2023: The Year of OArb

A common question asked after our presentations on online dispute resolution (ODR) is: “when can we expect the arrival of robot arbitrators?” Even if we had focused our time during the presentation on topics like effective online hearing rooms or document management systems, the audience inevitably makes the leap to the irresistible topic of robot arbitrators. It’s not that we aren’t sympathetic; the excitement of the next breakthrough invention is often more compelling than the mundane (but essential) questions we consider in our work about ethical issues and ordering discovery in online arbitration (what Amy has termed “OArb”). But this has happened more times than we can count.

Perhaps the fascination stems from all the science fiction movies and TV shows we had in the 70s and 80s where robots would become commonplace and do whatever humans told them to do. Think back to those episodes of Lost in Space where Robby the Robot would carry around firewood and indicate when danger was approaching, even though it was clearly just a clunky metal costume with a person inside. The robots got a little more believable with R2D2 and C3PO in Star Wars, but they still felt like George Lucas’ fantasy more than reality.

But as the years have gone by, we’ve seen technology deliver on many imagined possibilities from our childhoods, from Dick Tracy’s wrist radio (the iWatch gets pretty close) to Knight Rider’s talking car (now named Siri or Alexa). As the saying goes, most advanced technologies are indistinguishable from magic, and we’ve gotten used to the release of seemingly magical new technologies on a regular basis.

During the past ten years we have seen a steady expansion in OArb. Arbitration clauses have become a norm in not only commercial business-to-business contracts, but also business-to-consumer (“B2C”), employment, and even cryptocurrency contracts.  Arbitration makes sense in many technical areas due to the inclusion of an expert arbitrator. Arbitration is also beneficial on an international level because it provides a neutral forum and enforceable awards under the New York Convention on the Recognition and Enforcement of Foreign Arbitral Awards (“New York Convention”). Additionally, In the United States, courts usually enforce arbitration clauses under the Federal Arbitration Act (“FAA”) , along with efficiency-focused arbitration and contract jurisprudence. This is true if arbitration clauses are included in e-contracts per the Electronic Signature Act (“ESign”).

At the same time, OArb is flexible and includes using technology and digital tools to facilitate and execute processes ending in a final determination of a dispute by a neutral third party.  For example, OArb may use asynchronous and/or synchronous communications.  It also may involve text-only or virtual hearings, and mixtures thereof.  OArb’s use of technology allows parties to upload and submit supporting documentation to support their claims. Online hearings save time, cost, and stress of traveling to and attending in-person processes. Such OArb systems may even provide more accurate and complete redress for consumers than class actions—which have been criticized for providing insufficient and inequitably distributed relief in some cases.  OArb may also incorporate the new and emerging technologies, such as blockchain and smart contracts for enforcement, predictive analytics to aid decision-making, and virtual reality to expand voice without violence.

OArb is is a distinct subset of ODR because it culminates in a final award rendered by a third-party neutral under the FAA and New York Convention.  Moreover, OArb has spiked in the COVID-19 pandemic.   Virtual meeting technology such as Zoom, Skype, Google Meet, WebEx, and Teams has made virtual hearings relatively cheap and easy. Individuals have become accustomed to online communications in the lockdown.   Even in large-dollar claims, such as international construction deals, COVID-19 prompted parties to arbitrate online.  Parties grew eager to resolve their disputes, and arbitrators began ordering virtual arbitration, even over a party’s objection.  All have increasingly embraced virtual platforms as their best, safest, and most convenient means for moving forward. OArb has become the new normal in many contexts.

Still, the idea of the robot arbitrator continues to loom large. What if robots could replace all the highly paid arbitrators? What if we could use a robot arbitrator instead of a $400 per hour arbitrator who takes a great deal of time to sift through legal briefs and reams of supporting documentation? What if the robots do a better job than we can do, does that mean we will become useless? Will we be obsolete relics, just waiting to be upgraded to a better model? This concern isn’t unique to arbitrators, of course; similar fears are being expressed by others in well paid professions like finance and medicine.

And the logical next question: if the robots do in fact replace us, who is going to ensure that the people programming the robots aren’t putting their fingers on the scales? We’ve put a lot of time and energy into developing ethical rules and conflict checks for arbitrators, and we have systems to ensure that human arbitrators are playing by those rules. It is much harder to look into the “eyes” of a robot (webcams?) to see whether it’s planning to respect rules around confidentiality, neutrality, and privacy.

