Dashboards for Discourse
The automation-fueled drop in the cost of machine learning enables new, and possibly more socially beneficial forms of civic engagement.
In August, U.S. viewers witnessed the dashboard for Hurricane Dorian being displayed and discussed in every major news outlet. That dashboard is the well-known image of a clear hurricane track from the time the storm formed leading to a cone of uncertainty that extends into the future. It is visually compelling, future-oriented, and it encompasses the idea that there are multiple models of how the future will unfold.
The ubiquity of the standardized dashboard for hurricanes may indicate that the use of a dashboard in general has become essential for maintaining viewers’ attention. When a severe storm threatens the U.S., viewership and coverage rises, and the rise in coverage appears to be proportional to storm severity. The intense coverage of hurricanes by the Weather Channel has demonstrably helped them capture viewer interest, and their intensity of coverage appears to have spread to other cable news outlets. As unseemly as it is to discuss, the ratings for televised hurricane coverage are good.
Just as market forces helped refine and propagate rational dashboards for hurricanes, they can also refine and propagate rational dashboards for political issues.
Guidance from AI and the Free Market
Like the hurricane dashboard, a dashboard for political discourse can demonstrate the competing views of where we have been and where we should be going through engaging visual displays that compare and contrast the different vantage points. Automated machine learning can now make engaging predictive displays possible and profitable for their sponsoring media companies, as the availability of AI increases and the cost of predictions decreases.
DataRobot helps media companies apply models to increase their volume of and profits from subscription and advertising services. Media companies also use DataRobot to manage their customer relationships — prospecting for new customers, preventing customer churn, identifying the best offer for each customer, understanding marketing channels, and improving customer satisfaction.
For example, consider a hypothetical dashboard for the current conversation around health care in the United States. Presidential candidates, and others, have plans for how to reform the current system. A dashboard for these plans would show information about the existing system, such as total average cost per household, percentage of population covered, and so on. The display of the existing system would lead to multiple projections into the future of the same quantities under the various competing plans for reform. This hypothetical dashboard should prove informative and engaging for the audience, and ultimately profitable for a media company that uses it to discuss the issue.
Dashboards for Discourse
A dashboard for healthcare plans would be an improvement on the status quo and also a small step from it. At present, Presidential campaigns must score their own plans, which means they model the costs and benefits of their proposed legislation. The candidates then verbally describe the outcomes of their models (such as total average cost per household and percentage of population covered) in response to questions about their plans.
In contrast, with a dashboard, the candidates’ existing models would be aggregated into a single forum. The models would be transparent to the public and readily compared without the public getting lost in confusing verbal descriptions of the math. Again, the media companies that do this should outcompete the others simply on the strength of their more compelling coverage of the issue.
Above and beyond the benefits of more engaging and informative coverage of an issue, a dashboard will also benefit the public (and the media company) by serving as a focus for online public discourse. A smaller but passionate slice of the audience will be interested in adjusting the published models and putting their own models into the competition. It appears this currently happens with hurricane dashboards, and presumably there would be a larger audience interested in discussing the details of healthcare and other major issues.
We Can Help
Our Data Engineers and AI Success Managers will work with your reporters and technical staff to identify good data sources and forecast variables for the issue. This list of possibilities will go to your creative department who should mock up many creative visualizations. These visualizations do not have to be focused on the same forecasts. For example, one visualization for a dashboard on tax issues might look at the ratio of tax rate to GDP; another might look at taxes on capital gains versus taxes on goods and services. We recommend a path to broadcast publication that includes rapid, inexpensive experimentation, because automation reduces costs by orders of magnitude. The results of these experiments can also be modeled to identify the dashboard features your audience finds compelling.
How can any democracy commit to an enormous goal, formulate plans of action around it, and track its progress over the decades or centuries that it would take to be completed? This is possible by aggregating the competing viewpoints on issues into common visual frameworks called dashboards. Those dashboards can then profitably support improved public discourse by organizing the details of the competing viewpoints and making them accessible.
As we enter the 2020 election season, DataRobot is ready and able to help media companies explore new, more engaging, and more profitable debate formats and news displays, including formats that revolve around dashboards of political issues and current events.
DataRobot is engaged in world affairs, and we believe that AI must serve the public interest. This includes tools and approaches that enable Americans to trust AI solutions; that protect our civil liberties; and that help our country grow stronger. As a world leader in automated machine learning, DataRobot is uniquely positioned to help news agencies, non-profit organizations, federal agencies, and other public entities make this dream a reality. Visit us here to learn more about our work in the public sector.
About the Author:
Eric Loeb works as a Customer-Facing Data Scientist at DataRobot. Previously, Eric wrote the first Congressional website (by hand, in raw HTML!) and contributed to first websites at all levels of government. He was the chief software engineer for a Presidential campaign in 2000, chief internet architect for a major political party, targeting and modeling lead for a winning Presidential campaign, and then a political appointee in the Department of Defense. Eric has a Ph.D. from MIT in cognitive neuroscience, an MS from UC Berkeley in adaptive signal processing, and a BS in mathematics from the University of Illinois.
Eric wrote the first Congressional website (by hand, in raw HTML!) and contributed to first websites at all levels of government. He was the chief software engineer for Gore 2000, chief internet architect for the Democratic National Committee, targeting and modeling lead for Obama ‘08, and a political appointee in the Department of Defense. Eric has a Ph.D. from MIT in cognitive neuroscience, an MS from UC Berkeley in adaptive signal processing, and a BS in mathematics from the University of Illinois.
We will contact you shortly
We’re almost there! These are the next steps:
- Look out for an email from DataRobot with a subject line: Your Subscription Confirmation.
- Click the confirmation link to approve your consent.
- Done! You have now opted to receive communications about DataRobot’s products and services.
Didn’t receive the email? Please make sure to check your spam or junk folders.
Belong @ DataRobot: AAPI Heritage Month with the ACTnow! CommunityMay 25, 2023· 3 min read
Deep Learning for Decision-Making Under UncertaintyMay 18, 2023· 5 min read
Getting Value Out of Generative AIMay 10, 2023· 3 min read
Many companies are experiencing mounting pressure to have a generative AI strategy, but most are not equipped to meaningfully put generative AI to work. For AI leaders, there are deeper questions you need to ask as you consider your path with generative AI.
Discover the challenges and benefits of big data in AI, downsampling, and smart sampling techniques to reduce data size without losing accuracy.
DataRobot 9.0 helps organizations scale the use of AI to create value enterprise-wide. Discover how it simplifies ML production, automates deployment, and manages model drift to maintain business value.