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March 29, 2023
Optimizing Review with Your Legal Team, AI, and a Tech-Forward Mindset
Lighthouse‚Äôs Mary Newman, Executive Director of Managed Review, joins the podcast to explore how adopting a technology-forward mindset can provide better results for document review teams., To keep up with the big data challenges in modern review, adopting a technology-enabled approach is critical. Modern technology like AI can help case teams defensibly cull datasets and gain unprecedented early insight into their data. But if downstream document review teams are unable to optimize technology within their workflows and review tasks, many of the early benefits gained by technology can quickly be lost. Lighthouse‚Äôs Mary Newman , Executive Director of Managed Review, joins the podcast to explore how document review teams that adopt a technology-forward mindset can provide better review results now and in the future. This episode's sighting of radical brilliance: An A.I. Pioneer on What We Should Really Fear , New York Times, December 21, 2022. If you enjoyed the show, learn more about our speakers and subscribe on lawandcandor.com , rate us wherever you get your podcasts, and join in the conversation on LinkedIn and Twitter . , ai-and-analytics; legal-operations; lighting-the-way-for-review; lighting-the-path-to-better-review; lighting-the-path-to-better-ediscovery, review, ai/big data, podcast, managed review, ai-and-analytics, legal-operations, review; ai-big-data; podcast; managed-review
December 15, 2022
Review Analytics for a New Era
AI & Analytics
Law & Candor welcomes Kara Ricupero, Associate General Counsel at eBay, for a conversation about how analytics and reimagining review can help solve data challenges and advance business imperatives., In episode two, we introduce our new co-host Paige Hunt , Vice President of Global Discovery Solutions at Lighthouse, who will be joining Bill Mariano as our guide through the legal technology revolution. In their first Sighting of Radical Brilliance together they chat about an article in Wired that explores the rise of the AI meme machine, DALL-E Mini . Then, Paige and Bill interview Kara Ricupero , Associate General Counsel and Head of Global Information Governance, eDiscovery, and Legal Analytics at eBay. They explore how a dynamic combination of new technology and human expertise is helping to usher in new approaches to review and analytics that can help tackle modern data challenges. Other questions they dive into, include: How did you identify the kind of advanced technology needed for modern data challenges? Partnering with the right people and experts across the business to utilize technology and insights seems to be a big part of the equation. How did you work with other stakeholders to leverage analytics? With new analytics and intelligence, has it changed how you approach review on matters or other processes? How do you think utilizing analytics will evolve as data and review continue to change? What kinds of problems do you think it can help solve? If you enjoyed the show, learn more about our speakers and subscribe on the podcast homepage , listen and rate the show wherever you get your podcasts, and join in the conversation on Twitter . , ai-and-analytics; ediscovery-review; lighting-the-way-for-review; lighting-the-path-to-better-review; lighting-the-path-to-better-ediscovery, review, data-re-use, ai/big data, podcast, ai-and-analytics, ediscovery-review, review; data-re-use; ai-big-data; podcast
November 16, 2021
eDiscovery Review: Family Vs. Four Corner
Pooja Lalwani of Lighthouse and our hosts discuss these two ediscovery review methodologies, and walk through the advantages and disadvantages of both and which better supports AI technology., Bill Mariano and Rob Hellewell kick off this episode with another segment of Sightings of Radical Brilliance, where they discuss Dalvin Brown‚Äôs piece in the Washington Post about how AI was used to recreate actor Val Kilmer‚Äôs voice . Bill and Rob consider this great scientific achievement along with the potentially nefarious ways it can used. Next, our hosts chat with Pooja Lalwani of Lighthouse about two key approaches to ediscovery review: family and four corner. Pooja helps break down the benefits and drawbacks of each through questions such as: What are some of the key differences between both approaches? With modern communication platforms and data creating a more dynamic and complex review process, what are some of the considerations for when and how to deploy family and four corner review? What review methodology is better suited to supporting TAR and AI tools? How do these review methodologies either help classify privilege more efficiently or potentially create limitations? Our co-hosts wrap up the episode with a few key takeaways. If you enjoyed the show, learn more about our speakers and subscribe on the podcast homepage , rate us on Apple and Stitcher , and join in the conversation on Twitter . , ediscovery-review; ai-and-analytics; lighting-the-way-for-review; lighting-the-path-to-better-review, privilege, review, ai/big data, tar/predictive coding, podcast, ediscovery-review, ai-and-analytics, privilege; review; ai-big-data; tar-predictive-coding; podcast
