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Assessing the Legal Risks in AI—And Opportunities for Risk Managers

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Last year, Amazon made headlines for a developing a human resources hiring tool fueled by machine learning and artificial intelligence. Unfortunately, the tool came to light not as another groundbreaking innovation from the company, but for the notable gender bias the tool had learned from the data input and amplified in the candidates it highlighted for hiring.

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As Reuters reported, the models detected patterns from resumes of candidates from the previous decade and the resulting hiring decisions, but these decisions reflect that the tech industry is disproportionately male. The program, in turn, learned to favor male candidates.

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As AI technology draws increasing attention and its applications proliferate, businesses that create or use such technology face a wide range of complex risks, from clear-cut reputation risk to rapidly evolving regulatory risk. At last week’s RIMS NeXtGen Forum 2019, litigators Todd J. Burke and Scarlett Trazo of Gowling WLG pointed toward such ethical implications and complex evolving regulatory requirements as highlighting the key opportunities for risk management to get involved at every point in the AI field.

For example, Burke and Trazo noted that employees who will be interacting with AI will need to be trained to understand its application and outcomes. In cases where AI is being deployed improperly, failure to train the employees involved to ensure best practices are being followed in good faith could present legal exposure for the company. Risk managers with technical savvy and a long-view lens will be critical in spotting such liabilities for their employers, and potentially even helping to shape the responsible use of emerging technology.

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To help risk managers assess the risks of AI in application or help guide the process of developing and deploying AI in their enterprises, Burke and Trazo offered the following “Checklist for AI Risk”:

  • Understanding: You should understand what your organization is trying to achieve by implementing AI solutions.
  • Data Integrity and Ownership: Organizations should place an emphasis on the quality of data being used to train AI and determine the ownership of any improvements created by AI.
  • Monitoring Outcomes: You should monitor the outcomes of AI and implement control measures to avoid unintended outcomes.
  • Transparency: Algorithmic decision-making should shift from the “black box” to the “glass box.”
  • Bias and Discrimination: You should be proactive in ensuring the neutrality of outcomes to avoid bias and discrimination.
  • Ethical Review and Regulatory Compliance: You should ensure that your use of AI is in line with current and anticipated ethical and regulatory frameworks.
  • Safety and Security: You should ensure that AI is not only safe to use but also secure against cyberattacks. You should develop a contingency plan should AI malfunction or other mishaps occur.
  • Impact on the Workforce: You should determine how the implementation of AI will impact your workforce.

For more information about artificial intelligence, check out these articles from Risk Management:

Six Tips For Risk Managers When Assessing Automation Hazards

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From a risk management perspective, one of the benefits of automation is that robots can play a significant role in reducing injuries when deployed to replace or support workers in high-hazard jobs, such as those involving high force and repetition. Yet, without appropriate risk assessments, their benefits can become skewed in other situations.

Unfortunately, many companies still make critical automation decisions without adequately engaging risk management, which can leave workers vulnerable to a new set of unanticipated workplace hazards. By some estimates, manufacturers will deploy 1.2 million new robots in the next decade; the expanding use of robotics may bring numerous new significant safety considerations along with a critical need for effective risk management.

As the trend toward greater automation gains momentum, here are six tips for risk managers to assess automation-related workplace hazards and help their organizations achieve the gains they envisioned with these major investments:

  1. Do not underestimate the value risk management brings to automation. Although automation is not new, companies still have much to learn about its effective deployment and implementation – especially in situations where the aim is increased productivity.
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     Risk managers need to be actively involved in assessing potential risks as automation purchasing decisions are made, as well as in planning and managing implementation, related employee training and post-implementation safety assessments and injury monitoring.

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  1. Initiate a dynamic dialogue. When the aim of investing in robotics and automation is specifically for productivity improvement, the starting point should be for risk and operations managers and safety/ergonomics experts to open a dialogue with workers in units designated for automation; they are much more flexible than robots and can offer insights on improving the workplace, reducing injuries and driving efficiency – either without significant investment or by focusing deployment of automation where it is likely to have the greatest impact.
  1. Focus on human factors with increased automation. As plants become more fully automated, the interface between the equipment and employees becomes increasingly significant. Historically, there has been an increased emphasis on automation, but an insufficient focus on the human interface. With more industries retooling plants and upgrading operations, the premium will be on the intelligent design of the next generation of facilities. It calls for the use of advanced tools, such as HumanCAD 3D, to analyze the impact of new equipment on human operators, production, and maintenance, as well as assessments from ergonomics and risk management professionals.
  1. Understand automation is not a panacea. Even the latest robotics may not address every issue, such as assembly tasks that require very fine motor skills, hand-eye coordination and higher-level thinking (such as complex assemblies, part orientation, inspection and precision fits). The automation of some tasks ultimately could require higher rates of repetition in the upper extremities of workers. In this case, ergonomic workstation design, scheduled breaks and worker feedback will be keys to prevent injuries and achieve gains in quality and productivity.
  1. Do not overlook worker demographics. Although automation may help all workers raise their productivity levels, implementation should account for the needs of an aging workforce. Businesses with multiple manufacturing facilities may have to refine workstations, signage, and lighting in areas with higher concentrations of older workers to achieve consistent productivity gains across all operations.
  1. Monitor potential worker safety issues with new product designs. Some forward-looking organizations are pushing for the application of design rules and human factors analysis to evaluate the “Design for Assembly and Ergonomics” (DFMAE) process. In these situations, product designers and advanced manufacturing equipment engineers collaborate with ergonomists to evaluate new product designs and the manufacturing equipment that goes with it. Until such approaches become widespread, it makes sense to check how new product designs might affect assembly workers.
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    Even slight adjustments in product design, manufacturing equipment or workstations can make the job easier and less stressful for employees without expensive robotics.

