Court filing
ORDER DENYING MOTIONS TO EXCLUDE LLOYD, TUROW, AND KIVETZ AND… — Brooks v. Thomson Reuters Corporation (Dkt. 221)
No. 3:21-cv-01418-EMC · Doc. 221 · Docket on CourtListener
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Case 3:21-cv-01418-EMC Document 221 Filed 08/09/23 Page 1 of 22
1
2
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4 UNITED STATES DISTRICT COURT
5 NORTHERN DISTRICT OF CALIFORNIA
6
7 CAT BROOKS, et al., Case No. 21-cv-01418-EMC
8 Plaintiffs, REDACTED PUBLICLY FILED
9 v. VERSION (ECF NO. 206)
10 ORDER DENYING MOTIONS TO
THOMSON REUTERS CORPORATION, EXCLUDE LLOYD, TUROW,
et al., AND KIVETZ AND BAMBAUER
11
Defendants. Docket Nos. 157-2, 157-4, 159
12
United States District Court
13
14 Plaintiffs Cat Brooks (“Brooks”) and Rasheed Shabazz (“Shabazz”) filed suit individually
15 and on behalf of others similarly situated (“Plaintiffs”) against Defendant Thomson Reuters, Corp.
Northern District of California
16 (“Reuters”) for unjust enrichment and injunctive relief under Unfair Competition Law, Cal. Bus.
17 & Prof. § 17200. Docket No. 145 (“FAC”). Ms. Brooks and Mr. Shabazz allege that Reuters has
18 appropriated and sold individuals’ personal data without their consent through its platform
19 CLEAR, reaping millions of dollars in profit.
20 Along with Brooks and Shabazz’s Motion to Certify Class, Docket No. 148 (“MCC”),
21 Plaintiffs also filed a Daubert motion to exclude the expert testimony of Kivetz and Bambauer.
22 Docket No. 159 (“P’s MTE Kivetz and Bambauer”). Reuters filed a Daubert motion to exclude
23 the expert testimony of Lloyd, Docket No. 157-2 (“D’s MTE Lloyd”) and Daubert motion to
24 exclude the expert testimony of Turow, Docket No. 157-4 (“D’s MTE Turow”). For the following
25 reasons, the Court DENIES the Motion to Exclude Lloyd, DENIES the Motion to Exclude
26 Turow, and DENIES the Motion to Exclude Kivetz and Bambauer.
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1 I. LEGAL STANDARD
2 A. Motion to Exclude Expert Testimony (Daubert)
3 Federal Rule of Evidence 702 tasks a district judge with “ensuring that an expert’s
4 testimony both rests on a reliable foundation and is relevant to the task at hand.” Daubert v.
5 Merrell Dow Pharmaceuticals, Inc., 509 U.S. at 597. “Expert opinion testimony is relevant if the
6 knowledge underlying it has a valid connection to the pertinent inquiry. And it is reliable if the
7 knowledge underlying it has a reliable basis in the knowledge and experience of the relevant
8 discipline.” Elosu v. Middlefork Ranch Inc., 26 F.4th 1017, 1024 (9th Cir. 2022) (quoting Alaska
9 Rent-A-Car, Inc. v. Avis Budget Grp., Inc., 738 F.3d 960, 969 (9th Cir. 2013)).
10 “[T]he test under Daubert is not the correctness of the expert’s conclusions but the
11 soundness of his methodology.” Daubert, 43 F.3d at 1318. The Court is “a gatekeeper, not a fact
12 finder.” Primiano v. Cook, 598 F.3d 558, 568 (9th Cir. 2010) (quotation omitted). If the proposed
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13 testimony meets the thresholds of relevance and reliability, its proponent is “entitled to have the
14 jury decide upon [its] credibility, rather than the judge.” United States v. Sandoval-Mendoza, 472
15 F.3d 645, 656 (9th Cir. 2006). “Challenges that go to the weight of the evidence are within the
Northern District of California
16 province of a fact finder, not a trial court judge. A district court should not make credibility
17 determinations that are reserved for the jury.” City of Pomona v. SQM N. Am. Corp., 750 F.3d
18 1036, 1044 (9th Cir. 2014). “Shaky but admissible evidence is to be attacked by cross
19 examination, contrary evidence, and attention to the burden of proof, not exclusion.” Primiano,
20 598 F.3d at 564.
21 II. DISCUSSION
22 A. Reuters’ Motion to Exclude the Expert Testimony of Lloyd (Docket No. 157-2)
23 Plaintiff’s expert Dr. Terry Lloyd is a certified public accountant (CPA) and a Chartered
24 Financial Analyst (CFA) who authored an expert report on behalf of Finance Scholars Group.
25 Docket No. 127-12 (“Lloyd Rep.”) at 1; Docket No. 157-7 (“Lloyd Dep.”). Dr. Lloyd’s practice
26 primarily involves the valuation of assets, liabilities, and businesses. Id. His report found that it
27 “is possible to calculate Thomson Reuters’s net profits attributable to using and selling
28 Californians’ data through CLEAR.” Lloyd Rep. at 1. At a high level, Dr. Lloyd conducted the
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1 following steps: “(1) examining Thomson Reuters’s revenues from CLEAR during the relevant
2 time period, (2) apportioning the total revenues to just California natural persons, and (3) reducing
3 the gross revenues, based on marginal costs, to derive net profits.” Id. at 17. He concluded that
4 “Thomson Reuters’s net profits attributable to CLEAR’s use of Class members’ data can be
5 calculated as approximately million.” Id. at 19.
6 Reuters moves to exclude Dr. Lloyd’s testimony. Docket No. 157-2 (“D’s MTE Lloyd”).
7 Plaintiffs oppose. Docket No. 168 (“P’s Opp. to D’s MTE Lloyd”).
8 1. Dr. Lloyd’s Analysis
9 Dr. Lloyd determines that of Reuters’ revenue from CLEAR is from recurring
10 revenue (flat-rate subscriptions that allow customers to conduct an unlimited or fixed number of
11 searches) and is from transactional revenue (per-search charges) from the years between 2017
12 and 2021. Docket No. 127-13 (D’s Supplemental Answers to Interrogatories) at 2–3.
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13 Dr. Lloyd allocated revenue by calculating what revenue was generated by CLEAR, what
14 revenue was attributable to California, and what revenue was from information about individual
15 persons. As to how much revenue from CLEAR was attributable to the United States, he
Northern District of California
16 determined that the was made in 2017, in 2018, in 2019,
17 in 2020, and in 2021. Lloyd Rep. at 18. As to how much of the U.S. revenue was
18 attributable to California, he determined that 14.0% of that revenue was attributable to California
19 each year, based on the mean average of California’s share of U.S. GDP in 2021 (14.6%) and
20 California’s share of U.S. arrests from 2018 (13.4%). Id. at 13, 18. He explains that,
21 Because the CLEAR product provides information to a combination
of companies seeking financial information and governmental
22 entities (some of the latter of which are using CLEAR for economic-
related purposes), and at least half the usage pertains to economic
23 activity, I have averaged those two figures (14.6% and 13.4%) to
conservatively estimate that California accounts for about 14% of
24 total U.S. CLEAR revenues.
