TAMI Functional Architecture (Figure 1)
The transparency and accountability architecture depends upon three components:
The inference engine provides assistance to the government investigator or analyst in identifying suspicious profiles in those data sets accessible for this purpose. This data would then be processed through an inferencing engine (we use the cwm engine  in this case) that provides investigative results. In addition to these investigatory inferences, a record of the inferences and their justifications will be stored in the Truth Maintenance System (TMS)  . The TMS combined with a proof generator allows anyone with access to the system to determine whether or not the personal information in the system is being used in compliance with relevant rules and consistent with known facts. At critical stages of the investigation, such as sharing of information across agency boundaries or use of information to support an adverse inference (secondary screening, criminal indictment, arrest, etc.), the proof generator will attempt to construct a proof that the use proposed for the data at that transition point is appropriate. The proof generator would be able to draw on information collected in the TMS and bring to bear the relevant rule sets.
Our goal is to develop technical and legal design strategies for increasing the transparency of complex inferences across the Semantic Web and data mining environments. We believe that transparent reasoning will be important for a variety of applications on the Web of the future, including compliance with laws and assessing the trustworthiness of conclusions presented by reasoning agents such as search engines. Our particular focus is on using transparent inferencing to increase accountability for compliance with privacy laws. We also expect that this technical research will provide important guidance to policy makers who are considering how to fashion laws to address privacy challenges raised by data mining in both private sector and homeland security contexts.
National Science Foundation grants: the Transparent Accountable Data Mining Initiative (award #0524481) and Policy Aware Web project (award #0427275).
 Weitzner, Abelson, Berners-Lee, et al., "Transparent Accountable Data Mining: New Strategies for Privacy Protection", MIT CSAIL Technical Report MIT-CSAIL-TR-2006-007 (27 January 2006).
 Weitzner, Hendler, Berners-Lee, Connolly, Creating the Policy-Aware Web: Discretionary, Rules-based Access for the World Wide Web in Elena Ferrari and Bhavani Thuraisingham, editors, Web and Information Security. IOS Press, 2005.
 Berners-Lee, T., CWM A general purpose data processor for the Semantic Web, 2000. http://www.w3.org/2000/10/swap/doc/cwm.html
 [Do87] J. Doyle. A Truth Maintenance System. In Readings in Nonmonotonic Reasoning, pages 259279. Morgan Kaufmann Publishers, San Francisco, CA, USA, 1987.
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