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[…] Proposed Privacy Legislation Eliminates the Need for Regulation , RICH . J.L. & TECH ., Spring 2011, art. no. 13 ¶ 76, at 56 (2011), http: //jolt. richmond.edu/v17i4/article13.pdf . 2013 ] BENEFIT -COST ANALYSIS IN DIGITAL PRIVACY 1099 ifies that “Even where a market failure clearly exists, [agencies] should consider other means of dealing […]

[…] a right of privacy in the absence of statute. Id. at 447. This position was also apparently taken by a Virginia court in 1906. See Barker v. Richmond Newspapers Inc., 14 Va. Cir. 421 (1973) (citing Cyrus v. Bos. Chem. Co., 11 VA. L. REG. 938 (1906)). For a contrary Virginia perspective, see The […]

[…] May 18, 2009, at B3. 10 Emily Steel, A Web Pioneer Profiles Users By Name , WALL ST. J, Oct. 25, 2010 http://online.wsj.com/article/SB100014 24052702304410504575560243259416072.html . 11 Riva Richmond, How to Fix (Or Kill) Web Data About You , N.Y. TIMES , Apr. 14, 2011, at B6. PRIVACY MERCHANTS 3 building a portfolio of many […]

[…] use by companies. 52 Nevertheless, the evidence suggests that few users read the often dense legal or technica l language contained in privacy policies. 53 48 Riva Richmond, As ‘Like’ Buttons Spread, So Do Facebook’s Tentacles , NEW YORK TIMES , Sept. 27, 2011. 49 Id. 50 See http://www.facebook.com/legal/terms (“Facebook users provide their real […]

Does Microsoft + Yahoo = A Privacy Arms Race Among Web Giants? New York Times By Riva Richmond July 31, 2009 When Internet giants team up, civil-liberties advocates tend to worry that their consolidated power will end up hurting the privacy of average users. An agreement between Microsoft and Yahoo to work together on […]

[…] emerging technologies. We are grateful for the continued support of the IMDA, IAPP, as well as our members, partners, and participants, who helped make these events a memorable success. photo1 photo1 photo 2 Photo 2 photo 3 Photo 3 photo 4 Photo 4 photo 5 Photo 5 photo 6 photo 6 photo 7 photo […]

[…] time. Furthermore, the integration of search engines with advertising networks, user accounts, and other online services creates opportunities for linking search behavior with other extensive user profiles, amplifying the potential for privacy intrusions. The longitudinal nature of search logs, capturing behavior over extended periods, adds another layer of sensitivity, as sequences of queries can […]

[…] some bias. Policymakers have therefore identified the risk that biases in the data used to train generative AI systems may cause these systems to produce outputs that amplify these biases and encourage discrimination. Broadly, policymakers highlight two high-risk forms of bias that can arise in generative AI training data. Historical bias refers to patterns […]

[…] some bias. Policymakers have therefore identified the risk that biases in the data used to train generative AI systems may cause these systems to produce outputs that amplify these biases and encourage discrimination. Broadly, policymakers highlight two high-risk forms of bias that can arise in generative AI training data. Historical bias refers to patterns […]

[…] some bias. Policymakers have therefore identified the risk that biases in the data used to train generative AI systems may cause these systems to produce outputs that amplify these biases and encourage discrimination. Broadly, policymakers highlight two high-risk forms of bias that can arise in generative AI training data. Historical bias refers to patterns […]