With Lisa at the helm, My Boss Is A Robot is going to take a slightly different path toward our goal of doing crowdsourced journalism. Partly that’s because our first attempt didn’t exactly produce stellar results. But it’s also because Lisa thinks about the problem in a different way. Here’s what she says about her plans going forward:
The initial attempts to create news articles hit two obstacles: low speed and low quality. Unlike simple tasks such as labeling images, fewer workers are willing to write news articles, which require more cognitive effort. Since workers are less willing, it takes longer to assemble a crowd and complete the task. Moreover, either for lack of ability, or because workers are not putting sufficient effort into the task, the quality of the crowd’s work is low.
In order to find talented and motivated workers, we need to build a stable workforce. Our approach is called invitation-based participation. Instead of posting tasks to the public crowdsourcing market, we will invite workers to join a writing team. Workers who accept enjoy special benefits. We’ll also focus on building a team with a shared goal, establishing a bond between the requestors and workers, as well as between workers themselves. The team environment also allows for feedback and integrated learning.
The idea of building a stable workforce in the crowdsourcing market was inspired by theories from several disciplines. Ronald Coase, an economist, explained the emergence of the firms in terms of transaction costs. There are many costs that occur in the processing of market-based transactions: for example, search and information costs and bargaining costs. Firms with long-term employees are able to save such transaction costs. Posting the writing tasks to the crowd in the crowdsourcing market leads to very high transaction costs: long waiting times, lower quality from the unfitted workers, and higher payments. By building a long-term relationship with the right workers, we can reduce such costs.
We have conducted some studies and the preliminary results show that invited-participation leads to better performance. Now we’re gong to experiment with various ways of building up a stable and high-quality workforce. We’re also trying to combine machine learning techniques with the crowd. We will first shorten the source articles using text summarization techniques, then let the crowd work on the shortened articles. This machine-assisted summarization process is expected to be an integrated part of crowdsourced journalism.
Are we stepping back from our original goal? In some ways, yes. We’d hoped to be able to create articles using any workers from Mechanical Turk. We’re admitting that, at least at this stage, we need more control over our workforce. Having said that, our workforce will still consist of untrained journalists. If the system works, we’ll still be creating articles rapidly and at low cost. And if it fails? Maybe we’ll have shown that journalism isn’t something that can easily be done by an algorithm and the crowd.
We’ll be back here with more results as soon as Lisa has them.