risks related to advanced AI).
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Lina effectively tackled the all-to-all communication bottleneck by enhancing tensor partitioning, improving all-to-all communication and training step time, and optimizing resource scheduling during inference, leading to a reduction in training step time by up to 1.73 times and lowering the 95th percentile inference time by an average of 1.63 times compared to existing systems.
“The progression in distributed MoE training and inference, notably with innovations like Lina [299], has effectively tackled the all-to-all communication bottleneck by enhancing tensor partitioning, which not only improves all-to-all communication and training step time, but also optimizes resource scheduling during inference, leading to a substantial reduction in training step time by up to 1.73”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [299]
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McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works
- Authors
- Nyholm, S.
- Corresponding author
- Nyholm, Sven
- ORCID
- Nyholm, Sven
- Venue
- Risk and Responsibility in Context
- Publisher
- Routledge
- Published
- 2023-08-22
- Pages
- 191-213
- OpenAlex
- W4386067679
- Cited by
- 30
- References
- 24
- Citations by year
- 2026: 8 · 2025: 13 · 2024: 8 · 2023: 1
- FWCI
- 37.19
- Type
- book chapter
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Routledge eBooks (publishedVersion, cc-by-nc)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (024.004.031, NWO grant number 024.004.031)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Arts and Humanities › Philosophy › Philosophical Ethics and Theory
- Social Sciences › Political Science and International Relations › Literary and Philosophical Studies
- Keywords
- responsibility gap, value alignment, meaningful human control, AI ethics, AI risks
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