Machines That Let You Leave

I study and build personal AI systems whose goal is not to maximize engagement, intimacy, or dependence, but to increase the user’s capacity to leave the system and act in the world.

My past work studied how users sought autonomy from AI platforms through building open alternatives, and how those open alternatives still risk capture from within through paid open source labor from incumbents. These two projects focused on the politics of platform openness, highlighting how users refashion various levels of openness afforded to them by firms, and use such affordances to their advantage in achieving goals related to privacy, autonomy, and creativity.

This research agenda seeks to enable a mechanism of platform exit, made affordable to all. By a “mechanism of platform exit,” I mean algorithms which optimize for the user’s capacity to leave the system. By “made affordable to all,” I mean a scaffolding of dependence decay, through slowly reducing engagement, personalization, automation, companionship, and platform value.

I assume that the status quo is addiction to such platforms. By addiction, I mean uncontrolled dependence that is to the detriment to the user. By using addiction as the default state of being, I ignore the possibility that healthier forms of dependence can be achievable. This ignorance is not out of denial, but out of the multitudes of other research and corporate agendas aiming to achieve this goal. I instead operate in the design space of exit-oriented machines.

I rest this research agenda upon the shoulders of the following related works:

• Theoretical lenses
  • Non-use
    • Beyond the user: use and non-use in HCI
    • Making Un-Use: When Humans Disengage with Technology
  • Adversarial design
    • Adversarial Design
  • Seamful design
    • Seamful and Seamless Design in Ubiquitous Computing
    • Seamful XAI: Operationalizing Seamful Design in Explainable AI
  • Exit, Voice, and Loyalty, and its “upgrades”
    • Exit, Voice, and Loyalty: Responses to Decline in Firms, Organizations, and States
  • Beyond helpful, honest, and harmless
    • Antagonistic AI
    • Beneficent Intelligence: A Capability Approach to Modeling Benefit, Assistance, and Associated Moral Failures through AI Systems
  • Gap: These lenses legitimize non-use, contestation, seams, exit, antagonism, and capability-support, but mostly stop at vocabulary and norms; missing is a design program for AI systems that treat leaving as a positive objective and operationalize dependence decay over time.
• Empirical evidence of overreliance harms in LLMs and other algorithmic systems
  • Sycophancy/Anthropomorphism in Large Language Models
    • Towards Understanding Sycophancy in Language Models
    • Sycophantic AI decreases prosocial intentions and promotes dependence
  • Harms and overreliance in Large Language Models
    • Fatal deception: how generative AI fosters therapeutic misconception in vulnerable users
    • When Human-AI Interactions Become Parasocial: Agency and Anthropomorphism in Affective Design
    • To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making
    • Understanding Teen Overreliance on AI Companion Chatbots Through Self-Reported Reddit Narratives
  • Gap: These works show how sycophancy, anthropomorphism, therapeutic misconception, parasocial attachment, and overreliance can produce dependence, but they mostly diagnose harm after reliance forms; missing is empirical work on early, situated transitions from reliance to exit and on what users need to rebuild offline agency.
• Attempts at and evaluations of solutions against overreliance
  • Self-control tools
    • Self-Control in Cyberspace: Applying Dual Systems Theory to a Review of Digital Self-Control Tools
    • Directing smartphone use through the self-nudge app one sec
  • Benchmarking human agency support
    • HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistants
  • Kinder recommender systems
    • Building Human Values into Recommender Systems: An Interdisciplinary Synthesis
  • Gap: These solutions reduce specific failures through self-control tools, nudges, cognitive forcing, agency benchmarks, and value-aware recommendation, but remain oriented toward better use within systems; missing are interventions and metrics for longitudinal disengagement: making the system progressively less necessary, less rewarding, and easier to leave.