25 Id. at 13. As to how much of the California revenue resulted from the offering of information
26 about natural persons in the CLEAR database, he determined that of that California revenue
27 was attributable to information about natural persons. Id. He explains that,
28 A spreadsheet file provided by Thomson Reuters during this
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litigation, entitled “Fields_Used_by_Search Type 202009
1 YTD.xlsx,” provides aggregate numbers of CLEAR “desktop”
searches broken down by each search input field combination (e.g.,
2 searches where a customer input solely a social security number,
searches where a customer input a last name, first name, and date of
3 birth). Totaling the search types reasonably tied to natural persons
(i.e., excluding company search, court search, search all business,
4 risk inform business standalone, and intellectual property searches),
the data indicates that approximately of all CLEAR searches
5 relate to individuals.
6 Id. at 13–14.
7 Dr. Lloyd then allocated cost by estimating the “incremental cost” or “marginal cost”—the
8 added cost to Reuters to bundle and deliver information to a single new customer—to Reuters of
9 offering information pertaining to class members through CLEAR. Id. at 14–17. Dr. Lloyd
10 determined that the relevant marginal costs incurred by CLEAR in generating revenues using
11 Californians’ personal information was , resulting in a profit margin. Id. at 18. He
12 explained that “the only marginal costs for CLEAR are royalties that Thomson Reuters pays to
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13 other vendors for data made available through CLEAR.” Id. at 16. He explained that,
14 While Thomson Reuters designates certain additional costs, such as
sales, editorial, and other operational expenses as “direct” costs for
15 CLEAR, those costs do not actually appear to vary based on
customer usage or the availability of particular data within CLEAR
Northern District of California
16 (namely, the availability of data about Californians in CLEAR).
Cybersecurity and infrastructure costs generally function in the same
17 way. The costs that do vary for each additional data point included
within CLEAR—Thomson Reuters’s marginal costs for CLEAR—
18 are the royalty costs Thomson Reuters may incur for making that
data available.
19
20 Id. For vendor agreements between Reuters and its vendors, some of which contain both flat fee
21 royalties and transactional royalties, Dr. Lloyd only included the transactional royalties because he
22 saw “no indication that material flat fee royalty costs would have been avoided had Thomson
23 Reuters not made information about Californians available through CLEAR.” Id.
24 2. Dr. Lloyd’s Calculation of Net Revenue is Based on Reliable Methods
25 Reuters moves to exclude Dr. Lloyd’s opinions on the basis that he should have calculated
26 net revenue—not gross profit as in his report—by deducting fixed operational costs from profit.
27 D’s MTE Lloyd at 6. At the class certification stage, Daubert motions should be tailored to
28 scrutinize expert testimony under Rule 23(b)(3), which only requires that the plaintiff show that its
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1 damages are “capable of measurement on a classwide basis” and that its model for doing so “is
2 consistent with its theory of liability in the case.” Brown, 2022 WL 17961497, at *5 (citing
3 Comcast Corp v. Behrend, 569 U.S. 27, 35 (2013)).
4 The Restatement provides that the proper measure of disgorgement is “net profit” and that
5 a defendant “may be allowed a credit for money expended . . . in carrying on the business that is
6 the source of the profit that is subject to disgorgement.” Restatement (Third) of Restitution and
7 Unjust Enrichment § 51(5)(c) (2011). Reuters explains that “[n]et profit requires the deduction of
8 all expenses incurred in producing the revenue, while gross profit requires only the cost of goods
9 sold to be deducted.” Id. at 7 (citing Gross Profit, Black’s Law Dictionary (11th ed. 2019)).
10 Reuters cites to Dr. Lloyd’s deposition, in which Reuters’ counsel and Dr. Lloyd disagree as to
11 whether the Restatement refers to “gross profit” or “net profit.” See Lloyd Dep. at 56–62.
12 Although Reuters argues that Dr. Lloyd concedes that he calculated “gross profit,” Dr. Lloyd
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13 explained that he uses the terms interchangeably in his report because the calculation of net profit
14 is case-specific depending on the type of operational costs incurred. See Lloyd Dep. at 55 (Dr.
15 Lloyd stating: “For purposes of this report I have used them interchangeably. As I noted in the
Northern District of California
16 report, there might be variations depending on the user or the definition or the circumstances.
17 Marginal, incremental, and variable in my mind for this purpose are synonymous, and I've used
18 them that way here.”).
19 Regardless of whether Dr. Lloyd terms his calculation “gross profit” or “net profit,”
20 Reuters’ argument boils down to contesting that, in calculating what portion of CLEAR’s profit
21 was attributable to Plaintiffs’ data, Dr. Lloyd should have deducted “sales, editorial, and other
22 operational expenses” as costs from his calculation of net revenue (whether he refers to “gross
23 profit” or “net profit”). Id. at 7–8 (arguing that it is not a reliable method to calculate net profit to
24 “deduct only some of the costs of goods sold while ignoring all of the other costs necessary to
25 operate the business”). But Dr. Lloyd has used standard accounting practices and logic in making
26 his calculations. He has abided by the Restatement, which describes the focus of disgorgement
27 damages as the harm “attributable to [defendant’s] underlying wrong.” Restatement (Third) of
28 Restitution and Unjust Enrichment § 51 cmt. e (2011) (“The profit for which the wrongdoer is
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1 liable by the rule of § 51(4) is the net increase in the assets of the wrongdoer, to the extent that this
2 increase is attributable to the underlying wrong.”). It is reasonable, for the purposes of a Daubert
3 motion, for an expert to assume that individuals’ data substantially drives the revenue from
4 CLEAR when that data makes up the entirety of the product, not merely a component of a larger
5 product. Id. (“The question of attribution will sometimes have a simple answer. Observable facts
6 concerning the defendant’s activities may support a direct inference about the proportion of the
7 defendant’s overall business profits, or the increase over profits that would otherwise have been
8 realized, attributable to the defendant’s interference with the claimant's interests.”); see also
9 Sheldon v. Metro-Goldwyn Pictures Corp., 309 U.S. 390, 408–09 (1940) (holding that the court
10 properly relied on expert testimony in “making a fair apportionment” as to what profits from a
11 motion picture are attributable to a copyrighted play). Dr. Lloyd has explained that operational
12 costs were not specifically attributable to Californian’s personal information and “do not actually
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13 appear to vary based on customer usage or the availability of particular data within CLEAR,” and
14 thus is not an additional cost generated by providing information on natural persons on CLEAR.
