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Artificial Intelligence in Construction

Artificial Intelligence is the next industrial revolution with seemingly no limit to its capabilities and implying radical reforms

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AI in construction: without planning, worker unrest is inevitable

Stephen Chegwin is a principal associate and Emma Humphreys is a partner at law firm Eversheds

Artificial intelligence (AI) is the next industrial revolution, with seemingly no limit to its capabilities. For many contractors, AI that radically reforms the workplace may seem a long way off. In some cases, this may lead to construction leaders not thinking about how AI may impact their workforce in the future. Amid a period of economic crisis, with low levels of growth and forever-shifting political agendas, five-to-10 years’ time may not be at the forefront of executives’ minds.

“The potential changes could have a material and lasting impact on employee and industrial relations”

We need only look across to businesses affected by climate-change targets, which are now having to decarbonise, to realise that these are issues that need consideration now. It is an exciting time, but do workers and unions feel the same? In some cases, AI has been a catalyst for industrial action, borne out of fear of job losses and insufficient time to reskill workers to re-enter the job market. The financial impact this unrest will have on businesses will be significant, and the long-term effect on reputation, worker engagement and employee/industrial relations is unquantifiable. 

From a legal perspective, as long as an employer properly consults and (where applicable) negotiates changes not already permitted under a worker’s contract, protects their workers’ privacy rights and ensures robots do not discriminate, there is little, legally, preventing employers from introducing new technology. Indeed, subject to any express contractual term limiting its effect, employees have an implied contractual duty requiring them to adapt to new ways of working. 

While compliance is important, it is not employment laws that will have the biggest impact.  The potential changes and their consequences will cause significant concern for workers and their trade unions, which could have a material and lasting impact on employee and industrial relations. This is what has brought about unrest and industrial action.

Shifting policies

The fear is there will be fewer jobs, and certain professions will disappear altogether – remember, there were actual human job titles called ‘computers’ before electronic computers existed. There is also sometimes an apprehension towards retraining when having spent years learning a skilled trade. 

But there are positives, too. Declining birth rates and tighter immigration policies mean there are fewer people available to recruit – a well-known issue in the construction sector. AI may help fill these skill gaps without impacting employment opportunities. Experts say using AI in hazardous environments such as construction will improve workplace safety. AI can also perform monotonous, mundane tasks that nobody likes doing.

But if monotonous tasks disappear, does that mean we are going to have to do more demanding, complex and stressful tasks without the mental respite of doing that easy task that gives us a break? Will there be more people available for work due to a reduction in jobs, meaning employers can pay less? And will there be enough time and capability to reskill displaced workers?

These questions are firmly in the minds of workers and trade unions right now. This is evidenced by policies, shaped by trade unions, that the Labour Party intends to progress if it forms the next government. These policies include industry-level collective bargaining agreements (known as Fair Pay Agreements) that not only set rules on matters such as pay, but also on the implementation and

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AI Governance

The Concerning Energy Cost of Artificial Intelligence 

Unregulated adoption of Artificial Intelligence could result in a surge in global energy consumption, similar to cryptocurrency mining.

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The Concerning Energy Cost of Artificial Intelligence 

By Felicity Bradstock – Nov 29, 2023

  • Unregulated AI adoption may lead to a substantial increase in global energy consumption, similar to the impact seen with cryptocurrency mining.
  • Advanced technologies, particularly AI, are becoming essential tools in various industries, optimizing operations but potentially driving up energy usage.
  • The EU’s AI Act acknowledges the energy-intensive nature of AI systems, highlighting the need for global regulatory measures to address the surge in energy consumption from new technologies.

As the world welcomes innovative technologies, they could spur a sharp spike in energy consumption if not regulated appropriately. A wide range of industries, including the energy sector, is looking to Artificial Intelligence (AI) to modernize operations. However, many are not considering the potential energy costs of adopting AI and other innovative technologies. Just as we’ve seen with cryptocurrency mining, the use of new, advanced technologies is expected to significantly drive up energy usage across different industries, and a lack of management could lead to disaster. 

There have been great advances in AI technology in recent years, leading many companies to adopt the technology and many individuals to gain a better understanding of it. It is quickly becoming an essential tool in everyday life, as it is used for a range of activities that we may not even consider. Checking into a flight, conducting a Google search, or using cruise control all rely on AI. For companies, the use of AI technology can optimize operations through smart decision-making and automation. It minimizes human error and typically drives up efficiency. It’s for this reason that so many companies are investing in the technology.

