Presenters: Nicolas Economou (Director, The Future Society, CEO, H5, Co-chair, Law Committee, IEEE Global Initiative), Jiyu Zhang (Associate Professor, Renmin University), Lee Tiedrich (AI Initiative Co-Chair, Covington & Burling), Jia Gu (Researcher, Legal Intelligence in China), Robert Silvers (Partner, Paul Hastings, Former Assistant Secretary for Cybersecurity at U.S. Department of Homeland Security), Mike Philips (Associate General Counsel, Microsoft), Deepa Krishna (Director of Business Development, ClearAccessIP) To read the full report click here for the digital edition. AI has unrelentingly ascended into American and Chinese legal systems. As citizens’ liberty, wellbeing, privacy and rights depend on the strength of a nation’s judiciary, many apprehend AI’s increasingly prominent role and its implications for due process. Today, the rule of law by humans and for humans, which Nicolas Economou characterized as the foundation of civilized society, is increasingly challenged, as decisions that affect humans are progressively surrendered to machines in the world’s legal systems. While AI has significant potential to better society and the administration of justice, its deployment must follow norms that will ensure lawyers, courts, other institutions of state, and civil society at large can trust it. The “AI in the Law” panelists shared perspectives on how AI can benefit American and Chinese justice systems while mitigating its risks. Noting that technology has the potential to improve access to and the quality of justice, the speakers’ collective perspectives highlighted the need for artificial intelligence systems, in order to be trusted in the legal system, to be appropriately transparent, effective at meeting the specific purpose for which they are intended, competently operated, and accountable. It is important that AI systems (and their operators) deployed in support of the administration of justice and enforcement of the law remain under the ultimate supervision and control of legal practitioners and courts. KEY TAKEAWAYS China’s judiciary system applies AI to improve efficiency and fairness. China faces both efficiency and fairness challenges. The recent round of legal reforms lowered the number of practicing judges in China by a third in the face of a 30 percent increases in the amount of legal cases sent to Chinese courts annually. At the same time, recent studies show that judges in different geographical regions within China make dissimilar judgements for similar cases. As there is a significant mismatch between the number of judges and litigations and also uneven judgements made across the nation, the Supreme People’s Court (SPC) has taken a proactive role in introducing AI into China’s judicial system. To overcome these mismatches, the Supreme People’s Procuratorate and SPC are developing intelligent courts and intelligent procuratorates. Technology can streamline tedious court processes. Chinese judges are overwhelmed by the large amount of litigations they confront, with Beijing appellate court judges concluding only a third of their presented cases each year. To improve this statistic, Jiyu Zhang notes that the SPC is introducing automated technologies to complete cumbersome tasks. Specifically, dictating technologies intended to transform court speeches into text will improve efficiency. Additionally, automated technologies that not only correct errors in judicial documents, but also generate parts of the documents themselves will allow judges to spend their time doing more meaningful work. Artificial intelligence can provide judges with more information. Specifically, the SPC hopes to apply AI’s predictive and research capabilities to intelligent courts. By predicting the number of different kinds of cases, judges and court staff can better allocate resources. Also, improving court search capabilities and implementing an AI-fueled profiling system will not only make judicial processes more efficient, but also provide judges in different regions with similar information to make more consistent judgements. Legal profiling systems, Jia Gu notes, reduce information gaps and help judges find similar cases with similar arguments. This technology then assists legal practitioners to sift through large quantities of data. Though these types of technologies are highly contested, they allow judges to search similar cases electronically, provide sentencing guidelines and help complete risk evaluations. Attention to data can overcome biases. Freedom from bias is essential to justice systems. The U.S. in particular questions to what extent AI applications need to reduce bias in the outputs they produce. Lee Tiedrich notes that as technology continues to advance, communities must comprehensively analyze AI’s impacts on legal bias issues. Since bias is not only morally reprehensible, as Robert Silvers states, but illegal under fair employment, housing and lending laws, robust protections need to be implemented to ensure that AI does not replicate or exacerbate existing biases. As AI becomes more prominent in legal systems, data is vital to containing and detecting bias. Legal data and information need to be accessible. To enable citizens to participate in increasingly AI-enabled legal systems and to be fully aware of what AI systems are doing, as Mike Philips highlighted, individuals need access to adequate data and information within their legal systems. China’s SPC published all of its judgements since 2013 (approximately 48 million cases) online, giving inclusive access to case conclusions. The SPC also opened a trial network with access to court proceeding videos. As data barriers are torn down and individuals play more involved roles in the legal system, knowing AI’s data inputs will help determine whether outputs are biased. Algorithms are only as good as their data. As AI enters the courtroom, defendants are increasingly assessed by algorithms (for example for bail decisions). In the event that data is undesirably biased, the accompanying algorithm is likely to be adversely affected, producing erroneous outcomes. In addition, as raw data must often be labeled by subject matter experts, undesirable human bias can be introduced. This risk underscores the need for great caution in determining the norms under which courts and judges should feel confident in relying on the assessments made by artificial intelligence. Qualified experts should operate AI and interpret the data. AI can be used in the service of the law, but it is a scientific discipline distinct from the law, that requires specific expertise in order to be competently operated. It is important that