Interviews with 15 builders map forecasting-bot construction problems
Original title: Programming Prescience: Issues in Human Construction of Forecasting Bots
Successful competition participants struggled to ground bots in facts, keep reasoning coherent, and judge real performance.
A qualitative interview study of 15 builders of forecasting bots, sampled from successful participants in a recent AI forecasting competition, describes the human work of building and evaluating such systems. The authors group difficulties into acquiring factual research, implementing coherent reasoning, and knowing how well a bot performs. Builders often added complexity that did not pay off and tested opportunistically and piecemeal. The paper argues these accounts should guide later tools and human-AI teaming for forecasting.
Unconfirmed: Findings reflect interviews with 15 competition participants, not a representative sample of all forecasting-bot builders.
- Study interviewed 15 builders of forecasting bots.
- Participants were sampled from a recent AI forecasting competition.
- Three problem areas: factual research, coherent reasoning, and evaluation.
- Builders often added complexity that did not improve results.
From the original article
1 min read at arxiv.orgAI models' growing capacity for complex reasoning has made them attractive tools for forecasting, or the systematic prediction of the future based on past and present data. However, should AI forecasting systems be adopted, human builders will shoulder the work of building and evaluating them.
By Priya Dhawka · arxiv.org
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.