December 20, 2022
Why You Need a Specialized Key Document Search Team in Multi-District Litigation
Few things are more ominous to a company‚Äôs in-house counsel than the prospect of facing thousands of individual lawsuits across 30-40 jurisdictions, alongside various other companies in a multi-district litigation (MDL) proceeding. In-house teams can, of course, lean on the expertise of external law firms that have strong backgrounds in MDLs. However, even for experienced law firms, coordinating an individual company‚Äôs legal defense with other law firms and in-house counsel within a joint defense group (JDG) can be a Sisyphean task. But this coordination is integral to achieving the best possible outcome for each company, especially when it comes to identifying and sharing the documents that will drive the JDG‚Äôs litigation strategies. An MDL can involve millions of documents, emanating from multiple companies and their subsidiaries. Buried somewhere within that complicated web of data is a small number of key documents that tell the story of what actually happened‚Äîthe documents that explain the ‚Äúwho, what, where, and when‚Äù of the litigation. Identifying those documents is critical so that JDG counsel can understand the role each company played (or didn‚Äôt play) in the plaintiffs‚Äô allegations, and then craft and prepare their defense accordingly. And the faster those documents are identified and shared across a JDG, the better and more effective that defense strategy and preparation will be. In short: A strong and coordinated key document search strategy that is specific to the unique ecosystem of an MDL is crucial for an effective defense. Ineffective search strategies leave litigators out at sea Unfortunately, outdated or ineffective search methodologies are often still the norm rather than the exception. The two most common strategies were created to find key documents in smaller, insular litigation proceedings involving one company. They are also relics of a time when average data volumes involved in litigation were much smaller. Those two strategies are: one, relying on linear document review teams to surface key documents as they review documents one by one in preparation for production, and, two, relying on attorneys from the JDG‚Äôs counsel teams to arbitrarily search datasets using whatever search terms they think may be effective. Let‚Äôs take a deeper look at each of these methodologies and why they are both ineffective and expensive: Relying on linear review teams to find key documents. Traditional linear review teams are often made up of dozens or even hundreds of contract attorneys with no coordination around key document searches and little or no day-to-day communication with JDG counsel. Each attorney reviewer may also only see a tiny fraction of the entire dataset and have a skewed view of what documents are truly important to the JDG‚Äôs strategy. The results are often both overinclusive (with thousands of routine documents labeled ‚Äúkey‚Äù or ‚Äúhot‚Äù that JDG counsel must wade through) and underinclusive (with truly important documents left unflagged and unnoticed by review teams). This search method is also painfully slow. Key documents are only incidentally surfaced by the review team if they notice them while performing their primary responsibility‚Äîresponsive review. Relying on attorneys from JDG counsel teams. Relying on individual attorneys from the JDG‚Äôs outside counsel to perform keyword searches to find key documents is also ineffective and wastefully expensive. Without a very specific, coordinated search plan, attorneys are left running whatever searches each thinks might be effective. This strategy inevitably will risk plaintiffs finding critical documents first, leaving defense deposition witnesses unprepared and susceptible to ambush. This search methodology is also a dysfunctional use of attorney time and legal spend. Merits counsel‚Äôs value is their legal analytic skillset‚Äîi.e., their ability to craft the best litigation strategy with the evidence at hand. Most attorneys are not technologists or linguistic experts. Asking highly skilled attorneys to craft the most effective technological and linguistic data search is a bit like asking an award-winning sushi chef to jump onboard a fishing vessel, navigate to the best fishing spot, select the best bait, and reel in the fish the chef will ultimately serve. Both jobs require a highly specialized skillset and are essential to the end goal of delighting a client with an excellent meal. But paying the chef to perform the fisherman‚Äôs job would be ineffective and a waste of the chef‚Äôs skillset and time. Both of these search strategies are also reactive rather than proactive, which drives up legal costs, wastes valuable resources, and worsens outcomes for each company in a JDG. A better approach to MDL preparation and strategy Fortunately, there is a