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Robo-Lawyers Take on the Supreme Court: Can AI Help Overturn Convictions?

Robo-Lawyers Take on the Supreme Court: Can AI Help Overturn Convictions? - Bias In The Justice System - Can AI Help?

The justice system is not immune to the biases that pervade society. Studies have shown troubling discrepancies in how different groups are treated, from higher arrest and incarceration rates for minorities to harsher sentences for the poor. This systemic unfairness further disadvantages vulnerable populations.

Can AI and algorithms help counteract these biases? Some experts are optimistic. Algorithms coded to be blind to race, class and other irrelevant factors could evaluate evidence and risk more impartially. Software analyzing case histories could also detect inadvertent discrimination in charging, sentencing and parole decisions. Law professor Sonja Starr argues algorithms properly designed with fairness in mind could consistently outperform humans when making high-stakes predictions.

However, algorithms also risk perpetuating biases if their training data reflects distorted enforcement patterns or their criteria correlate with protected classes. ProPublica found an algorithm widely used to predict recidivism was biased against black defendants. Others caution predictive analytics could entrench discrimination and widen disparities. Civil rights groups like the ACLU warn against overreliance on biased data or algorithms that lack transparency.

How to balance AI's potential with risks of unfairness? Some jurisdictions now require algorithms be vetted to avoid discrimination before adoption. Techniques like adversarial debiasing and sampling correction show promise. But many experts argue the law should allow some form of algorithmic predictions while ensuring accuracy, accountability and due process protections. AI developer Ari Ezra Waldman argues algorithms could improve fairness but only with thoughtful regulation and sound policy.

Robo-Lawyers Take on the Supreme Court: Can AI Help Overturn Convictions? - Sifting Through The Evidence - AI Discovery Tools

A monumental challenge in complex legal cases is sifting through massive document collections to find the proverbial needles in the haystack. Discovery frequently entails reviewing hundreds of thousands or even millions of documents - an onerous task far beyond human capacity. This is where AI discovery tools can provide game-changing assistance.

Powerful algorithms can rapidly process astronomical volumes of data to surface key facts and connections. Natural language processing identifies concepts and semantic relationships within documents to extract relevant insights. Machine learning algorithms continually improve at classifying documents, recognizing entities, and surfacing salient portions. This makes AI indispensable for efficiently honing in on vital evidence.

Leading legal AI companies offer e-discovery platforms integrating advanced analytics to expedite document review. Tools like predictive coding and technology-assisted review use algorithms to predict document relevance. This allows attorneys to prioritize the most pertinent records for human examination. Recommender systems also suggest additional documents similar to ones flagged important. By automating portions of discovery, AI enables lawyers to focus time on higher-value tasks.

AI is even demonstrating skill at legal judgment in discovery tasks. An analysis of TAR performance in the tobacco litigation of the 1990s found algorithms matched or exceeded human reviewers for accuracy in identifying relevant documents. Other studies indicate AI can replicate human judgment in prioritizing and classifying documents. With further advancement, AI may one day take the lead on discovery decisions.

Robo-Lawyers Take on the Supreme Court: Can AI Help Overturn Convictions? - Writing Better Briefs With AI Research

Legal briefs are pivotal documents that can make or break a case, yet drafting persuasive briefs is challenging. AI research tools are emerging that can assist lawyers write stronger, more compelling briefs in less time. These tools aid with critical elements of brief writing from structure to style to citations.

One major challenge is crafting logically structured arguments. Software like Casetext's Compose and BriefCatch can analyze brief drafts to recommend improving organization and flow. They identify disjointed sections and suggest rearranging content for better coherence. Outlining tools can also assist creating clear, cogent frameworks upfront.

Another key challenge is locating relevant precedents and extracting insights to bolster positions. Legal research AI like Casetext CARA and ROSS Intelligence swiftly analyze millions of cases to surface on-point decisions and identify persuasive passages and language. This enables lawyers to readily weave in supporting case law. As ROSS co-creator Andrew Arruda explains, "It used to take hours and hours of reading cases to find language to cite. Now it takes seconds."

Accuracy and consistency in citations is vital for briefs. Citation management platforms like Citat or EVA reduce mistakes by automatically formatting citations and generating tables of authorities. They also update citations if precedents are overturned.

Style and tone play a subtle but crucial role. Microsoft's Editor tool leverages AI to suggest rephrasing sentences to improve clarity, vary diction, and enhance flow. Grammarly offers similar help editing for concision, readability, and precision. ProWritingAid goes further by analyzing style aspects like cadence and imagery. Subtle language polishing can make arguments more convincing.

Robo-Lawyers Take on the Supreme Court: Can AI Help Overturn Convictions? - Modeling Supreme Court Decisions

As AI capabilities grow, researchers are exploring how machine learning could help predict Supreme Court rulings on novel cases. Modeling the nation's highest court presents unique challenges versus lower courts due to its small docket of selectively chosen cases involving complex constitutional issues. Each year the Supreme Court decides only around 80 cases yielding limited training data. Justices also base rulings not just on technical merits but underlying judicial philosophies. Still, modeling advocates believe AI can uncover subtle patterns and insights to forecast high court decisions.