15 Lloyd Rep. at 16. If these costs (that would have been incurred regardless) were deducted from
Northern District of California
16 the calculation of net profit, then Reuters would effectively receive a subsidy on its other business
17 as a windfall. The question of whether Dr. Lloyd should have included additional cost categories
18 is a question for the jury, not a basis on which to exclude the report as unreliable. And whether he
19 should have included additional cost categories goes to the weight of his opinion, not to
20 admissibility. See In re Actiq Sales & Mktg. Pracs. Litig., No. CIV.A. 07-4492, 2014 WL
21 3572932, at *8 (E.D. Pa. July 21, 2014) (finding that “the issue of whether certain costs should
22 have been included in [the expert’s] analysis is ultimately more a question of the accuracy of her
23 analysis, not necessarily the methodology she used in conducting her analysis”). Indeed, the scope
24 of Reuters’ liability (which cost categories it extends to) is not an appropriate issue to be resolved
25 at the class certification stage through a Daubert motion. See Brown, 2022 WL 17961497, at *4
26 n.4 (explaining that a defendant’s arguments on a “merits dispute about the scope of its liability []
27 is not appropriate for resolution at the class certification stage of this proceeding”).
28 Moreover, Dr. Lloyd’s calculation of cost need not have applied the same 14% Californian
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1 and natural person proportions that he used in his calculation of revenue. Reuters does not
2 allege that it earns profits and expends costs in the same way, so there is little to support to the
3 notion that Dr. Lloyd’s measurements profits and costs must be approximated in the same non-
4 “hybrid” way. D’s MTE Lloyd at 10. Reuters’ criticisms of Dr. Lloyd’s methodology rest on the
5 erroneous assumption that deducting all costs from revenue is the only accurate way of measuring
6 net profit. Which of these opinions is a more accurate reflection of Reuters’ net profits from
7 Californian’s personal information in CLEAR comes down to a difference of expert opinion and is
8 a question for the jury.
9 Dr. Lloyd’s calculation of net profit is properly based on reliable facts and methods,
10 including his decision to exclude operational expenses from the cost calculation. To the extent
11 that such a calculation is not an optimal calculation of net profit, Reuters may explore that line of
12 questioning on cross-examination. The Court DENIES the Daubert motion on this ground.
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13 3. Dr. Lloyd’s Calculation of Gross Revenue is Based on Reliable Facts and Data
14 Reuters moves to exclude Dr. Lloyd’s opinions on the basis that he should have excluded
15 revenues that were irrelevant to Plaintiffs’ theory of liability. D’s MTE Lloyd at 9. Specifically,
Northern District of California
16 Reuters argues that it is not a reliable method to simply average California’s share of GDP
17 (14.6%) with its share of national arrests (13.4%) to determine that 14% of CLEAR’s domestic
18 revenue should be allocated to California. Id. at 11.
19 Dr. Lloyd utilized these two metrics based on a reliable method. For Reuters’ revenue
20 from corporate economic-related activity, Dr. Lloyd uses California’s share of GDP as a proxy
21 (14.6%). For Reuters’ revenue from governmental law-enforcement activities, Dr. Lloyd uses
22 California’s share of national arrests as a proxy (13.4%). Based on the fact that “about of
23 CLEAR’s current customers are corporate users” and the other are governmental entities,
24 Lloyd Rep. at 13 n.47, Dr. Lloyd uses a simple mean to combine the two metrics of share of GDP
25 (14.6%) and share of arrests (13.4%) to achieve an average of 14%. It is logical and reliable to
26 average these metrics based on the data that about the customers are governmental entities
27 and are corporate users. It is especially telling that, as Plaintiffs argue, subsequent document
28 production showed that across all CLEAR searches run since 2015 with a “state” search field,
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1 were for California. P’s Opp. to D’s MTE Lloyd at 8 n.6. Both the input metrics of Dr.
2 Lloyd’s calculation and the calculation itself are reliable.
3 Moreover, “that the experts dispute what the appropriate inputs should be does not
4 undermine the approach or the reliability of [the] model.” In re Lidoderm Antitrust Litig., No. 14-
5 MD-02521-WHO, 2017 WL 679367, at *12 (N.D. Cal. Feb. 21, 2017). Reuters’ criticism that the
6 metrics used to calculate Californian’s proportion of national arrests are inaccurate—that “Dr.
7 Lloyd’s sole source for the arrest rate did not account for arrests by the New York City Police
8 Department or the District of Columbia’s Metropolitan Police Department, or from the State of
9 Iowa, and it appears to be missing data from large swaths of Illinois”—goes to the weight of the
10 testimony, not the reliability. See Grasshopper House, LLC v. Clean & Sober Media LLC, No.
11 218CV00923SVWRAO, 2019 WL 12074086, at *3 (C.D. Cal. July 1, 2019) (“[A]n expert’s
12 opinion is not per se unreliable because it relies on some unverified or inaccurate information
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13 provided by the expert’s client.” (internal citations omitted)). Likewise, Reuters’ criticism that Dr.
14 Lloyd miscalculated percentage (Reuters alleges that the Californian share of national arrests is
15 12.8% as opposed to 13.4%) does not render the calculation excludable. See Gen. Elec. Co. v.
Northern District of California
16 Joiner, 522 U.S. 136, 146 (1997) (asserting that the focus of a Daubert inquiry must generally be
17 “on principles and methodology, not on the conclusions they generate”); McCrary v. Elations Co.
18 LLC, No. EDCV-13-00242-JGB (OPx), 2014 WL 12589137, at *15 (C.D. Cal. Dec. 2, 2014) (“An
19 expert opinion is not rendered inadmissible merely because the conclusion reached may not be
20 correct.”).
21 Dr. Lloyd’s calculation of gross revenue is properly based on reliable facts and methods,
22 including his calculation of the 14% allocation of CLEAR’s domestic revenue to California. The
23 Court DENIES the Daubert motion on this ground.
24 B. Reuters’ Motion to Exclude the Expert Testimony of Turow (Docket No. 157-4)
25 Plaintiff’s expert Prof. Joseph Turow is a Professor of Media Systems and Industries at the
26 Annenberg School for Communications at the University of Pennsylvania. Docket No. 124-7
27 (“Turow Rep.”) at 1; Docket No. 157-10 (“Turow Dep.”). His research concerns the internet,
28 marketing, society, privacy, and mass media. Id. For this case, Prof. Turow considered the
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1 amounts and types of information collected and made available, the connections made between
2 disparate sources of information, the protections in place to guarantee accuracy and reliability of
3 the information, and the opportunities for individuals to understand, contextualize, correct, and
4 remove information. Id. at 5. His expert report concludes that “Thomson Reuters’ operation of
5 the CLEAR product affects privacy interests of Californians—the right to control personal
6 information and to be let alone—in such a way that all Californians whose information is
7 accessible through CLEAR are harmed.” Id. at 5.