more proactive, cost-efficient, holistic, and effective way to identify the key documents in an MDL environment. It involves engaging a small team of highly trained linguists and technology search experts, who can leverage purpose-built technology to find the best documents to prepare effective litigation strategies across the entire MDL data landscape. A specialized team with this makeup provides a number of key advantages: Precise searches and results‚ÄîLinguistic experts can carefully craft narrow searches that consider the nuance of human language to more effectively find key documents. A specialized search team can also employ thematic search strategies across every jurisdiction. This provides counsel with a critical high-level overview of the evidence that lies within the data for each litigation, enabling each company to make better, more informed decisions much earlier in the process.Quick access to key documents‚ÄîTechnology experts leveraging advanced AI and analytics can ensure potentially damaging documents bubble up to the surface‚Äîeven in the absence of specific requests from JDG counsel. Compare this to waiting for those documents to be found by contract attorneys as they review an endless stream of documents, one by one, during the linear review process. A flexible offensive and defensive litigation strategy‚ÄîA team of this size and composition can react more nimbly, circulate information faster, and respond quicker to changes in litigation strategy. For example, once counsel has an overview of the important facts, the search team can begin to narrow their focus to arm counsel with the data needed for both offensive and defensive litigation strategies. The team will be incredibly adept at analyzing incoming data provided by opposing counsel‚Äîflagging any gaps and raising potential deposition targets. Defensively, they can be used by counsel to get ahead of any potentially damaging evidence and identify every document that bolsters potential defense arguments. An expert partner throughout the process‚ÄîA centralized search team is able to act as a coordinated ‚Äúsearch desk‚Äù for all involved counsel, as well as a repository and ‚Äúsource of truth‚Äù for institutional knowledge across every jurisdiction. As litigation progresses, the search team becomes the right hand of counsel‚Äîusing their knowledge and expertise to prepare deposition and witness preparation binders and performing ad-hoc searches for counsel. Once a matter goes to trial in one jurisdiction, the search team can use the information gleaned from that proceeding to inform their searches and strategy for the next case. Conclusion Facing a complex MDL is an undoubtedly daunting process for any company. But successfully navigating this challenge will be downright impossible if counsel is unable to quickly find and understand the key facts and issues that lie buried within massive volumes of data. Traditional key document search methodologies are no longer effective at providing that information to counsel. For a better outcome, companies should look for small, specialized search teams, made up of linguistic and technology experts. These teams will be able to build a scalable and effective search strategy tailormade for the unique data ecosystem of a large MDL‚Äîthereby proactively providing counsel with the evidence needed to achieve the best possible outcome for each company. lighting-the-way-for-review; ai-and-analytics; ediscovery-review; lighting-the-path-to-better-review; lighting-the-path-to-better-ediscoveryreview, blog, ai, ai-and-analytics, ediscovery-reviewreview; blog; aikdisarah moran
January 13, 2022
Purchasing AI for eDiscovery: Tips and Best Practices
AI & Analytics
eDiscovery is currently undergoing a fundamental sea change, including how we think about data governance and the EDRM. Linear review and older analytic tools are quickly becoming outdated and unable to handle modern datasets, i.e., eDiscovery datasets that are not only more voluminous than ever before, but also more complicated ‚Äì emanating from an ever-evolving list of new data sources and steeped in variety of text and non-text-based languages (foreign language, slang, emojis, video, etc.).Fortunately, technological advancements in AI have led to a new class of eDiscovery tools that are purpose built to handle ‚Äúbig data.