Each online interaction requires the use of remote servers – machines in data centers that use electricity to carry out operations. At present, data centers worldwide account for between 1 and 1.5 percent of the world’s electricity use, according to the International Energy Agency. While this figure may seem fairly low, the rapid rollout of new technologies, such as AI, is expected to drive up the sector’s energy usage significantly. There have been increasing discussions in the academic world around the high energy needs of AI, but this will have to quickly translate into national policy if we hope to manage future energy use in the sector.

An analysis published in October showed that the Nvidia Corporation – a multinational technology company, will be shipping around 1.5 million AI server units a year by 2027, which, when running, could equate to the annual use of 85.4 terawatt-hours of electricity. This is higher than the total electricity use of several small countries. Companies such as Nvidia now use advanced graphics processing units (GPUs) rather than simpler processors, called CPUs, to power operations, which require more energy to power. Brady Brim-Deforest, the CEO of Formula Monks, stated, “For the next decade, GPUs are going to be the core of AI infrastructure. And GPUs consume 10 to 15 times the amount of power per processing cycle than CPUs do. They’re very energy intensive.”

One of the technologies to take off in recent months is OpenAI’s ChatGPT, a chatbot that can conduct a humanlike conversation, respond to questions, and create written content. A recent paper from the University of Washington showed that hundreds of millions of queries on ChatGPT can cost around 1 gigawatt-hour a day, equivalent to the energy consumption of 33,000 U.S. households.

A professor of electrical and computer engineering at Washington, Sajjad Moazeni, explained “The energy consumption of something like ChatGPT inquiry compared to some inquiry on your email, for example, is going to be probably 10 to 100 times more power hungry.”

Industry experts expect the individual and industry use of AI to increase significantly in the coming years. We are at just one percent of where AI adoption is expected to be within the next two to three years. This means that governments must prepare their countries now to ensure the spike in energy use from advanced technologies is controlled early on.

While this seems like a negative outlook for AI and other innovative technologies, there are many plus sides to using advanced tech. Although energy use is high, these types of machines are typically much more efficient than humans, contributing to improved productivity and fewer human errors.

In Europe, the EU’s AI Act recognizes that AI systems will likely have a high energy consumption during their lifecycle. The legislation categorizes AI systems, setting out requirements for so-called “high-risk AI systems”. They must be designed and developed with logging capabilities that can record energy consumption, the measurement or calculation of resource use, and the environmental impact throughout the system’s lifecycle.

At present, there are no regulations in place to reduce the energy consumption of AI technologies in the EU, rather, the European Parliament focuses on transparency and gaining a better understanding of the energy use of the advanced technology.

The adoption of AI technology, and other advanced tech, is happening rapidly and is expected to soar in the coming years. While this could dramatically boost operational efficiency across a range of industries, it is also expected to drive up energy consumption.

The EU has taken a step in the right direction by encouraging greater transparency in AI, but governments worldwide must now consider introducing regulations to reduce unnecessary energy waste in advanced technologies.

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AI Governance

For Health AI to Fulfill Its Potential, Regulation Is Paramount

Clinicians are anxious about accuracy, administrators are worried about the cost of implementing AI

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For Health AI to Fulfill Its Potential, Regulation Is Paramount

Artificial Intelligence (AI) has been used in healthcare for years, assisting with radiology dictations, lab result analysis, and EKG interpretations. Newer AI innovation could revolutionize clinical workflows, alleviate documentation burden, and elevate clinical decision-making, offering substantial benefits for patients, health system operations, and even clinicians suffering from burnout.

Widespread adoption of AI in healthcare will be gradual, however, as there are several substantial barriers to overcome. Clinicians are anxious about accuracy, administrators are worried about the cost of implementing AI and whether their clinicians will use the tool, and patients are just plain skeptical. (Only 39% of adults believe AI is safe and secure.) Data security and bias also are significant concerns.

U.S. policymakers, including those in the White House, also have questions. The Biden administration just issued a far-reaching executive order on AI, which will require federal health agencies to establish a task force and, within 365 days, produce a strategic plan that could include regulatory action for AI-enabled tools used in hospitals, insurance companies, and other healthcare organizations. The president also called for the establishment of a national tracking system for AI-related harms and unsafe practices, which will be used to develop best practices to avoid these harms.