Investments in highly sophisticated equipment require thorough evaluation of all potential risks involving the interface between the equipment and employee. In some cases, operating equipment may expose workers to a range of injuries, such as repetitive motion issues. And high-speed mobile equipment can pose an outright danger on a factory floor without the delineation of designated “safety zones.” As key members of their organization’s automation team, risk managers play a critical role in anticipating and assessing exposures, developing remedies and facilitating success to ensure robots are working in collaboration with employees and not creating new, unanticipated risks.

Why Visibility Into Contracts is Crucial for Procurement Pros

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Risk is a topic of pervasive and growing concern in supply and sourcing management. Procurement itself is no longer just about acquiring products and services for the best price and controlling expenses. Instead, purchasing and supply professionals are under tremendous pressure to drive out costs and ensure that procurement strategy is in line with wider business objectives, including how personally identifiable information is handled.

That scrutiny means that the procurement function is often the external face of an organization, especially today’s competitive enterprise, acting as an early-detection system for spotting anything that might represent a financial legal, or reputational risk. As a result, they are not only tasked with, but also relied upon, to give executives the analytics and guidance they need to make better business decisions. It means also that they are a crucial player in understanding and revealing legal and regulatory exposure, while providing stakeholders with useful intelligence for achieving broader strategic goals.

The fact is, access to more and better information changes how we think about and act upon that reconnaissance. While a procurement team’s performance, alongside their suppliers, can have a major impact on the revenue and profitability of any business, effective procurement and with it, better risk management, requires the right technology.

The enterprise can positively change how it manages exposure throughout the procurement lifecycle based on contract analysis, and by managing and optimizing the supply base through the aggregation of suppliers and their contracts into a single system, a 360-degree view into supplier information, capabilities, risk and performance can be created.

Contract analysis as opportunity
Generally, when we think about risk management in the enterprise, the accepted school of thought would be how to control it. But what if we instead looked to properly identify risk as a tactic for finding more revenue? The most powerful thing is to see how risk translates into lost revenue, or perhaps more importantly, new-found revenue opportunities.

There is a lot of talk about the seismic changes that AI will bring, and the prospects for self-driving cars, home robots, and all manner of time and labor-saving applications are truly staggering. However, it is the more prosaic application of AI through text analytics (enabled by the marriage of machine learning and natural language processing) that is driving the most successful applications of AI in business right now.

Contract management and analysis has emerged as, perhaps, the most important shift in the work undertaken by commercial and procurement teams in a modern business. Historically, manual contract review took months or even years to complete, if it was done at all. The new demands placed on procurement and supply management teams requires that they look to technology to automate the process.

Contract analytics, which is most effectively being driven by the application of machine learning-based decision-science and AI, has become central to efficient contract servicing and risk management. In short, data sophistication is now essential to competitive excellence, but more importantly should be central to how risk is identified as a strategy towards profitability.

A better view of the heat map
Imagine a heat map of your organization that points out where different liabilities exist and indicates the risk level of each of these liabilities. This is analogous to the application of advanced analytics to contracts within the enterprise, where the identification of non-standard language or clauses, and how far they deviate from the standard, are a primary objective. This requires far more than a static database with reporting capabilities–it demands accurate, dynamic, real-time intelligence that can only be derived from AI-driven methodologies.

A clear view of your contracts will not only provide you with the analytics and guidance needed to make better business decisions, but also better manage management.  Contract analytics and discovery systems can uncover hidden opportunities to rationalize suppliers, negotiate better deals, and take advantage of incentives.

Every organization has its own appetite for risk. Assessing a company’s risk profile is the first step toward applying right-fit contract analytics that give a view of contracts at scale and provide a heat map of potential risk exposure. Without this assessment, it is hard to take a holistic view of the entire contract corpus, and with it a comprehensive understanding of, for example, service-level agreements and uptimes, non-standard conditions and terms, and whether or not liabilities are covered by insurance.

