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 platform. This technology can be used in the construction industry by way of design, operations and asset management, and construction itself.
Machine learning at its core is a simple process: using an algorithm 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 engineering 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, constructability 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 uncertainty regarding what liability standard applies by negotiating which party is liable for certain malfunctions or damages within the governing contract.
A possible solution for risk allocation is a collective liability regime. Here, artificial intelligence manufacturers pay a levy, which is sent to a centralized pool and paid to consumers who suffered injuries from failures associated with AI. Individuals who suffer AI-related injuries would not be required to prove a particular entity was at fault; instead, they would only need to prove they suffered an injury causally related to an AI system.
Parties will also need to discuss who will own the data the technology records and uses, and how that data can be used by vendors, if at all. This will require third parties to comply with applicable data protection laws and their requirements when negotiating contract terms with vendors.
Machine learning can provide a tremendous amount of value to a firm. However, many legal issues and liabilities are created due to the use of new technology. Such issues, including ownership of the technology, liability standards, and data protection rights, need to be weighed against the benefit of the software and are contract terms that will need to be negotiated and clarified.
Image Recognition and Sensors-on-Site technology use cameras and other sensors to assess vast quantities of video, pictures, and other recorded conditions from worksites. Such technology has the potential to: (1) monitor worksite conditions for safety risks and hazards; (2) enhance equipment and material management, boosting productivity; and (3) improve worker safety by identifying unsafe behavior to inform future training priorities.
For example, Suffolk, a Boston-based general contractor, is already developing predictive-algorithms to monitor safety risks. Suffolk collected over 700,000 images, taken from over 360 job sites in the last 10 years, and uploaded them to startup Smartvid.io’s cloud-based platform. The algorithm analyzed the images to identify safety hazards, such as workers not wearing proper protective equipment. Suffolk is also exploring ways to easily locate equipment on the jobsite and how contractors can track materials from suppliers. Knowing where available tools are and when critical materials arrive can reduce downtime and increase productivity through better planning and resource allocation.
A primary concern for construction industry stakeholders will be what new duties and responsibilities will accrue to those who implement and use the technology. Contractors may unknowingly be opening themselves to additional risks, liability, and greater responsibility with the information this technology provides. While most of these questions can be addressed through careful contractual drafting, stakeholders will have to think through these questions and possibilities. To reach acceptable risk allocation as AI usage in construction increases, parties should be prepared to intensely negotiate these terms in any agreement.
Building information models (“BIMs”) are three-dimensional, digital construction blueprints. BIMs allow numerous project participants to view and modify the same model and are generally highly detailed, allowing users to access information on each building.
BIMs offer several benefits: improving participants’ capacity to visualize and comprehend a design; allowing for better communication between participants by constantly updating the design when changes are made; improving design quality, detail, and precision; and allowing owners to closely monitor a project for deviation from the original plan in real-time. These benefits can likely reduce the risk of liability in many cases.
BIMs also create several new risks of liability. First, the roles and responsibilities of participants can become irreversibly intertwined in a BIM. However, this concern can be addressed by clearly defining the participants’ rights and responsibilities by contract. Second, BIMs create intellectual property right concerns. The traditional rule is the party that creates the model owns it. Since BIMs are often compiled from information contributed by numerous sources and parties, the situation becomes more complicated. The solution to this issue is to address it by contract. If parties fail to do so, however, they should be prepared to follow a convoluted web of information to locate the true owner of the model.
“Smart contracts” use computer code that automatically executes all or parts of an agreement and is stored on a blockchain-based platform. Like traditional contracts, smart contracts define the rules and penalties of an agreement; however, smart contracts automatically enforce their obligations and penalties. Once operational, smart contracts generally require no human intervention to execute and enforce their terms. An example is automatically transferring funds from one party to another when specific criteria are met and imposing penalties if certain conditions are not met. Hybrid contracts, however, consist of a traditional written contract alongside a smart contract to cover an automated function, such as payment.
Smart contracts pose a variety of legal issues. Since data shared on blockchain technology cannot be altered or modified, it is virtually impossible to alter the terms of the contract. Additionally, courts will likely struggle adjudicating smart contracts and blockchain technology due to a lack of familiarity with the nascent technology. However, as this new technology is slowly, but increasingly, implemented across industries, the steepness of the learning curve should decline. Hybrid contracts allow some automation and provide security for parties by having a written contract that can easily be read and interpreted by a court, holding the most promise for industry-wide application.
Common legal issues can arise from increasing implementation of AI in construction.
Technological advancement and the implementation of AI in construction pose new legal implications and questions for industry stakeholders. While most concerns can be addressed through careful drafting of contracts, stakeholders should be aware of these legal issues. There is still much uncertainty regarding the legal standards, responsibilities, and expectations of parties when integrating this technology to construction. However, early adopters stand to gain a competitive advantage over others who lag behind. Acting not blindly, but with an acute awareness of legal issues not previously encountered, is of the highest importance.
Cleves practices in Taft’s Construction and Real Estate groups with a focus on integrated project delivery (IPD) and developing and revising contracts. He is recognized by Best Lawyers in America and was named “Lawyer of the Year” in Cincinnati for Construction Law. Zenus Franklin is a business and finance attorney in Taft’s Dayton office, where he focuses on corporate governance, privacy and data security, and data governance planning.
https://www.khl.com/international-construction/machine-learning-its-all-about-the-data/145259.article