Healthcare tech investors and innovators, along with those of us who believe in the promise of AI, will need to work hard to establish this tool’s trustworthiness and to demonstrate the good it can do for patients and providers. Stringent regulations and protection of health information will play key roles in making this a reality.

AI Will Be a Game Changer for Patients and Clinicians

As physicians, one area where we see the most potential for AI is to reduce clinician burnout and improve patient-doctor relationships.

Physicians spend nearly two-thirds of their time on administrative tasks, including heightened documentation demands and onerous prior authorization processes. These distractions from spending time directly with our patients can lead us to burn out, and reduce our work hours or leave the profession entirely. The application of AI to create efficiencies have yielded promising results, particularly among tasks such as clinical note documentation, work shift scheduling, and medical billing and coding.

The University of Pittsburgh Medical Centre, the University of Kansas Health System, University of California San Diego Health, and others also are using AI to reduce documentation burdens.

Additionally, AI tools have effectively organized scattered patient data from various sources into concise patient history timelines, which will give providers a more accurate view of what a patient might need and what solutions have already been tried. As the American Hospital Association has said, “The use of AI has advanced patient safety by evaluating data to produce insights, improve decision-making and optimize health outcomes. Systems that incorporate AI can improve error detection, stratify patients and manage drug delivery.”

Valid Concerns About Bias, Equity, and Security

Despite this potential, other storylines have emerged: AI could perpetuate racial biases and may lead to data breaches.

Indeed, recent studies have confirmed worries that AI is amplifying racial biases, but this phenomenon aligns with the fact that we all possess implicit biases. The technology operates based on the inputs and algorithms created by fallible humans. As an article published by the World Economic Forum states, “Bias is an inherent human trait and can be reflected and embedded in everything we create, particularly when it comes to technology.” There are potential fixes, however, and the article suggests using open-source data might be one solution.

The point is we need to — and can — solve for these problems. We should not abandon the use of AI in healthcare simply because we have not yet tackled this important issue.

Because AI draws data from various platforms, there also is growing apprehension regarding the safety of intellectual property and other confidential information. For instance, if one of us were to seek assistance crafting a corporate announcement involving a trade secret set to be revealed in 6 months and input this data into ChatGPT, could others gain access to it? The ownership of the data becomes a pertinent question, and the confidentiality of the secret may be compromised. Patients share similar concerns about the privacy of their health information and whether it may be inadvertently shared.

These dual concerns demand careful consideration in the evolving landscape of AI. So, what are the solutions?

Public Policy: AI Needs to Be Regulated

The industry needs clear regulatory guidelines. Americans agree with this idea. In fact, more than 80% of U.S. adults believe there should be regulations ensuring AI is safe and secure.

Numerous companies have appealed to congressional leaders, urging the establishment of regulatory standards to safeguard patients, businesses, and innovators. A less-discussed issue is the uncertainty lack of regulation creates for innovators and investors. Investors recognize regulation is inevitable. Consequently, investing in products or technologies that could face stringent regulation in the future raises concerns. What if a product we have poured money into is later regulated out of existence?

Regulation provides a necessary framework and level playing field for investors and innovators alike. The Biden administration’s executive order is a good start, but Congress’s input will be essential.

The World Health Organization recently outlined six areas for regulation of AI for health: transparency and documentation; risk management; validating data and intended use; data quality; privacy and data protection; and ensuring collaboration between regulatory bodies, patients, healthcare professionals, industry representatives, and government partners.

This is a useful framework for U.S. policymakers to consider as they go forward. In the slow-moving world of public policy, lawmakers need to act with haste.

Healthcare Systems: Investment in Data Protection

Government actors cannot solve all the problems related to AI, of course. As with any new IT integration, healthcare systems must consider the new data protection measures critical to building trust and adoption.

These protection measures include having comprehensive plans for potential data breaches and ransomware attacks. Additionally, health systems should implement access and encryption controls, leveraging strong multi-factor authentication models. These measures add additional layers of security and can limit the likelihood of unauthorized access.

While adding these layers of security, health systems must avoid inadvertently injecting further complexity into workflows and processes, which could limit timely access for those who should be using the system. Working with technology developers and vendors regularly to proactively address cyber threats and minimize potential incidents’ impact is essential.

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