Line of inquiry
Inquiring lines›How do we keep AI systems safe and…›How does AI reshape human understa…›this line of inquiry
How should human-AI contributions be measured, disclosed, and verified?
A broader line of inquiry — a family of 36 specific questions the research asks around this. Follow one into its inquiring-line page, or move sideways to a related line below.
Questions in this line of inquiry 36
Specific inquiring lines the field asks around this — ordered from the most general framing down to the most specific angle.
- How do collaborators react when they see detailed AI tool usage logs?
- Can users tell the difference between their own thinking and AI contribution?
- What counts as human versus AI contribution in research disclosure?
- How does disclosure of AI use differ from proof of who did the work?
- How should code authorship be measured in human-AI collaborative development?
- Does process data reliably distinguish delegation from genuine collaboration?
- How do managers and individual contributors differ in their exposure to low-quality AI work?
- How should systems design transparency to make human-machine contribution boundaries visible?
- What gap exists between how creators think they made work versus how audiences perceive it?
- Why do collaborative writers want visibility of AI use while public posters avoid it?
- How do organizational policies on GenAI affect whether workers hide or reveal their use?
- Can users accurately recall their role versus the system's role in production?
- How do process signatures distinguish delegation from ordinary collaboration?
- Does AI shift knowledge work away from communication toward solo documentation tasks?
- How does lower marginal effort in AI production change creator behavior?
- What makes colleagues willing to share how they actually use GenAI at work?
- How does ownership over final products change reliance on AI suggestions?
- What workplace cultures make professionals more willing to disclose AI use openly?
- How often does hidden AI use actually get discovered in practice?
- How do students behave differently when collaborating with AI versus human teammates?
- Does the shift from expert creation to AI curation happen consciously or invisibly?
- Should AI training data sharing be opt-in by default?
- What role does evaluation play in human-AI creative collaboration?
- Does knowing an AI peer's identity change how much its behavior influences you?
- Can colleagues detect when a coworker stops sounding like themselves in AI-mediated messages?
- Do reputational penalties from workslop persist after senders change their practice?
- Who is most affected by the transparency penalty when AI is disclosed?
- What data do developers expose by sharing session logs publicly?
- What happens to collaborative trust when effort becomes invisible in finished work?
- How should AI be integrated into creative workflows to protect collective diversity?
- Can open collaboration survive if sharing work-in-progress becomes competitively dangerous?
- What does 'liveness' mean in human-AI collaboration systems?
- What directions does AI-generated workslop flow within organizations most often?
- What distinguishes ghost work from traditional academic or paid research collaboration?
- How much does social context matter for algorithmic transparency?
- What do AI researchers actually mean when they use the term AGI?