AI Attribution in Art offers perspectives for tech workers pondering #GenAI disclosure: from subtle references to detailed disclosures, artists and curators are using various approaches to acknowledging AI contributions in their work.
My article on derived Perspectives for Software Engineers examines:
- The “AI Assisted” label used by curator Janet Bellotto
- Segregated disclosure at the Singapore Art Museum
- Integrated disclosure by artist Daito Manabe
- Comprehensive disclosure practices by:
- 🌠 the Italian art studio fuse* and
- 👨🎨 artist and writer Prof. Lev Manovich
As AI becomes increasingly interwoven with our development lifecycle, tech stacks and products, the art community’s thoughtful exploration of authorship, originality, and ethical AI use may offer valuable insights for our own practices. While it’s unlikely that there’s a one-size-fits-all solution, these examples provide food-for-thought as we consider the nuances of transparent and responsible AI attribution in our own fast-paced industry.
Whether you’re a software engineer or in any other role shaping tech solutions, I invite you to explore these perspectives and consider how they might inform our work.
[Update]: Related: this article from Stanford Institute for Human-Centered Artificial Intelligence (HAI) on AI adoption in Healthcare: https://lnkd.in/gyuF65Qq.
This article raises considerations that parallel those discussed in my article:
❗ “Doctors and patients may have very different perceptions about what level of disclosure is appropriate.”
❗ the counterintuitive consideration that “opaque poorly-performing models are less likely to generate a lawsuit than easy-to-understand better performing models” and
❗ hospitals thus “need to be fastidious about documenting precise details of the tool they’ve deployed, like which model version it is and which software package it’s using”.
These parallels underscore the importance of nuanced, context-sensitive approaches to AI attribution and the value of cross-disciplinary dialog.