Researchers have employed machine learning to analyze textual features in past Supreme Court opinions and dissents to build predictive models. In a 2017 study, Daniel Katz, Michael Bommarito, and Josh Blackman developed an AI system that showed 70% accuracy in classifying case outcomes. It outperformed experts and baseline statistical models. The system identified stylistic, semantic, and syntactic patterns correlating with rulings. AI has also shown promise modeling individual justices' positions. A 2020 paper by Nikolaos Aletras et al. analyzed justices' votes using neural networks and achieved over 70% accuracy for most justices. Some factors it considered included opinion content, ideology, and overlaps in voting coalitions.

However, modeling dissenting and concurring opinions remains challenging. Researchers at the University of Lagos found machine learning models struggled to predict minority opinions due to their relative rarity and complexity. There are also concerns AI cannot yet account for nuances in legal principles and reasoning that drive Supreme Court decisions. Human expertise is still required to contextualize AI predictions and interpret subtleties.

Robo-Lawyers Take on the Supreme Court: Can AI Help Overturn Convictions? - What Would Scalia Think? AI and Originalism

The late Supreme Court Justice Antonin Scalia was an intellectual giant and titan of originalist jurisprudence. As a staunch proponent of interpreting the Constitution consistent with its original public meaning, Scalia helped cement originalism as a major school of legal thought. This raises the intriguing question - what would Justice Scalia have made of using AI to analyze the law?

On one hand, Scalia may have looked askance at applying algorithms to law, which he saw as an irreducibly human endeavor. He often stressed that judges must use practical wisdom, not formulas, when applying original meaning to modern facts. Scalia might argue human intellect and judgment is not reducible to machine processes.

Yet in some ways AI aligns with Scalia's philosophy. Originalists rely extensively on history to elucidate original meaning. AI's ability to rapidly search vast datasets could aid identifying Founding-era sources and precedents consistent with originalism. Algorithms modeling language patterns might also discern meanings lost to time. As Scalia himself said "Words have meaning. And their meaning doesn't change." AI textual analysis could uncover how semantic content has evolved.

Indeed, some AI researchers see connections between originalism and corpus linguistics - using hugedatabases to study meaning. Both originalists and computational linguists analyze historical texts to trace the evolution of language and concepts. AI techniques like latent semantic analysis, word embeddings and semantic networks may offer insights into how constitutional terminology was used and understood at the Founding.

Several legal tech startups are exploring AI for originalist research. Originalist.io applies natural language processing to parse meaning from original source texts. Ravel Law's Context tool visualizes the connections between cases to identify pivotal precedents. Casetext's CARA algorithm surfaces historical antecedents relevant to research questions. An originalist scholar could leverage tools like these to strengthen arguments with overlooked primary sources and influential cases.

At a broader level, Scalia saw himself as a champion of democracy, decrying judicial activism that strayed from ratifiers' intent into policymaking. He would likely approve of using technology to reinforce a jurisprudence rooted in historical consent. While Scalia was no tech utopian, he appreciated innovation's benefits. As he once remarked, "What constituent is going to vote against modernity?" He may have accepted AI as a means to honor the Constitution's original meaning.

Robo-Lawyers Take on the Supreme Court: Can AI Help Overturn Convictions? - The Road Ahead - Limitations of Legal AI

While AI has made impressive strides assisting lawyers, even the most advanced legal AI systems have limitations compared to human capabilities. As the technology continues evolving, developers are working to address critical gaps that currently constrain real-world deployment. Understanding these limitations provides insight into the road ahead for AI in law.

A fundamental limitation of legal AI is that it lacks generalized intelligence and common sense. Algorithms excel at narrow, specialized tasks but struggle with the fluid reasoning and contextual judgment integral to practicing law. Stanford's SAINT project found that an AI system that could pass law exams failed at basic comprehension when given unfamiliar scenarios. Legal judgment often requires weighing ethics, logic, and human factors no algorithm can fully replicate.

Another key limitation is that AI cannot replace certain essential human skills like empathy, creativity, and intuition. A lawyer's ability to empathize and build rapport with clients provides emotional intelligence algorithms lack. Negotiating settlements or swaying juries relies on creativity and intuition when applying legal principles. Human emotional connection and ingenuity have no substitute.

Moreover, today's legal AI depends heavily on training data that itself reflects human biases and inconsistencies. Algorithms can perpetuate flaws and distortions present in their data. Eliminating bias 100% remains an open technical challenge. This is why oversight from unbiased humans is indispensable when deploying legal AI.

In terms of applications, a persistent limitation is that legal AI still cannot reliably replace end-to-end human reasoning on complex multifaceted cases. At best current AI can assist individual discrete tasks like discovery or research, not holistic reasoning across the entire legal workflow. Seamlessly integrating human and machine capabilities remains difficult. Limitations like these make clear that while legal AI is powerful, it will not render lawyers obsolete.

Looking ahead, developers are pursuing innovations to address limitations. Teaching AI common sense knowledge and general reasoning skills is a major priority. New techniques in contextual learning, causal inference, and hybrid reasoning hold promise. Natural language models like GPT-3 also point toward more adaptable AI. But human-level comprehension remains distant. Advancing emotion and ethics is equally challenging but critical for socially responsible AI.

Robo-Lawyers Take on the Supreme Court: Can AI Help Overturn Convictions? - Robots In The Courtroom - Closer Than You Think

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