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Caleb Lewis
Caleb Lewis

Big Data Law

Cooley lawyers work with our clients every day to address issues and mitigate risks for companies in the big data and data analytics sectors. We help our clients through a number of our dedicated practice areas, from corporate formation and capital financings to intellectual property and licensing.

Big data law

In this Article we critically examine the use of Big Data in the legal system. Big Data is driving a trend towards behavioral optimization and "personalized law," in which legal decisions and rules are optimized for best outcomes and where law is tailored to individual consumers based on analysis of past data. Big Data, however, has serious limitations and dangers when applied in the legal context. Advocates of Big Data make theoretically problematic assumptions about the objectivity of data and scientific observation. Law is always theory-laden. Although Big Data strives to be objective, law and data have multiple possible meanings and uses and thus require theory and interpretation in order to be applied. Further, the meanings and uses of law and data are indefinite and continually evolving in ways that cannot be captured or predicted by Big Data.

Whom does big data exclude? What are the consequences of exclusion for them, for big data as a technology, and for societies? These are underexplored questions that deserve more attention than they receive in current debates over big data. And because these technologies pose unique dangers to equality, and not just privacy, a new legal doctrine may be needed to protect those persons whom the big data revolution risks sidelining. I call it data antisubordination.

First, those left out of the big data revolution may suffer tangible economic harms. Businesses may ignore or undervalue the preferences and behaviors of consumers who do not shop in ways that big data tools can easily capture, aggregate, and analyze. Stores may not open in their neighborhoods, denying them not just shopping options, but also employment opportunities; certain promotions may not be offered to them; new products may not be designed to meet their needs, or priced to meet their budgets. Of course, poor people and minority groups are in many ways already marginalized in the marketplace. But big data could reinforce and exacerbate existing problems.

AI works with Big Data to accomplish several different outcomes. For example, AI can use Big Data to recognize, categorize, and find relationships from the data.[5] AI can also work with Big Data to adapt to patterns and identify opportunities so that the data can be understood and put into context. For organizations looking to improve efficiency and effectiveness, AI can leverage Big Data to predict the impact of various decisions. In fact, AI can work with algorithms to suggest actions before they have been deployed, assess risk, and provide feedback in real time from the Big Data pools. When AI works with Big Data and biometrics, AI can perform various types of human recognition for applications in every industry.[6] In other words, the more data AI can process, the more it can learn. Thus, the two rely on each other in order to keep pushing the bounds of technological innovation and machine learning and development.

As concerns grow about the privacy and security of data used in AI, there is currently no federal privacy law in the United States. Senators Jeff Merkley and Bernie Sanders proposed the National Biometric Information Privacy Act in 2020, which was not passed into law; it contained provisions such as requiring consent from individuals before collecting information, providing a private right of action for violations, and imposing an obligation to safeguard the identifying information.[8] The act also required private entities to draft public policies and implement mechanisms for destroying information, limit collection of information to valid business reasons, inform individuals that their information is stored, and obtain written releases before disclosure.

Medical research is increasingly becoming data-intensive; sensitive data are being re-used, linked and analysed on an unprecedented scale. The current EU data protection law reform has led to an intense debate about its potential effect on this processing of data in medical research. To contribute to this evolving debate, this paper reviews how the dominant 'consent or anonymise approach' is challenged in a data-intensive medical research context, and discusses possible ways forwards within the EU legal framework on data protection. A large part of the debate in literature focuses on the acceptability of adapting consent or anonymisation mechanisms to overcome the challenges within these approaches. We however believe that the search for ways forward within the consent or anonymise paradigm will become increasingly difficult. Therefore, we underline the necessity of an appropriate research exemption from consent for the use of sensitive personal data in medical research to take account of all legitimate interests. The appropriate conditions of such a research exemption are however subject to debate, and we expect that there will be minimal harmonisation of these conditions in the forthcoming EU Data Protection Regulation. Further deliberation is required to determine when a shift away from consent as a legal basis is necessary and proportional in a data-intensive medical research context, and what safeguards should be put in place when such a research exemption from consent is provided.

Across every industry, big data is being used to guide and develop better decision-making and business insights. However, its use in the legal industry is relatively new, mainly due to skeptics in the industry and willingness to spend on new technology being quite low.

Funnily enough, most law firms believe that big data and its assistance in decision making is important especially when it comes to managing tasks like client terms. 90% of firms believe that big data is important, but only 16% are using it. This presents a gap in the market where costs to implement analytics technology are falling and data collection technologies are becoming more effective. It is now more accessible for law firms to improve their business model, exceed client expectations and increase profitability.

As with many technological advances, the legal industry has been slow to keep up compared with others. This is partly down to the fact that attorneys tend to be risk-averse, and partly to do with the nature of legal data. As long as the legal practice has been around modern technology simply adds more practicality. In the past, lawyers would spend hours studying books of court records to painstakingly qualify every relevant case for a client.

Such research tactics will largely lead the show as big data technology tends to become cheaper and more widely popular across the market. In the near future, big data is going to be applied in a plethora of industry verticals and we are quite excited to witness impactful results.

When you think of the legal industry you think of money, overtime, and lots of paperwork. Big data can play a role in case of management for lawyers. Cases are filed every day in South Africa and each case requires a mountain of paperwork behind it. Big data can help organize this and improve the customer journey when dealing with the firm. In addition to this, Big Data is helping law firms with time management and billing. This includes a deeper understanding of revenue streams, the most profitable case types and teams better suited to cases to ensure maximum output.

In reports from the White House and the Federal Trade Commission, the federal government itself has recognized the potential danger for big data algorithms to reinforce racial, gender, and other disparities.

Approximately 64 million consumers in the United States have no credit history or lack sufficient credit history to generate a credit score, cutting off access to traditional banking services. Finding a way of getting affordable access to credit is of vital importance to the economic well-being of this population. It also represents an untapped market with the potential for big profits. So it is unsurprising that in this era of big data, information culled from Internet searches, social media, and mobile apps would be put to usetowards that goal. However, it is unclear as to whether doing so will be beneficial for the low-income consumer. These products may fill a void and provide affordable access to credit to these underserved populations or they may be a means of preying on vulnerable communities.

Law enforcement agencies gather big data for an investigation by bringing in more and different sources of data, both structured and unstructured. The amount of data and the process of sifting through it can be overwhelming, so the use of technology is imperative. Agencies can use data fusion to enable entity resolution from disconnected data sources and leverage artificial intelligence and machine learning to enrich unstructured data for more effective, automated analytics that improve decision intelligence. AI and ML can also surface previously hidden relationships and patterns between individuals and/or entities; however, these applications are only part of the solution.

To overcome failure of imagination and failure of conception in investigations, it is wise to apply proven decision intelligence methodologies that can help tackle problems and make sense of data and analytics insights. Here are a few examples:

The applications of these methodologies are rooted in military intelligence; however, they can and should be applied in law enforcement as well. They can be used in many domains, including financial investigations, customs cases, cybercrime investigations and more. Why? Because these decision intelligence methodologies help analysts realign how they view the data. These methodologies should be used in combination with big data analysis, AI and machine learning to ensure the right results. 350c69d7ab


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