Mainstream media, through their decisions on what to cover and how to frame the stories they cover, can mislead readers without using outright falsehoods.Therefore, it is crucial to have tools that expose these editorial choices underlying media bias.In this paper, we introduce the Media Bias Detector, a tool for researchers, journalists, and news consumers.By integrating large language models, we provide near real-time granular insights into the topics, tone, political lean, and facts of news articles aggregated to the publisher level.We assessed the tool's impact by interviewing 13 experts from journalism, communications, and political science, revealing key insights into usability and functionality, practical applications, and AI's role in powering media bias tools.We explored this in more depth with a follow-up survey of 150 news consumers.This work highlights opportunities for AI-driven tools that empower users to critically engage with media content, particularly in politically charged environments.

Financial options are contracts that specify the right to buy or sell an underlying asset at a strike price by an expiration date. Standard exchanges offer options of predetermined strike values and trade options of different strikes independently, even for those written on the same underlying asset. Such independent market design can introduce arbitrage opportunities and lead to the thin market problem. The paper first proposes a mechanism that consolidates and matches orders on standard options related to the same underlying asset, while providing agents the flexibility to specify any custom strike value. The mechanism generalizes the classic double auction, runs in time polynomial to the number of orders, and poses no risk to the exchange, regardless of the value of the underlying asset at expiration. Empirical analysis on real-market options data shows that the mechanism can find new matches for options of different strike prices and reduce bid-ask spreads. Extending standard options written on a single asset, we propose and define a new derivative instrument ---combinatorial financial options that offer contract holders the right to buy or sell any linear combination of multiple underlying assets. We generalize our single-asset mechanism to match options written on different combinations of assets, and prove that optimal clearing of combinatorial financial options is coNP-hard. To facilitate market operations, we propose an algorithm that finds the exact optimal match through iterative constraint generation, and evaluate its performance on synthetically generated combinatorial options markets of different scales. As option prices reveal the market's collective belief of an underlying asset's future value, a combinatorial options market enables the expression of aggregate belief about future correlations among assets.

We report on a large-scale case study of a combinatorial prediction market. We implemented a back-end pricing engine based on Dudik et al.'s (2012) combinatorial market maker, together with a wizard-like front end to guide users to constructing any of millions of predictions about the presidential, senatorial, and gubernatorial elections in the United States in 2012. Users could create complex combinations of predictions and, as a result, we obtained detailed information about the joint distribution and conditional estimates of election results. We describe our market, how users behaved, and how well our predictions compared with benchmark forecasts. We conduct a series of counterfactual simulations to investigate how our market might be improved in the future.