Transparency in artificial intelligence
By Stefan Larsson, Fredrik Heintz. Internet Policy Review, 2020
296 citations recorded by the source corpus.
This conceptual paper addresses the issues of transparency as linked to artificial intelligence (AI) from socio-legal and computer scientific perspectives. Firstly, we discuss the conceptual distinction between transparency in AI and algorithmic transparency, and argue for the wider concept ‘in AI’, as a partly contested albeit useful notion in relation to transparency. Secondly, we show that transparency as a general concept is multifaceted, and of widespread theoretical use in multiple disciplines over time, particularly since the 1990s. Still, it has had a resurgence in contemporary notions of AI governance, such as in the multitude of recently published ethics guidelines on AI. Thirdly, we discuss and show the relevance of the fact that transparency expresses a conceptual metaphor of more general significance, linked to knowing, bringing positive connotations that may have normative effects to regulatory debates. Finally, we draw a possible categorisation of aspects related to transparency in AI, or what we interchangeably call AI transparency, and argue for the need of developing a multidisciplinary understanding, in order to contribute to the governance of AI as applied on markets and in society. (Less)
Subfields: Safety Research
View Paper: https://www.semanticscholar.org/paper/73395c0a04a86f2c504fc9d02076266620a54e7a
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Citations in the corpus
- The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review (2024, background): “There is also a comprehensive review of 84 global ethical AI guidelines reveals that transparency (Larsson & Heintz, 2020), along with related concepts like explainability, interpretability, and disclosure, emerges as a predominant ethical principle, cited in 73 of these documents (Larsson, 2020).”
- Ethical principles for artificial intelligence in education (2022, background): “Data ethics emphasized the need for transparency in data usage in AIED (Larsson & Heintz, 2020 ).”
- Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback (2023, background): “For all of these reasons, working to incorporate transparency standards into an AI governance framework will be important [Larsson and Heintz, 2020, Anderljung et al., 2023].”
- Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models (2023, background): “When users and regulators can see that developers are taking steps to ensure the fairness and equity of their models, they may be more likely to trust and adopt these technologies [92, 93].”