‚Äù These tools can more accurately identify and classify responsiveness, privileged, and sensitive information, parse multiple formats, and even provide attorneys with data insights gleaned from an organization‚Äôs entire legal portfolio.This is great news for legal practitioners who are faced with reviewing and analyzing these more challenging datasets. However, evaluating and selecting the right AI technology can still present its own unique hurdles and complexities. The intense purchasing process can raise questions like: Is all AI the same? If not, what is the difference between AI-based tools? What features are right for my organization or firm? And once I‚Äôve found a tool I like, how do I make the case for purchasing it to my firm or organization?These are all tough questions and can lead you down a rabbit hole of research and never-ending discussions with technology and eDiscovery vendors. However, the right preparation can make a world of difference. Leveraging the below steps will help you simplify the process, obtain answers to your fundamental questions, and ultimately select the right technology that will help you overcome your eDiscovery challenges and up level your eDiscovery program.1. Familiarize Yourself with Subsets of AI in eDiscoveryNewer AI technology is significantly better at tackling today‚Äôs modern eDiscovery datasets than legacy technology. It can also provide legal teams with previously unheard-of data insights, improving efficiency and accuracy while enabling more data-driven strategic decisions. However, not all technology is the same ‚Äì even if technology providers tend to generally refer to it all as ‚ÄúAI.‚Äù There are many different subsets of AI technology, and each may have vastly different capabilities and benefits. It‚Äôs important to understand what subsets of AI can provide the benefits you‚Äôre looking for, and how those different technology subsets can work together. For example, Natural Language Processing (NLP) enables an AI-based tool to understand text the same way that humans understand it ‚Äì thus providing much more accurate classifications results ‚Äì while AI tools that leverage deep learning technology together with NLP are better able to handle large and complex datasets more efficiently and accurately. Other subsets of AI give tools the ability to re-use data across matters as well as across entire legal portfolios. Learning more about each subset and the capability and benefits they can provide before talking to eDiscovery vendors will give you the knowledge base necessary to narrow down the tools that will meet your specific needs. 2. Learn How to Measure AI ROIAs a partner to human reviewers, advanced AI tools can provide a powerful return on investment (ROI). Understanding how to measure this ROI will enable you to ask the right questions during the purchasing process to ensure that you select a tool that aligns with your organization or law firm‚Äôs priorities. For example, if your team struggles with review accuracy when utilizing your current tools and workflows, you‚Äôll want to ensure that the tool you purchase is quantifiably more accurate at classifying documents for responsiveness, privilege, sensitive information, etc. The same will be true for other ROI metrics that are important to your team, such as lower overall eDiscovery spend or increased review efficiency.These metrics will also help you build a strong business case to purchase your chosen tool once you‚Äôve selected it, as well as a verifiable way to confirm the tool is performing the way you want it to after purchase.3. Come Prepared with a List of QuestionsIt‚Äôs easy to get swept up in conversations about tools and solutions that end without the metrics you need. A simple way to control the conversation and ensure you walk away with the information you need is to prepare a thorough list of questions that reflect your priorities. Also be sure to have a method to record each vendor‚Äôs response to your questions. A list of standard questions will keep conversations more productive and provide a way to easily contrast and compare the technology you‚Äôre evaluating. Ensure that you also ask for quantifiable metrics and examples to back up responses, as well as references from clients. This will help you verify that vendor responses are backed by data and evidence.4. Know the Pitfalls of AI Adoption‚Äîand How to Avoid ThemIt won‚Äôt matter how much you understand AI capabilities, whether you‚Äôve asked the right questions, or whether you understand how to measure ROI, if you don‚Äôt know how to avoid common AI pitfalls. Even the best technology will fail to return the desired results if it‚Äôs not implemented properly or effectively. For example, there are some workflows that work best with advanced AI, while other workflows may fail to return the best results possible. Knowing this type of information ahead of time will help you get your team on board early, ensure a smooth implementation, and enable you to unlock the full potential of the technology.These tips will help you better prepare for the AI purchasing process. For more information, be sure to download our guide to buying AI. This comprehensive guide offers a deep dive into tips and tactics that will help you fully evaluate potential eDiscovery AI tools to ensure you select the best tool for your needs. The guide can also be used to reevaluate your current AI and analytic eDiscovery tools to confirm you‚Äôre using the best available technology to meet today‚Äôs eDiscovery challenges.lighting-the-way-for-review; ai-and-analytics; ediscovery-review; lighting-the-path-to-better-review; lighting-the-path-to-better-ediscoveryreview, ai-big-data, blog, ai-and-analytics, ediscovery-reviewreview; ai-big-data; blogai-analyticssarah moran
April 12, 2023
Is 2023 the Tipping Point for AI Adoption in Legal?