8 Reuters now moves to exclude Prof. Turow’s testimony on three grounds. Docket No.
9 157-4 (“D’s MTE Turow”). Plaintiffs oppose. Docket No. 166 (“P’s Opp. to D’s MTE Turow”).
10 1. Prof. Turow’s Opinion on Californians’ Ethical and Social Rights of Privacy Are
11 Properly Based on Seminal Legal Scholarship and Historical Legislation
12 First, Reuters seeks to exclude Prof. Turow’s opinion that all Californians have suffered
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13 uniform harm to their ethical and social rights on the basis that it is nothing more than ipse dixit
14 philosophizing and “not supported by any objective or verifiable evidence or methodology.” D’s
15 MTE Turow at 1, 4. Reuters argues that “Prof. Turow lacks objective verifiable evidence that his
Northern District of California
16 testimony is based on scientifically valid principles.” Id. at 4. Experts must explain their
17 “reasoning between steps in a theory . . . based on objective, verifiable evidence and scientific
18 methodology of the kind traditionally used by experts in the field.” Domingo ex rel. Domingo v.
19 T.K., 289 F.3d 600, 607 (9th Cir. 2002). In this case, Prof. Turow’s reliance on legal scholarship
20 is a method traditionally used in the field of academic research. He cites to historical research
21 about the origins and recognition of privacy rights in American and in California, as well as other
22 seminal research in the field, such as Alan F. Westin’s book Privacy and Freedom and Louis
23 Brandeis’ article The Right to Privacy. Turow Rep. at 6–7, 9. He cites to legislative proposals,
24 California ballots, and legislators’ comments. Turow Rep. at 8–9. He cites to modern privacy
25 scholars’ work. Turow Rep. at 30–32.
26 Prof. Turow’s expertise also rests on solid credentials and qualifications as a privacy and
27 marketing scholar: he has researched, taught, presented, and been awarded on his work on privacy
28 and digital audience targeting. Turow Rep. at 1–2. And there is no dispute about whether the
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1 general history of privacy is relevant to this case: Prof. Turow’s opinions about the ethical and
2 social rights of privacy provide “background and context information about what privacy is [and]
3 the importance of privacy.” Brown v. Google, LLC, No. 20-CV-3664-YGR, 2022 WL 17961497,
4 at *10 (N.D. Cal. Dec. 12, 2022). The Court DENIES the Daubert motion on this ground.
5 2. Prof. Turow Need Not Have Conducted a CLEAR-specific Survey Nor Expounded
6 Upon His General Citations to Surveys Conducted by Others
7 Second, Reuters challenges Prof. Turow’s failure to articulate how consumer preference
8 affects his opinion or the reliability of the consumer surveys that he cites. D’s MTE Turow at 5.
9 Reuters argues that Prof. Turow’s expert opinion should be excluded because he conducted
10 no survey of consumer preferences related to CLEAR. Plaintiffs contends that Prof. Turow need
11 not have conducted a CLEAR-specific survey because his expert opinion doesn’t extend to the
12 issue of consumers’ perceptions; in fact, it is his expert opinion that privacy harms are not affected
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13 at all by individual consumers’ perceptions. P’s Opp. to D’s MTE Turow at 3. Indeed, Prof.
14 Turow explains that CLEAR can harm consumers without their knowledge. See Turow Rep. at 14
15 (“[W]hether a person has consented to the sale of their data has nothing to do with whether
Northern District of California
16 Thomson Reuters lists the use as permissible.”); id. at 14–15 (“Californians have not consented to
17 the collection of sale of their information through Thomson Reuters’ CLEAR platform, and
18 largely do not know about it.”); id. at 16 (“Californians are not meaningfully informed of CLEAR,
19 let alone [Reuters’ Public Records Privacy Policy], and for this reason alone the policy can hardly
20 be considered protective of privacy.”). The lack of a CLEAR-specific survey is not an “analytical
21 chasm,” D’s MTE Turow at 5, that renders the entire report baseless. It is a matter that can be
22 explored on cross-examination.
23 Reuters also argues that, to the extent Prof. Turow relies on survey evidence conducted by
24 others to support his “sense” of consumer preferences, that extrinsic evidence is unreliable and
25 fails to support his own conclusion. D’s MTE Turow at 5. If an expert relies on surveys
26 conducted by others, the expert must demonstrate that the survey was “conducted according to
27 accepted principles” and that “the results were used in a statistically correct manner.” F.T.C. v.
28 Com. Planet, Inc., 642 F. App’x 680, 682 (9th Cir. 2016); M2 Software, Inc. v. Madacy Ent., 421
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1 F.3d 1073, 1087 (9th Cir. 2005). “[W]hile studies involving similar but not identical situations
2 may be helpful, an expert must set forth the steps used to reach the conclusion that the research is
3 applicable.” Domingo, 289 F.3d at 606. Here, Prof. Turow cites to six papers describing surveys.
4 See Turow Rep. at 6–7 (citing the Turow working paper, National Telecommunications and
5 Information Administration survey, ValuePenguin survey, boyd and Marwick paper, Pew
6 Research Center survey (2015), and Pew Research Center survey (2019)). However, for each
7 paper, Reuters only disagrees with the survey’s relevance to the circumstances at issue, not with
8 the survey’s methodology or statistical analysis. See D’s MTE Turow at 6–7. Importantly, Prof.
9 Turow does not rely on these surveys to demonstrate what consumers think about CLEAR in
10 particular. Rather, as Plaintiffs point out, the 2015 Pew Center survey and the National
11 Telecommunications and Information Administration survey are only cited for the general
12 proposition that “[t]he majority of Americans believe that privacy and confidentiality are very
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13 important aspects of their lives.” See Turow Rep. at 6 n.6 (citing the 2015 Pew Center survey, and
14 National Telecommunications and Information Administration survey). The Turow 2015 working
15 paper is cited to generally show that “84% of adult Americans want to have control over what
Northern District of California
16 businesses can learn about them online.” See id. at 6 n.6 (citing the Turow 2015 working paper).
