Two related themes were prevalent in 2019: artificial intelligence (AI) and data analytics. Following are some of the best quotes and observations we found on AI in law, finance and insurance, healthcare and pharmaceuticals and recruiting.
Since AI requires data, that’s where we’ll start. One promising new AI trend may eliminate or circumvent the obstacle of not having enough data or the right kind for machine learning. According to AI experts, one of the biggest trends for 2020 is the emerging use of synthetic data generation to simulate and create new scenarios, overcoming the scarcity and limitations of real-world data available to researchers and developers of AI solutions.
AI in law
Law firms have been known as being technology laggards, but that’s changing with advancing legal technologies like AI.
While legal teams can’t change the way AI technology essentially functions, they can account for its limitations and work around them. So long as AI operates with human oversight and is used exclusively for the specific tasks for which it was trained and tested, such problems can be mitigated.
Legal Tech News
Law firms have adopted AI to enhance and facilitate processes like document review, legal research and due diligence. Yet, AI is also opening up new practice areas:
Paul Hastings launched an AI practice group to help clients deploying AI-driven services and products. The firm will help clients defend against class-action lawsuits and give legal advice in areas such as compliance with laws and regulations, data privacy issues, AI governance and ethics.
DLA Piper launched an AI practice that provides legal guidance and helps companies understand the legal risks of adopting AI systems.
The Artificial Intelligence Video Interview Act, which takes effect in Illinois on January 1, 2020, is the first of its kind in the county. It’s designed to regulate the increasing use of AI in the hiring process—under the new law, employers must inform applicants that algorithms will analyze their interview videos, along with other requirements.
Society for Human Resource Management (SHRM)
AI in finance and insurance
From risk management to customer service, the banking and insurance sectors are adopting AI to improve a number of functions and processes.
Banks are leveraging AI on the front end to smooth customer identification and authentication, mimic live employees through chatbots and voice assistants, deepen customer relationships, and provide personalized insights and recommendations. AI is also being implemented by banks within middle-office functions to detect and prevent payments fraud and to improve processes for anti-money laundering (AML) and know-your-customer (KYC) regulatory checks.
Financial machine learning creates a number of challenges for the 6.14 million people employed in the finance and insurance industry, many of whom will lose their jobs—not necessarily because they are replaced by machines, but because they are not trained to work alongside algorithms.
If artificial intelligence (AI) systems are going to have anything to do with insurance, they ought to obey the same rules that flesh-and-blood players follow. Poorly designed AI systems, or systems drawing on poor data sources, might lead to illegally arbitrary or discriminatory business decisions, or decisions based on reasoning that’s locked away in an AI system and not available for review by live humans. As such, a team of state insurance regulators at the National Association of Insurance Commissioners (NAIC) has put together a new AI regulatory principles draft.
AI in healthcare and pharmaceuticals
AI has the potential to personalize healthcare, quickly bring new drugs and devices to market and contribute to better outcomes while removing costs and easing limitations like provider time and resources.
Combining AI with expert clinical and domain knowledge will begin to speed up routine and simple yes/no diagnoses—not replacing clinicians, but freeing up more time for them to focus on the difficult, often complex, decisions surrounding an individual patient’s care.
Drug development is a tedious process that can take up to 12 years and involve the collective efforts of thousands of researchers. The costs of developing new drugs can easily exceed $1 billion. But there’s hope that AI algorithms can speed up the process of experimentation and data gathering in drug discovery.
TNW (The Next Web)
AI technologies in health care may actually be ‘re-humanizing’ healthcare, just as the system itself shifts to value-based care models that may favor the outcome patients receive instead of the number of patients seen. Numerous technologies are in play today to allow healthcare professionals to deliver the best care, increasingly customized to patients, and at lower costs.
Health IT Analytics
This last observation within the healthcare industry also applies to other industries—there just happens to be more at stake in medicine. It touches on the growing concern of trust in AI:
But deep learning tools also raise worrying questions because they solve problems in ways that humans can’t always follow. If the connection between the data you feed into the model and the output it delivers is inscrutable—hidden inside a so-called black box—how can it be trusted? Among researchers, there’s a growing call to clarify how deep learning tools make decisions—and a debate over what such interpretability might demand and when it’s truly needed. The stakes are particularly high in medicine, where lives will be on the line. If doctors do not understand why the algorithm made a diagnosis, then why should patients trust the recommended course of treatment?
AI in manufacturing
AI and robotics continue to make inroads across various manufacturing sectors.
‘Scaling AI in manufacturing operations: A practitioners’ finds that manufacturers can focus on three use cases to kickstart their AI journey: intelligent maintenance, product quality control and demand planning. The report also reveals that global manufacturers in Europe are leading the way with at least a single use case of AI in their operations. In Germany, it’s 69 percent, compared to 28 percent in the US and 11 percent in China.
ET (Economic Times) Auto
For the manufacturing industry, one of the biggest challenges is quality control. Product managers are struggling to inspect each individual product and component while also meeting deadlines for massive orders. By integrating AI solutions as a part of workflows, AI will be able to augment and address this challenge.
TNW (The Next Web)
AI in recruiting
Our last look at AI is in recruiting and talent acquisition.
Companies just can’t get through the applications. And if they do, they’re spending three seconds on average. Using an AI system can ensure that every résumé, at the very least, is screened. Proponents of these recruiting tools claim that artificial intelligence can be used to avoid human biases, like an unconscious preference for graduates of a particular university, or a bias against women or a racial minority. But AI often amplifies bias, unless it strips out information like name, age, gender, or school, and they’re not good for nuance. Hiring is an extremely social process. Companies don’t want to relinquish it to tech.
If AI is an important topic for your organization, LAC Group’s research & intelligence solutions for briefings, curated alerts and other services will keep you well-informed to empower thought leadership, product development and any other business objective.