AI operators, as well as data labelers, have the right domain expertise. As noted, AI requires data labeling, which should be entrusted to those qualified to do so. Additionally, algorithms involved in legal decision-making require expert human operation and interpretation to make AI measurably effective and its findings understandable. Thus, experts with the appropriate scientific, technical, and subject matter competencies play a key role in ensuring the effective and safe operation of artificial intelligence systems in the law. Artificial intelligence systems complicate liability risks. Companies developing AI systems must account for the technology’s accompanying risks and liabilities. Silvers links liability and regulatory exposure to companies’ AI-centered business strategies. As AI-driven products can adversely affect people and cause individuals to come forward saying artificial intelligence injured or disadvantaged them, with State v. Loomis serving as an example, companies need to fortify themselves against such risks via contracts and procedures. AI shifts liability to companies. Currently, more than 90 percent of car accidents are caused by human error. In this context, individuals are often held legally accountable. But as automated vehicles surface and societies come to rely on AI systems, humans adopt more passive roles, raising questions of how responsibility for accidents should be apportioned. In the context of driverless cars and other entirely automated technologies, liability may be shifted from humans to technology. However, there are underlying convolutions and complexities. While liability shift to companies, Silvers questions “which companies?” As AI technologies have extensive value chains, comprised of OEMs, sensor manufacturers, infotainment companies, operating system manufacturers and others—a single product such as an autonomous vehicle may produce a vastly distributed network of liability. Companies need to develop protection and containment architecture. In order for companies to enjoy the benefits of AI while monetizing its technologies, companies should build an “architecture of protection and containment.” Simultaneously, contracts play an important role in shifting liability on to other companies in the AI ecosystem. As artificial intelligence grows, not only is the legal system more automated, but judiciaries struggle with more difficult decisions in courts. The lack of different domains to account for AI in the judicial system causes uncertainty, but also enables creativity since there are not many mandating rules. Societal values of transparency in decision-making and of protection of intellectual property must be balanced. A salient challenge that the increased reliance on artificial intelligence produces for the law is the balancing of transparency with intellectual property rights. To illustrate this challenge, panelists cited The Loomis matter, a case in Wisconsin where a man received a lengthy sentence, in part on account of an algorithm that assessed him to be at high risk of recidivism. The court denied the defendant’s request to examine the underlying data and algorithms, in part on intellectual property grounds, a decision that was affirmed on appeal by the Wisconsin Supreme Court. Whereas the balancing of many complex societal values and case-specific information led to these decisions, they nonetheless leave open the fundamental question associated with the deployment of AI in the legal system: on what grounds should society trust that such systems are effective for the purpose for which they are used? In common parlance: “Does it work? And how do we know?” Providing sufficient information to the institutions of state and to the general public in order to enable adequately informed action is an important principle. At the same time, society must protect entrepreneurship and innovation. Balancing these two considerations with complementary conceptual instruments, including ensuring evidence of effectiveness of AI and accountability for its operators is an important topic for further exploration. The legal community must conscientiously harness AI’s benefits in the future. Law practitioners need to do their research to ensure that AI is deployed in a manner that improves efficiency and is consistent with their profession’s ideals and obligations. Gu highlights China’s research to overcome transparency, efficiency, privacy and fairness issues in its legal system by analyzing the Loomis case, ProPublica’s machine bias article and IEEE’s Global Initiative reports on automated and intelligent systems, amongst others. Understanding changing privacy frameworks in California and Europe, as well as past AI legal cases and the technologies themselves, will better equip legal systems to benefit most from AI. AI usage should align with lawyers’ professional ethics obligations. Lawyers are subject to ethical obligations. In the United States in particular, attorneys have duties of competence, confidentiality and a duty to honestly represent clients under the rule of law. When deciding how to use AI tools in their legal practice, lawyers need to do their due diligence in understanding the AI tools available and their accompanying strengths and limitations, as well as the specific competence needed to operate such systems safely and effectively. In addition to choosing the correct AI technologies, legal practitioners must make informed decisions when integrating such systems into practice. The ultimate responsibility for the incorporation and effective operation of AI systems used in the provision of legal services rests with lawyers, not AI systems. Businesses and lawyers must adapt to new privacy and security frameworks. As privacy laws proliferate globally, the large supply of data that fuels AI brings potentially serious privacy and security considerations. Though many fear that these laws will prevent innovative data usage, companies can still monetize data and use it to fuel machine learning and other AI applications. However, they must be more conscientious in how they operate within the legal framework. Specifically, companies need to adopt a set of consumer notices, obtain consent to use customer data, have certain opt-ins and -outs rights and give individuals the right to erasure. In regards to data security, companies need to prevent “AI poisoning” related to cyber-attacks that impair data pools’ integrity. As data privacy and security are at the forefront of legal AI discussions, critical information systems need strong cyber protections and companies must learn to operate in a growing legal framework that prioritizes privacy.

