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From Our CEO This issue of Forum demonstrates the broad scope and deep impact of Analysis Group’s ongoing work on behalf of our clients. We continue to use leading-edge methodologies and analysis to tackle issues across virtually every sector in the landscape of economic and litigation consulting. Our teams are exploring new applications for machine learning and big data in different types of litigation support. Exciting developments in other evolving areas include examining the bases of competition for biosimilar products introduced in the pharmaceutical market; clarifying key privacy and data security issues in the online world; analyzing the impact of new regulatory requirements in the area of labor and employment; and providing analytical insights into new frameworks for clean energy. We also strengthened our relationships with groundbreaking thinkers in the field of economics. Our firm continues to grow – in size, geographic reach, depth of expertise, and scope of our extensive network of affiliates. We also recently modified our firm leadership. While continuing as CEO, I am now also Chairman of Analysis Group’s Board of Directors. Pierre Cremieux is now President, and Bruce Stangle, formerly Chairman and Co-founder, continues as Co-founder. We remain committed to our distinctive, collaborative culture, which provides the underpinning for the success of our firm and our clients. MARTHA S. SAMUELSON, CEO AND CHAIRMAN ANALYSIS GROUP FORUM | SPRING 2017 www.analysisgroup.com Contents Features Using Machine Learning in Litigation p. 2 Affiliate Roundtable: Privacy and Data Advances in machine learning can help attorneys and Security p. 14 their experts harness the proliferation of data that is Three leading experts weigh in on some of the most requiring analyses beyond the limits of traditional tools. pressing issues surrounding the collection, storage, use, and disclosure of consumer data. Q&A with Affiliate Austan Goolsbee p. 4 Analysis Group has a conversation with its new affiliate, Data Corner: Resolution of Federal Privacy & a leading figure in economic and government circles. Data Security Cases: 1999–2016 p. 16 There are many potential outcomes when these cases Implications of the Tyson Ruling for Class reach the class certification stage. Certification p. 6 Could the Supreme Court’s decision lead to either Evaluating Potential Development of a tighter or weaker standards for class certification? Broader Market for CO2 Allowance Trading p. 17 Q&A with Affiliate David Dranove p. 8 Analysis Group’s most recent report on carbon trading markets sheds light on the potential for expanding One of Analysis Group’s new affiliates discusses his these types of efforts to reduce carbon dioxide recent research and its application to antitrust litigation. emissions. The Biosimilar Revolution Is Just Beginning Recent Litigation p. 18 in the U.S. p. 10 The widespread introduction of medicines that are New Book Edited by Analysis Group: “grown” biologically may be one of the most significant events to hit the drug industries in decades. Decision Making in a World of Comparative Effectiveness Research p. 20 Affiliate Spotlight: Oliver Hart p. 12 Highlights from Our Pro Bono Work p. 21 Analysis Group congratulates Professor Hart on the work that earned him the Nobel Prize in Economics. Will Expanded EEO-1 Data Collection Yield New Insights into Discrimination? p. 13 Proposed changes to employer reporting requirements may have implications for wage discrimination class actions. www.analysisgroup.com ANALYSIS GROUP FORUM | SPRING 2017 1 Statistics & Sampling: Machine Learning Using Machine Learning in Litigation A proliferation of data is requiring analyses beyond the limits of familiar tools such as spreadsheets and statistical software. LISA B. PINHEIRO In health care, the advent of electronic These new techniques can be harnessed to VICE PRESIDENT medical records, the marked decline in DNA help attorneys improve legal strategies, JIMMY ROYER sequencing costs, and the introduction of conduct informed fact discovery, provide VICE PRESIDENT industry reporting requirements such as the testifying experts with the most complete set MIHRAN YENIKOMSHIAN Sunshine Act have ballooned the volume of of relevant information, and prepare analyses VICE PRESIDENT available data. In retail, advances in payment at a previously unseen level of granularity. NICK DADSON ASSOCIATE technology allow point-of-sale devices to capture millions of individual transactions, Here are a few examples of how attorneys can PAUL E. GREENBERG MANAGING PRINCIPAL resulting in much larger data sets along with leverage machine learning: increased security risks. Indeed, in almost Crafting a legal strategy. Machine learning ADAPTED FROM any