Many machine learning systems today make decisions on behalf of users—from what videos they see to how their resumes are screened—yet offer little visibility or control over how these predictions are generated. This creates frustration when recommendations feel off or automated decisions seem unfair. One way to address this could be to create a tool that lets users directly influence the ML systems that affect them.

Putting Control in Users' Hands

The idea centers on building an intermediary layer between users and opaque ML systems, translating user inputs into model adjustments. For instance:

• Weight sliders could let tweak how much a news recommender prioritizes recency versus popularity
• A "never recommend this" rule could block unwanted content sources
• Real-time feedback loops might let users correct biased predictions, like a hiring tool that unfairly ranks certain resumes

Unlike existing limited feedback buttons (e.g., YouTube's "Not Interested"), this approach would allow granular control over the underlying logic, not just passive reactions.

Balancing Stakeholder Needs

While users gain transparency and businesses could see improved engagement, some tensions exist. Companies might resist ceding algorithm control, and ML engineers may worry about stability. An initial browser extension targeting open APIs could sidestep platform resistance, while safeguards like bounded adjustments would prevent erratic model behavior. For non-technical users, simplified presets ("Show more variety") could make the tool accessible.

Path to Implementation

Starting with recommendation systems (e.g., video or music platforms) could provide a practical testbed. A basic MVP might offer sliders for key parameters like diversity or freshness, while later versions could integrate with enterprise systems like hiring tools. Monetization could range from freemium models for consumers to B2B licensing for companies seeking to reduce user churn.

By focusing first on domains where user frustration is high but technical barriers are low—like entertainment recommendations—this approach could demonstrate value before tackling more complex applications.