Transparency in the Digital Age: The Status of U.S. Open Government

1. Transparency in the Digital Age: The Status of U.S. Open Government By J.H. Snider, Ph.D. President iSolon.org Email: contact@isolon.org Presented at “2010 FOI Summit:Protecting the Public’s Right to Oversee its Government”May 8, 2010 Hyatt Arlington Hotel, Arlington, VA

2. Overview: The Status of U.S. Open Government • Introduction • The Federal Government • The Open Government Community • A Vision for the Future (involving some blood, sweat, and tears)

3. Introduction • The merits of a glass half-full vs. half-empty critique • On the relationship between open government and technology

4. The Federal Government • Making what’s already public more accessible • Example: The Open Government Directive • The weaknesses of a “high-value dataset” benchmark • The advantages of a “principal-agent” benchmark • Changing what’s public (garbage in, garbage out) • Information generated with the rulemaking process • Information generated with legislative procedure

5. The Open Government Community • The glass half-full • The glass half-empty (four provocations) • Inside vs. outside lobbying strategy • Advocacy vs. academic contributions • Short vs. long term thinking • Low vs. high industry cartelization

6. A Vision for the Future • A different approach built on standards setting. • An approach built on semantic web technologies • Private good case study: Google’s product reviews • Public good case study #1: XBRL (automating financial reporting) • Public Good Case Study #2:BiasML (automating conflict-of-interest reporting)

7. Human Vs. Machine-Readable Web Metadata The Tree, The House,. The Door, Shirt, Pants, The Cat, The Dog Ontology The Door is a part of The House

8. Human Vs. Machine-Readable Web MetadataFTC (Organization)600 Penn. Ave. (Street)Washington (City)DC (State)20580 (Zip Code) Address Ontology OrganizationStreetCityStateZip Code Structured Text

9. The Hierarchy

10. Ontologies Turn the Web into a Giant Database

11. The Giant Database in the Sky Source: Leigh Dodds, presentation at the International Semantic Web Conference, October 25-9, 2009.

12. Private Good Case Study: Google’s Product Reviews New Snippet Format Old Snippet Format

13. Product Reviews with Map Functionality

14. Product Review Ontology (it’s simple)

15. The Code (again, it’s simple)

16. Financial Ontology (XBRL)

17. XBRL: From Idea to Implementation • 1998: W3C publishes guidelines for XML, which makes it possible to attach “tags” to each piece of information in a document; Charles Hoffman comes up with the idea for XBRL. • 1999: American Institute of Certified Public Accountants (AICPA) endorses developing XBRL. • 2000: AICPA releases first draft of XBRL. • 2002: FDIC endorses XBRL. • 2004: Chinese stock exchanges adopt XBRL. • 2005: FDIC adopts XBRL; SEC endorses XBRL. • 2008: U.S. GAAP published in XBRL. • 2009: SEC adopts XBRL for largest companies, beginning four year rollout. Source: Karen Kernan, “The Story of Our New Language,” AICPA, 2009.

18. Conflict-of-Interest (or Bias) Ontology

19. Why a Conflict-of-Interest Ontology is Important • Progress progress depends on the division of laborand increasingly efficient trust mechanisms • Conflict-of-interest disclosure has become increasingly important in economics and politics • Approximately 1/3rd of Americans work in a licensed or certified occupation, with many having fiduciary obligations • Politicians and public officials all have fiduciary obligations.

20. Structure of a Bias Ontology (it’s elegant!) Four Key Elements of a Principal-Agent Claim • Principal • Agent • Agent’s Covered Interests • Agent’s Covered Actions

21. Simple Example: Earmarks For U.S. Senator Richard Shelby, the largest earmark recipient for fiscal year 2009 Agent Covered Action Linkage of Covered Interests and Covered Actions Covered Interests Source: Center for Responsive Politics, downloaded February 23, 2010

22. Benefits • Economies of scale in application markets • More efficient data entry and integration • More efficient semantic search • More efficient and effective economic and political markets • More efficient and effective information intermediaries (citizen and professional journalists). • More efficient and effective direct consumers of information.

23. Critique of Current Linkage Mechanisms • Highly labor intensive; low degree of automation • Limited to relatively high value, high profile, and simple cases (such as earmarks) • Inflexible, poorly integrated data analysis

24. Sophisticated Example: Budgets • For a state or large city, 100 million percent gain in efficiency over manual covered-interest/covered-action linking methods. • Ability to see new types of relationships never before practical to investigate.

25. More Efficient Semantic Search • One simple query can substitute for thousands of queries over space and time • Boolean Search Example AgencyClaim(Arkansas Governor) and CoveredAction(Budget) and CoveredActionDates(July 1, 2009 to July 1, 2010) and CoveredInterest(All) and CoveredInterestDates(January 1, 2006 to Present)

26. Example: Budgets (no bias ontology) Expenditures for the State of Arkansas across all state agencies, fiscal year 2010

27. Example: Budgets (with bias ontology) Expenditures for the State of Arkansas across all state agencies, fiscal year 2010 Contributions $87,300 $131,600 $400 Covered Interests Covered Actions Drill Down Covered Interests Alert

28. Example: Budgets (drill down view) DTA – Transportation, Department of, fiscal year 2010 Contributions $87,300 $87,300 Drill Down

29. Elected Officials and Voters

30. Doctors and Patients

31. Publishers and Authors

32. Brokers and Clients

33. Disc Jockeys and Listeners (payola)

34. TV News and Viewers (product placement)

35. Movies and Viewers (product placement)

36. Bloggers and Readers

37. Highlights Conflict of interest displayed within the review Reader chooses how the conflict of interest is displayed, e.g., with a highlight, footnote mark, underline, or box Reader chooses what is a material conflict of interest for display. Covered Interests Alert

38. Summary

39. Problems with the Market Solution • The Public Goods Problem • Standards development is expensive and participants have free riding incentives. • Ontology use has significant network effects/positive externalities. • The Google Problem • Google cannot develop and endorse every potentially useful ontology. • Most industries don’t have a single competitor with 70%+ market share. • The Conflict of Interest Problem • Private entities may not want to endorse ontologies that reduce their market power. • Private entities may lack the means to enforce the use of ontologies.

40. Major Public Policy Issue:Degree of Ontology Database Integration

41. Conclusion • Caveat: automation will be imperfect • Implementation timeframe: it will be long • Implications for the open government and media reform communities: standards setting will have to become part of the public policy agenda

42. For more information, go to www.isolon.org If you are interested in joining a standards group to develop a conflict-of-interest ontology, please email contact@isolon.org.