But it’s difficult to think about value when we have no buoy for understanding it outside our traditional lenses: for example, our time, our job, and what others tell us they are worth in cash. This, largely, is the world’s paradigm for value so far. But understanding what value really means changes everything—and will be at the center of the decentralized revolution in global coordination that will unfold over the next decade. So, where do we begin?

Let’s start with gold.

Gold is an inherent value. When backing a market, gold allows us to grow a balanced economy well into the trillions. But why does it allow for massive stable markets to form around it? It is gold's permanence that creates stability. We understand that gold will always have value, because it is inherent in all of us, not just in one part of the world, but everywhere, not just today, but tomorrow and for the long haul.

In the 1930s when the gold standard was removed, we learned that the U.S. dollar didn’t need gold to back its economy to flourish. We learned that it was just a symbol for U.S. citizens to decentralize their coordination around the United States economy.

It turns out, common agreement is a philosophy for building shared economy.

And so it seems inherent value is a marker for us to begin exploring what the future could look like—a future beyond gold and the existing realm of credit. And so what else has inherent value? Is education as valuable as gold? What about healthcare? What about a vote that can’t be tampered with? What about an ID that can’t be stolen or erased? What about access to nutrition or clean water? You will find value everywhere you look.

It turns out, we’ve already done the legwork necessary to uncover the most elemental inherent values: The Sustainable Development Goals are commitments grown out of the drive to bring to life basic tenets of the Universal Declaration of Human Rights—the closest possible social contract we have to a global, common agreement.

We’ve already agreed, as a global community, to ensure inclusive and equitable access to quality education. We’ve already agreed to empower all women and girls, to ensure pure and clean water access for all, to promote health at all stages of life, and to end hunger.

We’ve already agreed.

Our agreements are grounded in deep value centers that are globally shared, but undervalued and unfulfilled. The reason for this is our inability to quantify intangible value. All of these rich, inherent values are still nebulous and fragmented in implementation—largely existing as ideals and blueprints for deep, globally shared common agreement. That is, we all agree education, health, and equality have value, but we lack common units for understanding who and who is not contributing value—leaving us to fumble in our own, uncoordinated siloes as we chase the phantoms of impact. In essence, we lack common currencies for our common agreements.