Generative AI. Bard. Bing AI. Large language models. Artificial intelligence continues to dominate headlines and workplace chats across every industry since OpenAI‚Äôs public release of ChatGPT in November of 2022. Nowhere was this more evident than at this year‚Äôs Legalweek event. The annual conference, which gathers thousands of attorneys, legal practitioners, and eDiscovery providers together in New York City, was dominated by discussions of ChatGPT and AI. This makes sense, of course. Attorneys must understand how major technology shifts will impact their clients or companies‚Äîespecially those in eDiscovery and information governance who deal with corporate data and its challenges. But there was a slight twist to the discussions about ChatGPT. In addition to the possible impacts and risks to clients who use the technology, there was just as much, if not more, focus on how it could be used to streamline eDiscovery.The idea of using a tool released to the public less than four months ago seems almost ironic in an industry with a reputation for slowly adopting technology. Indeed, a 2022 ABA survey showed that as few as 19.2% of lawyers use predictive coding technology (i.e., technology assisted review or TAR) for document review, up from just 12% in 2018. Even surveys dominated by eDiscovery software providers showed TAR was being used on less than 30% of matters in 2022. Given that the technology behind traditional TAR tools has existed since the 1970s and the use of TAR has been widely accepted (and even encouraged) by court systems around the world for over a decade, these statistics are strikingly low.So, what is driving this recent enthusiasm in the industry around AI? The accessibility and generative results of ChatGPT is certainly a factor. After all, even a child can quickly learn how to use ChatGPT to generate new content from a simple query. But the recent excitement in eDiscovery also seems to be driven by the significant challenges attorneys are encountering:Macroeconomic volatility and unpredictability have been a near constant stressor for both corporate legal departments and law firms. Legal budgets are shrinking, and layoffs have plagued almost every industry, leaving legal teams to do the same volume of work with fewer resources.Corporate legal teams are being pressured to evolve from a cost center to one that generates revenue and savings, while attorneys at law firms are expected to add value and expertise to all sectors of a company‚Äôs business, beyond the litigation and legal sectors they‚Äôve traditionally operated in. And all attorneys are facing increasing demand to become experts in the risks and challenges of the ever-evolving list of new technology used by their clients and companies.New technology is generating unprecedented volumes of corporate data in new formats, while eDiscovery teams are still grappling with better ways to collect, review, and produce older data formats (modern attachments, collaboration data, text messages, etc.) In short, even the most technology-shy attorneys may be finding themselves at a technology ‚Äútipping point,‚Äù realizing that it is impossible to overcome some of these challenges without leveraging AI and other forms of advanced technology. But while the challenges may seem grim, there is an inherent hopefulness in this moment. The legal industry‚Äôs tendency to adopt AI technology more slowly than other sectors means there‚Äôs ample opportunity for growth. Some forward-thinking legal teams, with the help of eDiscovery providers, have already been leveraging advanced AI technology to substantially increase the efficiency and accuracy of eDiscovery workflows. This includes tools that utilize the technology behind ChatGPT, including large language models and natural language processing (NLP). And unlike ChatGPT, where privacy concerns have already been flagged regarding its use, existing AI solutions for eDiscovery were developed specifically to meet the stricter requirements of the legal industry‚Äîwith some already overcoming tough scrutiny from regulators, opposing counsel, and courts. In other words, the big eDiscovery question of 2023 may not be, ‚ÄúCan ChatGPT revolutionize eDiscovery in the future?