This may be why in the ODR field we have largely eschewed the language of AI to describe the roles technology can play in an arbitration process. We have instead opted for the concept of the “fourth party.” In this paradigm the disputants are party one and party two, the arbitrator is party three, and technology (in all its forms) is party four. This conceptualization emphasizes the collaboration between human neutrals and technology because there are some tasks the third party can do better and some tasks the fourth party can do better. The primary question instead becomes how to optimize the partnership to achieve our shared objective, which is finding a fair resolution to the dispute at hand.

We have nonetheless stalled in this partnership. There is a great deal of potential for machine learning in arbitration – which could make the fourth party smarter. We believe 2023 will be the year of expanding the role of the fourth party in OArb. Through this partnership, humans can benefit from modular assistance from natural language processing, predictive analytics, document analysis, agreement technologies, and the like. As computer processors get more powerful and we are able to store more and more information, and the power of machine learning will continue to grow.

You might have heard of the Turing Test, devised by Alan Turing in 1950, which focuses on “a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.” If you sit down at a computer and communicate with a machine learning algorithm through text message, and you can’t tell whether you’re speaking to a machine or a person, then that machine learning algorithm has passed the Turing Test. Expanding computing power has made it much harder to tell what is generated by a fourth party and what is generated by a third party.

But the real question is, what do these improvements in machine learning mean for the future practice of arbitration? Several opportunities jump to mind.

  1. Predictive analytics: Machine learning algorithms can analyze data from past disputes to identify patterns and predict the likelihood of future disputes. This can help mediators, arbitrators, and other dispute resolution professionals anticipate and prevent disputes before they arise.
  2. Decision support: Machine learning algorithms can help dispute resolution professionals make more informed decisions by providing them with insights and recommendations based on data analysis. For example, an algorithm could analyze data from past disputes to identify common factors that led to successful resolutions and suggest strategies for resolving similar disputes in the future.
  3. Automated dispute resolution: Machine learning algorithms can be used to automate certain aspects of the dispute resolution process, such as document analysis and contract interpretation. This can help to speed up the process and reduce the workload for dispute resolution professionals.
  4. Enhanced collaboration: Machine learning algorithms can facilitate collaboration between dispute resolution professionals by providing them with real-time data and analytics that can help them make more informed decisions.

Overall, the use of machine learning in dispute resolution has the potential to improve the efficiency and effectiveness of the process, helping to resolve disputes more quickly and accurately.

If you’d like an example of how an algorithm can pass the Turing Test, consider that we didn’t write the points above.  Starting with “Predictive analytics…” and ending with “…resolve disputes more quickly and accurately,” that passage was written by a new algorithm called GPTchat in response to the question, “how will machine learning change the practice of dispute resolution?”

It is not a stretch to contemplate the creation of similar machine-learning powered tools that are trained to listen to the arguments of both parties and render a decision. They can provide responses 24×7, only asking for a penny’s worth of energy as compensation, and they never take a break. Such tools will likely be imperfect and inaccurate at the beginning, but with each case they will learn more, and they will improve over time as the technologies they leverage underneath the hood become more powerful.

We have observed the development of artificial intelligence tools over the past 20 years, and for a long time the hype outpaced the reality. But the equation has changed in the past year.  Driven by rapid growth in computing power and storage capability, new AI tools are making the concept of “robot arbitrators” more of a reality. We now believe we will see AI powered evaluative dispute resolution processes become commonplace over the next 3-5 years, and 2023 will be the breakthrough year when they emerge into the mainstream in a big way.

Of course, we must guard due process and consider the finality of OArb in using these technological tools. Arbitration, again, is unique due to its finality. At this stage, we need a “human in the loop” to safeguard fairness, empathy, and human aspects of dispute resolution. There is still value in being human and bringing our experience, sensitivities, and proclivities for flexibility to the table of OArb.

For this and other reasons, arbitrators should not fear technological developments. Yes, there are risks in the disruptions AI will introduce, but there are many opportunities as well. AI and machine learning are just tools, and as with all tools, we need to set rules and guidelines to minimize the risk of harm. We must always ensure that the fourth parties we work with are under human control, that they are constantly reviewed and reviewable, and that they are monitored in a transparent way to ensure their compliance with the ethical guidelines that govern our field.

Used correctly, these tools will expand the reach of arbitration into dispute types and geographies that we were previously unable to service. They could result in a major expansion in access to justice around the world, with more fairness and justice for more people. Yes, many questions still need to be answered, and many best practices and ethical rules are yet to be devised. But from our perspective, the promise outweighs the pitfalls, and we should work together to build and refine these machine learning mechanisms to devise the optimal fourth party partner that can best assist us in helping our parties achieve fair resolutions.

Written by: Amy Schmitz,  Colin Rule

Credit: arbitrate.com