Now we find ourselves at the nexus of the real paradigm of Blockchain, allowing us to fuse economics with inherent value by proving the participation of some great human effort, then quantifying the impact of that effort in unforgeable and decentralized ledgers. It allows us to build economic models for tomorrow, that create wholly new markets and economies for and around each of the richest of human endeavors.

In late 2017 at the height of the Bitcoin bubble, without individual coordination, planning, or the help of institutions, almost $1 trillion was infused into blockchain markets. This is remarkable, and the revolution has only just begun. When you realize that Blockchain is in a similar stage of development as the internet pre-AOL, you will see a glimpse of the global transformation to come.

Only twice in the information age have we had such a paradigm shift in global infrastructure reform—the computer and the internet. While the computer taught us how to store and process data, the Internet built off that ability and furthered the conversation by teaching us how to transfer that information. Blockchain takes another massive step forward—it builds off the internet, adding to the story of information storage and transfer—but, it teaches us a new, priceless and not yet understood skill: how to transfer value.

This third wave kicked off with a rough start—as happens with the birth of new technologies and their corresponding liberties. Blockchain has, thus far, been totally unregulated. Many, doubtless, have taken advantage. A young child, stretching their arms for the first couple times might knock over a cookie jar or two. Eventually, however, they learn to use their faculties—for evil or for good. As such, while it’s wise to be skeptical at this phase in blockchain’s evolution, it’s important not to be blind to its remarkable implications in a post-regulated world, so that we may wield its faculties like a surgeon’s scalpel—not for evil or snake-oil sales, but for the creation of more good, for the flourishing of commonwealth.

But what of the volatility in blockchain markets? People agree Bitcoin has value, but they don’t understand why they are in agreement, and so cryptomarkets fluctuate violently.  Stable blockchain economies will require new symbolic gold standards that clearly articulate why someone would agree to support each market, to anchor common agreement with stability. The more globally shared these new value standards, the better.

Is education more valuable than gold? What about healthcare or nutrition or clean water?

We set out in 2018 to prove a hypothesis—we believe that if you back a cryptocurrency economy with a globally agreed upon inherent value like education, you can solve for volatility and stabilize a mature long lasting cryptomarket that awards everyone who adds value to that market in a decentralized way without the friction of individual partnerships.

What if education was a new gold standard?

And what if this new Learning Economy had protocols to award everyone who is helping to steward the growth of global education?

Education is a mountain. Everyone takes a different path to the top. Blockchain allows us to measure all of those unique learning pathways, online and in classrooms, into immutable blockchain Learning Ledgers.

By quantifying the true value of education, a whole economy can be built around it to pay students to learn, educators to create substantive courses, and stewards to help the Learning Economy grow. It was designed to provide a decentralized way for everyone adding value to global education to coordinate around the commonwealth without the friction of individual partnerships. Imagine the same for healthcare, nutrition, and our environment?

Imagine a world where we can pay refugees to learn languages as they find themselves in foreign lands, a world where we can pay those laid off by the tide of automation to retrain themselves for the new economy, a world where we can pay the next generation to prepare themselves for the unsolved problems of tomorrow.

Imagine new commonwealth economies that alleviate the global burdens of poverty, disease, hunger, inequality, ignorance, toxic water, and joblessness. Commonwealths that orbit inherent values, upheld by immutable blockchain protocols that reward anyone in the ecosystem stewarding the economy—whether that means feeding the hungry, providing aid for the global poor, delivering mosquito nets in malaria-ridden areas, or developing transformative technologies that can provide a Harvard-class education to anyone in the world willing to learn.

These worlds are not out of reach—we are only now opening our eyes to the horizons of blockchain, decentralized coordination, and new gold standards. Even though coordination is the last of the seventeen sustainable development goals, when solved, its tide will lift for the rest—a much-needed rocket fuel for global prosperity.

“Let us raise a standard to which the wise and the honest can repair.”  —George Washington
The views presented in this article are the author’s own and do not necessarily represent the views of any other organization.