17 The ValuePenguin survey, 2019 Pew Center survey, and the boyd study are cited for the general
18 proposition that “[c]ontemporary research continues to support the principle that consumers view
19 the right to control their information as central to individual privacy.” See id. at 7 n.9 (citing the
20 ValuePenguin survey, 2019 Pew Center survey, and the boyd study). Simply because each of
21 these studies include surveys is not fatal; an expert should not need to establish the statistical
22 accuracy of a survey when his expert opinion does not rely upon the survey results. Prof. Turow
23 does not attempt to import the statistics of the cited surveys as quantitative data on the CLEAR
24 tool, and thus he does not need to show the statistical accuracy of data that he did not rely upon.
25 Cf. M2, 421 F.3d at 1087 (requiring a description of the accepted principles behind an expert’s
26 cited survey when used to prove “likelihood of confusion” in a trademark infringement suit). And
27 Prof. Turow has the ability “rooted in the expert’s relevant expertise” to opine upon the potential
28 understandings and expectations of reasonable consumers about privacy in general. Cf. Brown v.
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1 Google, LLC, No. 20-CV-3664-YGR, 2022 WL 17961497, at *11 (N.D. Cal. Dec. 12, 2022)
2 (finding that a security technologist could opine “generally about relevant privacy issues” but not
3 on the “understandings and expectations of consumers specifically”). So long as Prof. Turow
4 cabins his expert testimony on these surveys to solely this proposition—general views on
5 privacy—and not to compare the various surveys’ results to specific consumer sentiment on
6 CLEAR—reliance on these citations is not so problematic as to undermine the Turow report.
7 Reuters has thus not established that Prof. Turow’s reliance on the papers falls short of the
8 Daubert standard. The Court DENIES the Daubert motion on this ground.
9 3. Prof. Turow’s Report Speaks to Only the “Ethical or Social” Privacy Rights and
10 Not “Legal” Privacy Rights and Thus Are Not an Improper Legal Conclusion
11 Third, Reuters challenges Prof. Turow’s opinion on whether consumers’ legal rights have
12 been violated as an improper legal conclusion. D’s MTE Turow at 7. “[A]n expert cannot testify
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13 to a matter of law amounting to a legal conclusion.” United States v. Tamman, 782 F.3d 543, 552-
14 53 (9th Cir. 2015) (citing Fed. R. Evid. 702(a)). Testimony about whether a right has been
15 violated is a legal opinion subject to exclusion. See, e.g., Gong v. Jones, 2008 WL 4183937, at *4
Northern District of California
16 (N.D. Cal. Sept. 9, 2008) (excluding testimony regarding “probable cause” because it went to the
17 ultimate legal issue of “whether any particular conduct violated any particular law or right”).
18 However, Prof. Turow’s opinion that “the CLEAR product affects privacy interests,” Turow Rep.
19 at 5, is not a legal conclusion: he clarifies in his deposition that his report is made in the context of
20 privacy interests as an “ethical or social” right, not a “legal” right. See Turow Dep. at 174 (“Q.
21 All right. And you also testified that you’re not rendering a legal opinion in this case. So what is
22 the nature of the rights you are talking about in your report? Are they legal rights? Moral rights?
23 Psychological rights? What – what type of right are you talking about? A. I would characterize
24 them as ethical and social.”). Indeed, although his report describes the legal history of privacy
25 rights, it does not render any ultimate legal conclusion that CLEAR violated privacy law.
26 The Turow report, as it stands, does not appear to give legal conclusions, so the Court
27 DENIES the Daubert motion to exclude Prof. Turow’s opinions as improper legal conclusions.
28
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1 C. Plaintiffs’ Motion to Exclude the Expert Testimony of Kivetz (Docket No. 159)
2 Reuters’ rebuttal expert Dr. Ran Kivetz is a professor at Columbia University Business
3 School. His field of expertise includes consumer psychology and behavior, survey design,
4 perception, judgment, decision making. Docket No. 151-14 at 4 (“Kivetz Rep.”); Docket No. 158-
5 4 (“Kivetz Dep.”). Dr. Kivetz’s main opinion is a critique that Prof. Turow “fails to provide any
6 empirical or scientific evidence of common injury allegedly suffered by the putative class
7 members due to CLEAR.” Id. at 11. In forming this opinion, Dr. Kivetz conducted the following
8 steps: (1) analyzed Prof. Turow’s statements about “ethical and social” privacy harm, (2)
9 evaluated the academic literature and consumer surveys upon which Prof. Turow relies, (3)
10 identified and analyzed other sources of evidence for assessing harm in this case, which he opines
11 that Prof. Turow inappropriately ignored. Id., Part D, E.
12 Plaintiffs move to exclude Dr. Kivetz, or at least Parts D and E of his report about the
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13 proper measure of privacy. Docket No. 158-3 (“P’s MTE Kivetz & Bambauer”). Reuters
14 opposes. Docket No. 165 (“D’s Opp. to P’s MTE Kivetz & Bambauer”).
15 1. Dr. Kivetz is Qualified to Opine on Consumer Preferences and Consumer Opinion
Northern District of California
16 Plaintiffs first argue that Dr. Kivetz is not qualified to define privacy harms nor to opine on
17 “California privacy rights” or “the proper contours of privacy harms[] under California law.” P’s
18 MTE Kivetz & Bambauer at 3–4.
19 Both arguments mischaracterize the nature of Dr. Kivetz’s opinions. First, Dr. Kivetz does
20 not opine—as characterized by Plaintiffs—“that the privacy harms here can only be defined and
21 measured based on subjective consumer expectations.” See id. at 3. Rather, he opines on what he
22 believes what is a logical conclusion of Prof. Turow’s conceptualization of privacy harms as
23 ethical and social harms. See Kivetz Rep. at 11–12. Prof. Turow had opined that “ethical and
24 social [privacy] rights do[] not correspond to any physical injury, financial consequences, loss of
25 property, or other quantification scheme.” Id. (citing Turow Dep. at 174, 177–185, 188). In
26 response, Dr. Kivetz argues that because Prof. Turow does not provide any examples of how
27 ethical and social harms can have quantifiable or legal effects, Prof. Turow’s own logic requires
28 that such harms be defined and measured by consumer perceptions and preferences. Id. Second,
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1 Dr. Kivetz does not opine on the precise scope of California privacy rights. See D’s Opp. to P’s
2 MTE Kivetz & Bambauer at 12. His opinions only concern consumer preferences and perceptions
3 about privacy rights and privacy harms. Id.
4 Dr. Kivetz is qualified to opine on how violation of California privacy rights would harm a
5 consumer. “An expert witness may be qualified by ‘knowledge, skill, experience, training, or
6 education’ as to the subject matter of the opinion.” Brown, 2022 WL 17961497, at *1 (quoting
7 Fed. R. Evid. 702). Dr. Kivetz is an expert on consumer psychology and behavior who teaches
8 graduate and doctoral courses regarding consumer behavior and perception. Kivetz Rep. at 4. Dr.
