May June CBA Report

Artificial Intelligence in Construction: The Legal Implications A dvancements in artificial intelligence have enabled a number of technological solutions to emerge in the construction industry with the potential to improve worksite efficiency, data quality, and overall innovation. Early adoption of such technologies has inherent operational and competitive benefits, though legal risks must be evaluated and addressed prior to implementation. This article provides a deep dive into the legal implications of Artificial Intelligence and how attorneys in this discipline can prepare for the risks their clients may face. Overview of Ar tificial Intelligence Artificial intelligence (AI) generally refers to technology that uses algorithms to process data and simulate human intelligence. Examples of AI technology include machine learning, image recognition and sensors-on-site, building information modeling (BIM), and “smart contracts” stored on a blockchain-based plat- form. This technology can be used in the construction industry by way of design, operations and asset management, and construction itself. Machine Learning Machine learning at its core is a simple process: using an algo- rithm and statistics to learn from huge amounts of data. This type of technology recognizes patterns, extracts specific data, makes data-driven predictions in real-time, and can optimize many processes. As detailed by KHL Group, an example of machine learning increasing efficiency includes reducing equipment and operator idling time. According to KHL, machinery and operators spend 40% of their time idling and waiting for their next order. Machine learning can coordinate the movement of the machinery and its operators in a more efficient way to reduce idling. Not only will this boost productivity, it also reduces emissions and costs related to stagnant machines and operators. Similarly, in large engi- neering projects, it can be very complex and difficult to properly make decisions or coordinate work with so many pieces moving simultaneously. Machine learning can assist a project manager in making these decisions about the coordination of machinery and workers. Machine learning can also help assess project risk, construc- tability issues, asset maintenance, and identify various materials and technical solutions. Machine learning’s ability to process and learn from large amounts of data makes the technology ideal for data-intensive tasks. Companies implementing machine learning technology should be aware of several legal considerations. For example, a contract should address who will shoulder the risk associated with the technology and what degree of liability a party is taking on. This issue is especially important depending on who owns the technology – the firm, or a third party. Furthermore, it is unclear whether strict product liability or a different standard of liability will apply to all, or some, machine learning technology. The parties involved can reduce such uncer- tainty regarding what liability standard applies by negotiating which party is liable for certain malfunctions or damages within the governing contract. By Joseph Cleves and Zenus Franklin 12 l May/June 2021 CBA REPORT Feature