Considering that figures from the International Association for Contract and Commercial Management (IACCM) indicate that businesses typically waste between 4% and 9% of total spend, the result of applying contract analysis to the procurement and supply management process is sobering. Based on the lower end of IACCM’s estimates, a company with an annual spend of $1 billion is losing $40 million. Should the procurement team successfully identify half of that loss, and just 50% of that figure is actually recoverable, it translates into a potential savings of $10 million.

In particular, contract analysis has been used by the enterprise in a powerful way for non-standard clause detection. Non-standard contracts constrain organizations under the best of circumstances. Vague or complex contract language, on which an organization can base many key business decisions, is a central factor in choosing between alternative courses of action. This “what if” evaluation of risk is always viewed by supply and procurement professionals as an aspect of business operations that must be properly managed within a tolerable range. This contribution to the resilience of the overall procurement process also ensures smooth and more predictable financial performance.

On the M&A side, for instance, the challenges and opportunities can be enormous. When an enterprise subsumes a smaller player, it may sample and review a set of contracts during the due diligence phase to assess obligations and risk exposure. Manual reviews or analysis using insufficient technologies, such as archaic contract lifecycle management systems that were never meant for this task, typically sample just a fraction of the overall contract portfolio–and thereby just a fraction of the overall risk–the organization could inherit through the deal.

Monitoring and managing risk is costly
Real and powerful insight can only be extrapolated from data at scale. But still, the point is not simply to mechanically extract as much data as possible from the contract corpus, but to apply it in the context of the business so that heat maps of risk are tailored to the risk assessment itself.

Better risk management of buy-side and sell-side contracts, and other legal agreements such as non-disclosure agreements and leases, requires that the analysis tools are able to learn an organization’s specific languages and clauses, and can be trained to search for specific elements, regulatory issues, and, yes, even non-standard clauses. This can only be achieved through machine learning techniques in AI.

When that risk consumes mission-critical resources, the cost is even higher, yet too many organizations remain unaware of the value that contract analysis can bring to the procurement process. That is why risk management must be well-understood by procurement teams, in particular, including a clear view of the technologies that can be implemented with minimum cost and high impact. With the application of AI to contract analysis, exposure to risk can be minimized and the impact of a potentially catastrophic event can often be avoided altogether.

Risk Management of Technology Risks Lagging, Survey Finds

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SAN ANTONIO—Technology is becoming more and more necessary for the growth of companies, enhancing their abilities to get products to their destination faster and automate core processes. In fact, it’s predicted that revenues from AI-related technologies will reach $127 billion by 2025.Technology has also led to safer work conditions for employees with the use of wearable technology and drones.

According to the 15th Annual Excellence in Risk Management report by Marsh and RIMS, which examines risk professionals’ knowledge of and role in managing technology innovation such as artificial intelligence (AI), blockchain, and the Internet of Things (IoT), 59% of respondents said their organizations are currently using or exploring the use of IoT systems; 47% are using or exploring the use of AI; and 24% are using or exploring the use of blockchain.

Despite this growth, however, only 14% said they strongly believe they have a clear process in place for addressing disruptive technology risks. Almost half could not say if there was a clear process.

The report found that most risk professionals would benefit from balancing their view of digital technology. When asked what it means for their organization to be “digital,” a majority cited operational improvements, such as automating core processes, over growth initiatives such as new ways of doing business and interacting with customers.

By ignoring how digitization is changing the way companies interact with their customers, risk professionals cannot fully understand the changing risk profiles of their organizations, the report notes.

“Emerging technologies like artificial intelligence and blockchain are fast becoming the new normal, yet risk management is not keeping up,” observed Brian Elowe, U.S. client executive leader at Marsh. “Only by asking questions and understanding the underlying technologies and their uses throughout the organization can risk professionals truly appreciate their organizations’ risks and respond accordingly.”

Fear and lack of understanding about these new technologies could be the basis of this lag. As the report indicates, however, it is not necessary for risk professionals to understand the detailed intricacies of every new technology. Instead, they should be able to discuss them with technologists.

“Risk management professionals can add tremendous value and insight, supporting organizations’ ability to make strategic decisions regarding disruptive technology,” said Carol Fox, RIMS vice president of strategic initiatives. “Engaging in innovation that impacts our companies, customers, industries, and even the practice of risk management itself is a giant first step. While risk professionals do not need to be ‘experts’ in the intricacies of these technologies, they can certainly advance the performance benefits that each new technology brings.”

The good news for many risk professionals – and their organizations – is that managing emerging risks and working across the organization are not new challenges. In recent years, risk professionals have had a number of risks to contend with, including terrorism, climate change and cyberattacks. “Risk management executives are well placed to be part of the leadership team around technology adoption; their position naturally connects them to others across their organizations,” according to the report.

Highlights from the report:

  • The majority of respondents said they are most interested in technology that enables them to identify emerging risks (57%) and enhance data security (57%).
  • Of the respondents whose organizations have cross-functional risk committees, 31% said disruptive technologies are discussed at every meeting.
  • 40% of respondents said they would consider switching insurers and other advisors based on their ability to provide innovations in the claims area.