9 Kivetz has conducted, supervised, and evaluated over 1,000 consumer surveys, which are relevant
10 as to the consumer surveys cited by Prof. Turow. Id. at 6. Thus, Dr. Kivetz is qualified to opine
11 on consumer opinions and preferences on how a violation of privacy rights would cause them
12 harm.
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13 Plaintiffs then argue that Dr. Kivetz’s opinions do not rest on reliable foundations and are
14 irrelevant to the task at hand. P’s MTE Kivetz & Bambauer at 4. But Dr. Kivetz’s analysis is
15 expressly based on “existing scientific research and treatises regarding survey design, consumer
Northern District of California
16 behavior, and decision making.” See D’s Opp. to P’s MTE Kivetz & Bambauer at 11–12. For
17 instance, Dr. Kivetz has cited academic literature showing how factors such as personality,
18 demographics, and level of trust might explain the variation in consumers’ perceptions towards the
19 use of information. See e.g., id. at 79–81 n.268–276 (citing several peer-reviewed articles and
20 surveys). It is on the foundation of such academic literature that Dr. Kivetz formulates his opinion
21 about the numerous variables that impact consumer preferences with respect to privacy. See
22 generally Kivetz Rep., Part E.1.
23 Lastly, Plaintiffs argue that Dr. Kivetz’s experience measuring consumer perceptions about
24 food labeling and false advertising is irrelevant to consumer perceptions in a privacy case. P’s
25 MTE Kivetz & Bambauer at 6. Considering Dr. Kivetz’s general expertise in consumer
26 psychology and behavior, his lack of specialization in privacy matters goes to weight, not
27 admissibility, of his opinions. See In re Silicone Gel Breast Implants Prod. Liab. Litig., 318 F.
28 Supp. 2d 879, 889 (C.D. Cal. 2004) (“[Expert’s] qualifications are construed broadly. . . . A court
14
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1 abuses its discretion when it excludes expert testimony solely on the ground that the witness’s
2 qualifications are not sufficiently specific if the witness is generally qualified.”). Dr. Kivetz has
3 demonstrated sufficient expertise in measuring consumer perception that may be applied here.
4 The Court DENIES the Daubert motion on this ground.
5 2. Dr. Kivetz Has Proposed a Methodology Compatible for Evaluating Privacy Harm
6 Plaintiffs argue that Dr. Kivetz has not shown that consumer perception surveys are a
7 widely accepted methodology in the privacy field when he allegedly claimed that the “only way to
8 establish and measure privacy harm” is “getting into the minds of consumers.” P’s MTE Kivetz &
9 Bambauer at 6.
10 This argument is unpersuasive for two reasons. First, Plaintiffs’ expert Prof. Turow
11 heavily bases his opinion on general consumer perceptions. See Docket No. 157-10, at 118:19–
12 121:14 (Prof. Turow answering affirmatively to the questions: “Are you offering opinion in this
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13 case regarding consumer perceptions?”, “So is your opinion in this case purporting to reflect
14 consumer perceptions . . .?”, and “So at Footnote 6 and the materials cited in Footnote 6, this is
15 where you -- this is one place your report addresses consumer perceptions?”). Dr. Kivetz’s
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16 opinions are merely rebuttal arguments that Prof. Turow’s view of ethical and social harms, when
17 taken to their logical conclusion, require such harm to be measured by consumer perceptions and
18 preferences. See infra, Section C.
19 Second, Plaintiffs again mischaracterized Dr. Kivetz’ proposal. Dr. Kivetz never opined
20 that surveys are the only acceptable substantiation for Prof. Turow’s opinions. Rather, he asserts
21 that Prof. Turow should bolster his conclusion about common harm with “(original) survey data
22 collected specifically for the current litigation, analysis of (existing) relevant secondary data, or a
23 careful application to the particulars of CLEAR of existing (empirical) academic literature on
24 consumers’ perceptions and preferences about privacy.” Kivetz Rep. at 24 (emphasis added). Dr.
25 Kivetz has sufficient expertise in consumer psychology and behavior, survey methods, and
26 perceptions to support the perspective that consumer perception surveys are one of many
27 acceptable methodologies in the privacy field. Id. at 4. Thus, it is appropriate for Dr. Kivetz to
28 critique another experts’ opinion as lacking survey evidence, secondary data, or application of
15
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1 academic literature.
2 The Court DENIES the Daubert motion on this ground.
3 3. Dr. Kivetz Has Cited Sufficient Facts or Data
4 Finally, Plaintiffs argue that Dr. Kivetz failed to cite sufficient facts or data. P’s MTE
5 Kivetz & Bambauer at 8. However, in his report, Dr. Kivetz has cited over 200 independent
6 sources, including 61 case material documents, 59 third-party sources (i.e., peer-reviewed
7 academic articles, textbooks, industry reports, etc.), and 104 examples of CLEAR use cases. D’s
8 Opp. to P’s MTE Kivetz & Bambauer at 12 n.4.
9 Plaintiffs particularly find inadequate Dr. Kivetz’s evidentiary support for his opinion that
10 some class members who recognize the benefits of CLEAR and other data aggregation businesses
11 are less likely to be surprised, blindsided, dissatisfied with or expect compensation from data
12 aggregators like CLEAR. Kivetz Rep. at 95–99. But Dr. Kivetz’s opinion is not “wholly
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13 speculative or unfounded testimony.” See Elosu v. Middlefork Ranch Inc., 26 F.4th 1017, 1025
14 (9th Cir. 2022). Dr. Kivetz has examined six articles that “indicate variation (i.e., a lack of
15 commonality) in consumers’ perceptions or preferences regarding privacy.” See Kivetz Rep. at
Northern District of California
16 75; see generally Kivetz Rep., Part D.2. Additionally, Dr. Kivetz has presented a figure from
17 another peer-reviewed article that identifies perceived benefits of information disclosure and
18 shows, among other things, that a consumer’s behavioral reaction to information disclosure is
19 shaped by their calculus of risks/costs and benefits.1 Kivetz Rep. at 77–80. Dr. Kivetz then
20 enumerates some benefits that government agencies, corporate or business entities, and individuals
21 can derive or have derived from CLEAR. Id. at 84, 146–68. It is not too great an analytical leap
22 for Dr. Kivetz to suggest that some putative class members—as a subset of consumers—might
23 have different preferences and that those who recognize CLEAR’s benefits would have less
24 negative reactions to CLEAR. See Kennedy v. Collagen Corp., 161 F.3d 1226, 1230 (9th Cir.