‚Äù but rather, ‚ÄúWhat can advanced AI and analytic tools do for eDiscovery practitioners right now?‚Äù While the former is up for debate, there are definitive answers to the latter threaded throughout many of the other major industry discussions happening now. Some of those discussions include:If you want to go far, go together Today‚Äôs larger and more complicated data volumes often make the traditional eDiscovery model feel like the proverbial round hole that the square (data) peg was not designed to fit into. And it‚Äôs becoming increasingly expensive for legal teams to try to do so. To operate in this new era, it‚Äôs essential to work with partners who can help you meet your data needs and align with your goals. A good example is when an in-house legal team partners with a technology-forward law firm and eDiscovery provider to build a more streamlined and modern eDiscovery program. This kind of partnership provides the resources, expertise, and technology needed to take a more holistic approach to eDiscovery‚Äîbreaking away from the traditional model of starting each new matter from scratch. These teams can work together to create and deploy tools and expertise that reduce costs and improve review outcomes across all matters. For example, customized AI classifiers built with data and work product from the company‚Äôs past matters, cross-matter analytics that identify review and data trends, and tailored review workflows to increase efficiency and accuracy for specific use cases. This partnership approach is a microcosm of how different organizations and teams can work together to overcome common industry challenges. Technology that meets us where we areDespite all the chatter around ChatGPT, there is currently no ‚Äúeasy AI button‚Äù to automate the document review process. However, modern eDiscovery technology (including advanced AI) can be integrated into almost every stage of the document review process in different ways, depending on a case team‚Äôs goals. This technology-integrated approach to eDiscovery workflows can help case teams achieve unprecedented efficiency and review accuracy, mitigate risks of inadvertently producing sensitive documents, minimize review redundancy across matters, and quickly pull out key themes, timelines, and documents hidden within large data volumes. Technology-forward law firms and managed review partners can help case teams integrate advanced technology and specialized expertise to achieve these goals in a defensible way that works with each company‚Äôs existing data and workflows. The only constant is change The days of a static, rarely updated information governance program are gone. The nature of cloud data, the speed of technology evolution and adoption, and the increasingly complex patchwork of data privacy and security regulations mean that legal and compliance teams need to be nimble and ready for the next new data challenge. New generative AI tools like ChatGPT may only add to this complexity. While this type of technology may be largely off limits in the near future for eDiscovery providers and law firms due to client confidentiality, data privacy, and AI transparency issues, companies in other industries have already begun using it. Legal and compliance teams will need to ensure that any new data created by generative AI tools follow applicable data retention guidelines and regulations and begin to think through how this new data will impact eDiscovery workflows.The furor and excitement over the potential use cases for ChatGPT in eDiscovery are a hopeful sign that more legal practitioners are realizing the potential of AI and advanced analytic technology. This change will help push the industry forward, as more in-house teams, outside counsel, and eDiscovery providers partner together to overcome some of the industry‚Äôs toughest data challenges with advanced technology.For other stories on practical applications of AI and analytics in eDiscovery, check out more Lighthouse content. lighting-the-way-for-review; ai-and-analytics; lighting-the-path-to-better-reviewai-big-data, blog, ai-and-analytics,ai-big-data; blogsarah moran
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