Law

You can't put it into a chip." Much has changed since Arnie, and his cyborg friends stormed LA in The Terminator. And while we're yet to experience havoc quite at the levels seen in the 1984 hit, artificial intelligence is a reality in our lives today. Autonomous vehicles were mooted in the 1920s and, hot on the heels of real-life'Iron Man' Elon Musk, car manufacturers are working tirelessly to stamp their badge on early mainstream vehicles. And as tech giants like Uber and Baidu invest heavily in AI research, many proclaim their cars are safer and more efficient than human-driven cars. While Musk predicts self-drive cars will be widely available within a couple of years, several issues need addressing before consumers will take their eye off the road: from the vehicles' ability to navigate phrenetic city centers to make critical moral choices in potentially dangerous situations. We're also beginning to see the emergence of AI across the financial services industry.

The impact that AI will have on different sectors of the economy is a widely debated topic. It comes as no surprise since leading technological innovations have always been met with fear and uncertainty. According to a study reported by Forbes, in 2016, something around US$8 billion to US$12 billion was invested in the development of AI worldwide. It's now difficult to imagine a job in near future that ultimately smart computers won't be able to do. So, with the advancement of AI, it's important to know where we stand and how it can alter the future.

Another industry that's starting to hone in on specific business use cases, instead of taking a technology-first approach, is financial services. Though the last few years have mainly seen companies looking at AI in the forms of automating content operations, enhancing trading tools and improving customer service, they're now demonstrating how AI can tackle much larger societal issues such as financial crime. Currently, only 1% of financial crime that happens through the banking system is stopped. AI has a real opportunity to bring together industry, government and regulators to consider a new approach. Various businesses – both start-ups and larger corporations alike – are making strides in fraud identification, sanctions screening, money-laundering, anti-bribery and corruption.

Over the past six years, the New York City police department has compiled a massive database containing the names and personal details of at least 17,500 individuals it believes to be involved in criminal gangs. The effort has already been criticized by civil rights activists who say it is inaccurate and racially discriminatory. "Now imagine marrying facial recognition technology to the development of a database that theoretically presumes you're in a gang," Sherrilyn Ifill, president and director-counsel of the NAACP Legal Defense fund, said at the AI Now Symposium in New York last Tuesday. Lawyers, activists, and researchers emphasize the need for ethics and accountability in the design and implementation of AI systems. But this often ignores a a couple of tricky questions: who gets to define those ethics, and who should enforce them?

Important resources like minerals, oil and diamonds often go hand-in-hand with conflict and poor governance. But when it comes to one particular resource -- the most important resource of all -- many think a different theory will hold true. Often referred to as the water wars thesis, it suggests that growing water scarcity will drive violent conflict as access to water dries up for certain communities. Analysts worry that people, opportunistic politicians and powerful corporations will battle for dwindling water supply, inflaming tensions. In a new study, researchers tried to map out how water wars will emerge around the world and which countries are most likely to see water-related conflict in the coming decades.

Text-based analytics, also known as text data mining, turns unstructured text into structured data that can be used in a multitude of ways by any business. Indian research firm MarketsandMarkets projects that the worldwide text-based analytics market will grow to 8.79 billion dollars by 2023, driven by major vendors like IBM and SAP. MarketsandMarkets continues that text analytics solutions empower users to perform quick data extraction and categorization with real-time insights from stored data and that the growing importance of insights generated from social media content to build effective marketing campaigns and enhance customer experience drives the market's growth. But while many companies are including text-based analytics to their roadmaps, the technology remains in the early stages of adoption. One reason for this is because companies are still struggling to master social media.

It was 2014, around the time when Travis Kalanick referred to Uber as his chick-magnet "Boober" in a GQ article, that I'd realized congestion in San Francisco had gone insane. Before there was Uber, getting across town took about ten minutes by car and there was nowhere to park, ever. With Boober in play, there was parking in places there never were spaces, but the streets were so jammed with empty, one-person "gig economy" cars circling, sitting in bus zones, mowing down bicyclists whilst fussing with their phones, still endlessly going nowhere, alone, that walking across the city was faster. To be fair, you wouldn't know there were 5,700 more vehicles a day on our roads if you'd just moved here. Nor if you were pouring Uber-delivered champagne over yourself in a tub of stock options while complaining about San Francisco's homeless from the comfort of your company-rental Airbnb where artists or Mexican families once lived.

AI and machine learning can produce background checks on people and companies in minutes instead of days.

The relationships are predicted from local polynomial regressions. Shaded areas indicate 95% confidence intervals. Preferences concerning time, risk, and social interactions systematically shape human behavior and contribute to differential economic and social outcomes between women and men. We present a global investigation of gender differences in six fundamental preferences. Our data consist of measures of willingness to take risks, patience, altruism, positive and negative reciprocity, and trust for 80,000 individuals in 76 representative country samples. Gender differences in preferences were positively related to economic development and gender equality. This finding suggests that greater availability of and gender-equal access to material and social resources favor the manifestation of gender-differentiated preferences across countries. Fundamental preferences such as altruism, risk-taking, reciprocity, patience, or trust constitute the foundation of choice theories and govern human behavior.

Today, Above the Law and Thomson Reuters present Big Data and the Litigation Analytics Revolution, the fourth and final installment of our Law2020 series, a multimedia exploration of how artificial intelligence and other cutting-edge technologies are reshaping the practice and profession of law. Previous Law2020 articles have explored the implications of AI for legal education, legal ethics, and legal research. Today, we take a deep dive into how sophisticated litigators are leveraging Big Data and analytics to decide critical questions of case strategy and tactics. Additionally, we explore litigation analytics is empowering lawyers to excel on many other fronts, including managing client expectations, accelerating client service, refining law firm operations, and optimizing legal research. You can read the feature here, and you can sign up using the form below to learn more information about Law2020.