25
26
1
See Smith, H. Jeff, Tamara Dinev, and Heng Xu (2011), “Information Privacy Research: An
Interdisciplinary Review,” MIS Quarterly, 35(4), 989–1015, at 1001 (“A subset of empirical
27 studies . . . assum[es] that a consequentialist tradeoff of costs and benefits is salient in determining
an individual’s behavioral reactions.”) (“Scholars have identified three major components of
28 benefits of information disclosure including financial rewards, personalization, and social
adjustment benefits.”).
16
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1 1998) (holding that the analytical gap is not too great even where an expert opined that there was a
2 link between a medical product and a disease but failed to cite studies that directly confirmed such
3 a link). Any countervailing concerns go to weight, not admissibility. See id. at 1230–31.
4 Plaintiffs also argue that Dr. Kivetz does not offer evidence for his claim that some
5 putative class members who view CLEAR as producing a net benefit might accept the alleged
6 privacy violations. P’s MTE Kivetz & Bambauer at 8–9. However, Dr. Kivetz has discussed a
7 2019 Pew Survey—which Prof. Turow also cites—that shows a significant percentage (between
8 36% and 57%) of U.S. adults were “somewhat” or “very” “comfortable with companies using
9 their personal data” for purposes such as “improv[ing] their fraud prevention systems” and
10 “sharing with outside research groups that might help improve society.” Kivetz Rep. at 60. Many
11 participants would accept the use of their personal data in some circumstances that could benefit
12 the society. Id. at 62. For purposes of a Daubert motion, it is reasonable for Dr. Kivetz to
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13 conclude that some putative class members might find that the benefits of CLEAR outweigh the
14 harms and might accept CLEAR on this basis. Id. at 63; see Elosu, 26 F.4th at 1025.
15 There is another opinion made once in passing in the Kivetz report: “it is likely that at least
Northern District of California
16 some putative class members who are informed about CLEAR’s various potential benefits and
17 safeguards would prefer to (or even be willing to pay to) have information collected and/or
18 disseminated through CLEAR.” Kivetz Rep. at 95 (emphasis added). The 2019 Pew Survey
19 indicates that 6% to 10% of respondents would be “very comfortable” with companies using their
20 personal data; this provides a reliable basis for Dr. Kivetz’s opinion that “some putative class
21 members . . . would prefer to . . . have their information collected and/or disseminated through
22 CLEAR.” Id. at 60–61. Dr. Kivetz’s opinion about putative class members’ willingness to pay is
23 a greater analytical leap, but it is still admissible. Experts are allowed to make predictions about
24 consumer purchasing decisions. See, e.g., Mier v. CVS Pharmacy, Inc., No.
25 820CV01979DOCADS, 2022 WL 1599633, at *6 (C.D. Cal. May 9, 2022) (explaining that the
26 expert “is doing precisely what experts in the advertising field do: making a prediction based on
27 experience as to how a claim will impact the consumer’s choice in purchasing a product”). Here,
28 Dr. Kivetz makes his prediction based on his knowledge, experience, and research. “Most of [Dr.
17
Case 3:21-cv-01418-EMC Document 221 Filed 08/09/23 Page 18 of 22
1 Kivetz’s] research has focused on buyers’ purchase behavior . . . ; and the effect of product
2 characteristics . . . on purchase decisions and perceptions.” Kivetz Rep. at 4; see Mier, 2022 WL
3 1599633, at *6. Elsewhere in the Report, Dr. Kivetz has cited use cases where consumers derived
4 significant benefits from CLEAR’s collection and dissemination of their information. For
5 example, “Department of Veterans Affairs has used CLEAR to locate veterans who are missing
6 their financial support due to a change of address,” including “a veteran who was missing $31,317
7 in disability compensation.” Kivetz Rep. at 87. Dr. Kivetz’s opinion that some putative class
8 members who might derive significant benefits from CLEAR might be willing pay to have their
9 information collected and/or disseminated through CLEAR is thus admissible.
10 Plaintiffs also argue that Dr. Kivetz parrots Reuters’ talking points without offering his
11 own opinion. See P’s MTE Kivetz & Bambauer at 9 (citing Ask Chemicals, LP v. Computer
12 Packages, Inc., 593 Fed. Appx. 506, 510 (6th Cir. 2014) (quoting King-Indiana Forge, Inc. v.
United States District Court
13 Millennium Forge, Inc., No. 1:07–cv–00341–SEB–SML, 2009 WL 3187685, at *2 (S.D. Ind.
14 Sept. 29, 2009))). However, Dr. Kivetz does not parrot CLEAR’s talking points. These so-called
15 “talking points” consist of factual information, such as: CLEAR’s use cases (e.g., use of CLEAR
Northern District of California
16 in preventing fraud); CLEAR’s data collection practice (e.g., CLEAR does not directly collect data
17 from consumers and has “a licensing agreement with every third-party [data] vendor”); CLEAR’s
18 business model (e.g., it does not directly “sell” any given individual’s “personal information” or
19 “dossier” to a third-party company); the existence of data aggregation platforms. See e.g., Kivetz
20 Rep. at 10 n.15–16, 47 n.161, 96 n.335. But Dr. Kivetz, as an expert, is allowed to testify to an
21 opinion formed on the basis of information that is handed to rather than developed by him.” See
22 King-Indiana Forge, 2009 WL 3187685, at *2 (quoting In re James Wilson Associates, 965 F.2d
23 160, 172 (7th Cir.1992)). In fact, he incorporates the information in forming his own opinions
24 about consumer perceptions. For example, he uses information about CLEAR’s use cases to form
25 his opinion that “Turow ignored all beneficial aspects . . . of CLEAR for consumers when forming
26 his conclusion that the product commonly harmed the putative class members.” See e.g., Kivetz
27 Rep. at 10–11, 88.
28 Lastly, Plaintiffs claim that Dr. Kivetz inadequately reviewed Prof. Turow’s work and
18
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1 sources. P’s MTE Kivetz & Bambauer at 10–11. An independent review of the Kivetz report
2 shows that Dr. Kivetz has extensively reviewed Prof. Turow’s works and sources. See Kivetz
3 Rep. at 27–31 (examining Prof. Turow’s arguments and evaluating in-depth 20 sources that he
4 cites). As to Plaintiffs’ critique that Dr. Kivetz fails to offer suggestions on how Prof. Turow’s
5 survey questions could be improved, it is the Plaintiffs—not Dr. Kivetz—who have the burden of
6 designing a reliable survey to establish commonality. P’s MTE Kivetz & Bambauer at 10; see
7 Olean Wholesale Grocery Coop., Inc. v. Bumble Bee Foods LLC, 31 F. 4th 651, 665 (9th Cir.
8 2022) (“[P]laintiffs must prove the facts necessary to carry the burden of establishing that the
9 prerequisites of Rule 23 are satisfied” and, to do so, “frequently offer expert evidence, including
10 statistical evidence or class-wide averages.”).
