Artificial Intelligence In Construction: The Legal Implications – Technology – United States – Mondaq News Alerts

Artificial Intelligence In Construction: The Legal Implications – Technology – United States – Mondaq News Alerts

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Advancements 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 Artificial 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
platform. 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
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

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 Modeling

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” and Blockchain Technology

“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.

Originally published by CBA Report.

The content of this article is intended to provide a general
guide to the subject matter. Specialist advice should be sought
about your specific circumstances.

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