The first Lab seminar of the spring semester convened on February 6, 2013 and was led by Edmond J. Safra Network Fellow, Daniel Newman. Daniel Newman is President and Co-Founder of MapLight, a nonpartisan nonprofit that reveals money's influence in politics. Newman began his presentation on MapLight's effort to illuminate the influence of money in politics by sharing an example of how the food-processing industry in California, by making $2.3 million in campaign contributions, influenced California State Senators to amend an $18 million state program intended to put fresh fruit in school breakfasts. The language in the proposal, which originally called for "fresh" fruit to be included in student's lunches, was changed from "fresh" to "nutritious" on the last day of the legislative session. With this subtle change, canned fruit in sugar syrup soon found its way into California's school cafeterias. Thus, a program intended to serve the health interests of children was ultimately manipulated by the outside influence of the food-processing industry. Newman went on to frame this example using Lawrence Lessig's definition of institutional corruption as a consequence of an influence within an economy of influence that illegitimately weakens the effectiveness of an institution especially by weakening the public trust of the institution. Through this lens of institutional corruption, Newman began to discuss MapLight's mission to highlight the problem of money-in-politics corruption and to increase support for reform.
Continuing with his presentation, Newman outlined the main purposes of Maplight, describing how the website actually works and highlighting some of its success stories. Specifically, by increasing the salience of the money-in-politics problem and connecting money to political issues, Maplight increases support for reform and makes accountability actors more effective. In doing this, it empowers activist groups, provides journalists and nonprofit groups with easily accessible and accurate information, and effects citizens' voting decisions in elections. Newman went on to compare Maplight to an MRI machine, in that it offers the public a nascent imaging technology that sheds light on correlations between special interest contributions and votes, exposing corruption in politics. As an example, he cited Maplight's findings on the Payday Lending legislation in California designed to raise the borrowing cap for payday lenders from $300 to $500. Assembly member Charles Calderon, who introduced the legislation, received more in campaign contributions from the payday loan industry ($31,450) than any other member of the Assembly. In total, the payday loan industry contributed $1.1 million since 2004 to California legislators. The story was widely reported on California where it generated 40 news stories and 15 editorials citing Maplight's data. As a result, the bill was defeated. At this point in the seminar, Newman concluded his presentation and opened the table to discussion.
Several members of the seminar expressed their concern regarding false assumptions and causation. In particular, one participant questioned if there was a way to determine what special interests a candidate had aligned with early on in his or her political career. He reasoned that doing so would prevent any post hoc assumptions from being drawn from a particular campaign contribution or vote. In general, participants of the Lab were concerned with differentiating established positions from positions that have changed because of outside influence. At this point in the seminar, one participant suggested that comparing the underlying demographics of the people in a certain district to the voting record of a representative in question would be the most effective way to reveal inconsistencies in policy. Finally, another Lab participant suggested that Newman might add a "Top 10 list" detailing the most egregious cases of money-in-politics to MapLight's homepage. Newman agreed that it would be an effective way of broadcasting Maplight's work and added that he would be open to working with a developer to create an algorithm to discern such cases.