business, the volume of unstructured “MACHINE-LEARNING can be applied during the discovery phase of information contained in electronic ALGORITHMS CAN litigation to quickly find relevant information HELP HEALTH CARE documents and communications such as email in large quantities of data. Consider a dispute LITIGATION,” BY LISA B. and instant messaging is now enormous. In a PINHEIRO, JIMMY ROYER, over alleged off-label promotion of litigation context, this proliferation of data NICK DADSON, AND PAUL prescription drugs. Conventional analyses E. GREENBERG, can be daunting. PUBLISHED ON might serve as a blunt instrument, grouping LAW360.COM, JUNE 8, Enter machine learning. Machine learning together all patients with a particular 2016; AND “PRACTICAL USES FOR MACHINE uses algorithms to detect complex and condition (e.g., lung cancer). Machine LEARNING IN HEALTH unforeseen relationships in high-dimensional learning methods, on the other hand, can CARE CASES,” BY MIHRAN data (i.e., where there is an abundance identify similarities among patients based on YENIKOMSHIAN, LISA B. PINHEIRO, JIMMY ROYER, of different types of variables, whether a wider and deeper range of variables or AND PAUL E. GREENBERG, involving numbers or unstructured data characteristics. (See figure.) Such clustering PUBLISHED ON LAW360.COM, contained in text or visual images). could reveal clinical differences (e.g., SEPTEMBER 22, 2016. Raw Data Data Clusters Machine Learning Algorithm Machine learning algorithms will generate data clusters by finding connections that may not have been expected. ANALYSIS GROUP FORUM | SPRING 2017 www.analysisgroup.com 2 Statistics & Sampling: Machine Learning advanced age, failure of other cancer therapies, genetic have been discarded as impractical or irrelevant for markers) among groups of patients that might explain expert modeling purposes can be mined for use in use of the drug independent of any promotion. discovery or economic analysis. Uncovering these types of patterns at an early stage can For example, a discrimination case may be proven or be beneficial to attorneys as they contemplate the refuted on the basis of unstructured data in the form of theory of the case. email and voicemail communications. Conventional Accessing information in unstructured communications. methods can be cumbersome, taking up valuable time Unlike conventional statistical methods, machine and staff resources to sift through physical records. With learning algorithms can be “taught” to recognize the a natural language processing algorithm based on importance of particular word and phrase combinations machine learning, search efficacy can be enhanced while or other characteristics within documents such as reducing the time and effort required. published articles, patent claims, medical notes, How to Employ a Machine Learning Approach regulatory filings, and emails. These characteristics can Of course, as was the case with other new technologies be associated with specified outcomes, and then used to that have been introduced to the courtroom (e.g., improve predictions or support an argument. fingerprints, DNA evidence), testifying experts’ reliance In patent infringement cases, for example, machine on machine learning might invite initial skepticism. learning can be used to sort through reams of filings When using such a methodology, the expert will need to using natural language processing capabilities to reveal rigorously validate the chosen model and evaluate features common to desired outcomes. This information whether results are meaningful and sufficiently accurate can be combined with other data to approximate the (e.g., a model that accurately predicts an outcome 90 patent office processes leading to final judgments. Such percent of the time but has a high false positive rate predictions can help the parties decide whether to might not be appropriate). Testifying experts using these negotiate a settlement or engage in costly litigation. methods will also need to educate and convince the court of the validity of these less familiar models. Mining data more efficiently to strengthen arguments. Machine learning can make use of the vast amounts of If appropriate care is taken, widespread adoption of data in a company’s possession to conduct more machine learning may prove to be a significant advan- sophisticated analyses that support testimony or provide tage in the increasingly complex and technical world of counterfactual scenarios. Information that might once litigation. n New ABA Working Group to Explore Guiding Principles for Data Science Testifiers Analysis Group is pleased to announce the formation “big data” topic with many ill-defined roles compounded of a new working group within the American Bar by ever-changing standards and evolving technologies.