11 The Court DENIES the Daubert motion on this ground.
12 D. Plaintiffs’ Motion to Exclude the Expert Testimony of Bambauer (Docket No. 159)
United States District Court
13 Reuters’ second rebuttal expert Prof. Jane Bambauer is a law professor at the University of
14 Arizona College of Law. Docket No. 151-1 (“Bambauer Rep.”); Docket 159-6 (“Bambauer
15 Dep.”). Her research focuses on “how new information technologies affect privacy, free speech,
Northern District of California
16 and competitive markets.” Bambauer Rep. at 4. For this case, her main opinion is that Prof.
17 Turow’s absolutist definition of privacy—that any loss of control of personal data, no matter how
18 trivial, and no matter how beneficial the data practice may be, constitutes privacy harm—is too
19 rigid and falls outside the bounds of scholarly consensus. Id. at 6.
20 Plaintiffs move to exclude Prof. Bambauer’s testimony on the grounds that she does not
21 discuss California privacy law. P’s MTE Kivetz & Bambauer. Reuters oppose. D’s Opp. to P’s
22 MTE Kivetz & Bambauer.
23 Plaintiffs’ argument is unpersuasive for two reasons. First, “matters of law are for the
24 court’s determination, not that of an expert witness.” United States v. Scholl, 166 F.3d 964, 973
25 (9th Cir. 1999) (citing Aguilar v. International Longshoremen’s Union, 966 F.2d 443, 447 (9th
26 Cir. 1992)). Expert should confine their opinion to “scientific, technical, or other specialized
27 knowledge that will assist the trier of fact to understand the evidence or determine a fact in issue.”
28 United States v. Tamman, 782 F.3d 543, 552 (9th Cir. 2015). Had Prof. Bambauer actually
19
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1 rendered a conclusion on California law, her opinion would not have been proper. See id. (“The
2 district court concluded that Dinehart’s opinion provided only a recitation of facts and the legal
3 conclusion that Tamman acted in conformity with unidentified SEC rules and regulations and
4 otherwise did not break the law. This is not a proper expert opinion.”).
5 Second, Prof. Bambauer does not address California privacy law because Prof. Turow does
6 not either. “A response is within the realm of proper rebuttal testimony if it is clearly intended
7 solely to contradict or rebut evidence on the same subject matter identified by another party in its
8 expert disclosures.” Teradata Corp. v. SAP SE, No. 18-CV-03670-WHO, 2021 WL 6498855, at
9 *2 (N.D. Cal. June 24, 2021) (citation and internal quotation omitted). Although Prof. Turow cites
10 the historical origin of privacy law in California, his opinion is not specifically focused on
11 California privacy law. Turow Rep. at 6–7, 9. Rather, his opinion is directed to the conception of
12 privacy and privacy harm generally. Thus, Prof. Bambauer’s rebuttal report, which offers her
United States District Court
13 opinion about the scholarly conception of privacy harm, is within the realm of a proper expert
14 response.
15 Moreover, Prof. Bambauer is qualified to opine on the academic conception of privacy
Northern District of California
16 harm. Her experience conducting “empirical research on consumer preferences and attitudes
17 related to the collection, disclosure, or repurposing of personal data” is evidence of her
18 qualifications. Bambauer Rep. at 6; see Brown, 2022 WL 17961497, at *1 (“An expert witness
19 may be qualified by knowledge, skill, experience, training, or education as to the subject matter of
20 the opinion.”); cf. Russell v. Walmart Inc., No. CV 19-5495-MWF (JCX), 2020 WL 9073046, at
21 *4 (C.D. Cal. Oct. 16, 2020) (finding that an IP law professor is not qualified to opine on lamp
22 design, sculptural art, marine biology, or jellyfish anatomy).
23 Plaintiffs also seeks to exclude Prof. Bambauer’s testimony on the basis that her tripartite
24 conceptual framework for assessing privacy harms is not widely used by other privacy scholars.
25 P’s MTE Kivetz & Bambauer at 11, 13. Specifically, she proposes that “data practices can be
26 [conceptually] organized into three categories: (1) practices that are per se privacy violations; (2)
27 practices that are . . . per se non-violations[]; and (3) the messy middle category, where the threat
28 to reasonable expectations in privacy will depend on controls and procedures that are in place, the
20
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1 sensitivity of the data, and the costs and benefits of the practice.” Bambauer Rep. at 10–11. The
2 Court disagrees with Plaintiffs. Prof. Bambauer’s tripartite framework is a conceptual framework
3 rather than a test or methodology. Bambauer Dep. at 70 (“[T]his is my way of helping to organize
4 concepts that aren’t always given precise terminology.”); see Bona Fide Conglomerate, Inc. v.
5 SourceAmerca, No. 314CV00751GPCAGS, 2019 WL 1369007, at *10 (S.D. Cal. Mar. 26, 2019)
6 (“In any event, the Court is not concerned by SourceAmerica’s arguments that the Acquisition
7 Time Zones are not reliable and not peer-reviewed. The Acquisition Time Zones are a conceptual
8 framework, not a test or methodology or technique subject to scientific testing.”) (emphasis
9 added). She has cited sufficient facts or data defining each of the three categories and how each is
10 treated by scholars. Bambauer Rep. at 10–12 n.20–25 (citing eight independent sources, including
11 the Second Restatement of Torts, peer-reviewed articles, and an FTC report, among others); see
12 Bona Fide Conglomerate, , 2019 WL 1369007, at *10 (“Moreover, the Acquisition Time Zones
United States District Court
13 adequately fits the particular facts of this case, and will advance the jury’s understanding of the
14 issues.”). Any challenges go to weight, not admissibility. See McMorrow v. Mondelez Int'l, Inc.,
15 No. 17-CV-2327-BAS-JLB, 2020 WL 1237150, at *4 (S.D. Cal. Mar. 13, 2020) (“Dr. Lustig’s
Northern District of California
16 minority view is admissible “because [his] opinions in this case are also based on his review of
17 materials, his research, the studies he has conducted, and his education and training. To the extent
18 Defendant argues Lustig’s methodology is ‘accepted by only a minority of scientists[,]’ this may
19 ‘be a proper basis for impeachment at trial.’”).
20 In conclusion, the Court DENIES the Daubert motion.
21 III. CONCLUSION
22 For the foregoing reasons, the Court DENIES the Motion to Exclude Lloyd, DENIES the
23 Motion to Exclude Turow, and DENIES the Motion to Exclude Kivetz and Bambauer.
24 This order disposes of Docket Nos. 157-2, 157-4, and 159.
25 ///
26 ///
27 ///
28 ///
21
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