Daily Archives: June 1, 2022

Artificial intelligence in medicine is the use of machine learning models to search medical data and uncover insights to help improve health outcomes and patient experiences. Thanks to recent advances in computer science and informatics, artificial intelligence (AI) is quickly becoming an integral part of modern healthcare. AI algorithms and other applications powered by AI are being used to support medical professionals in clinical settings and in ongoing research.

Currently, the most common roles for AI in medical settings are clinical decision support and imaging analysis. Clinical decision support tools help providers make decisions about treatments, medications, mental health and other patient needs by providing them with quick access to information or research that's relevant to their patient. In medical imaging, AI tools are being used to analyze CT scans, x-rays, MRIs and other images for lesions or other findings that a human radiologist might miss.

The challenges that the COVID-19 pandemic created for many health systems also led many healthcare organizations around the world to start field-testing new AI-supported technologies, such as algorithms designed to help monitor patients and AI-powered tools to screen COVID-19 patients.

The research and results of these tests are still being gathered, and the overall standards for the use AI in medicine are still being defined. Yet opportunities for AI to benefit clinicians, researchers and the patients they serve are steadily increasing. At this point, there is little doubt that AI will become a core part of the digital health systems that shape and support modern medicine.

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Artificial Intelligence in Medicine | IBM

Mention artificial intelligence to pretty much anyone and there's a good chance that the term that once seemed magical now spawns a queasy feeling. It generates thoughts of a computer stealing your job, technology companies spying on us, and racial, gender and economic bias.

So, how do we bring the magic back to AI? Maybe it comes down to people and things that humans actually do pretty well: thinking and planning. That's one finding that will become clear in a review of the articles in this Quick Study packed with InformationWeek articles focused on AI ethics and bias.

Yes, there are ways to develop and utilize AI in ethical manners, but they involve thinking through how your organization will use AI, how you will test it, and what your training data looks like. In these articles AI experts and companies that have succeeded with AI share their advice.

What You Need to Know About AI Ethics

Honesty is the best policy. The same is true when it comes to artificial intelligence. With that in mind, a growing number of enterprises are starting to pay attention to how AI can be kept from making potentially harmful decisions.

Why AI Ethics Is Even More Important Now

Contact-tracing apps are fueling more AI ethics discussions, particularly around privacy. The longer term challenge is approaching AI ethics holistically.

Data Innovation in 2021: Supply Chain, Ethical AI, Data Pros in High Demand

Year in Review: In year two of the pandemic, enterprise data innovation pros put a focus on supply chain, ethical AI, automation, and more. From the automation to the supply chain to responsible/ethical AI, enterprises made progress in their efforts during 2021, but more work needs to be done.

The Tech Talent Chasm

How a changing world is forcing businesses to rethink everything, and in recruiting IT talent understand that great candidates want their employers to take AI ethics seriously.

3 Components CIOs Need to Create an Ethical AI Framework

CIOs shouldnt wait for an ethical AI framework to be mandatory. Whether buying the technology or building it, they need processes in place to embed ethics into their AI systems, according to PwC.

Why You Should Have an AI & Ethics Board

Guidelines are great -- but they need to be enforced. An ethics board is one way to ensure these principles are woven into product development and uses of internal data, according to the chief data officer of ADP.

How and Why Enterprises Must Tackle Ethical AI

Artificial intelligence is becoming more common in enterprises, but ensuring ethical and responsible AI is not always a priority. Here's how organizations can make sure that they are avoiding bias and protecting the rights of the individual.

Common AI Ethics Mistakes Companies Are Making

More organizations are embracing the concept of responsible AI, but faulty assumptions can impede success.

How IT Pros Can Lead the Fight for Data Ethics

Maintaining ethics means being alert on a continuum for issues. Heres how IT teams can play a pivotal role in protecting data ethics.

Ex-Googler's Ethical AI Startup Models More Inclusive Approach

Backed by big foundations, ethical AI startup DAIR promises a focus on AI directed by and in service of the many rather than controlled just by a few giant tech companies. How do its goals align with your enterprise's own AI ethics program?

The Cost of AI Bias: Lower Revenue, Lost Customers

A survey shows tech leadership's growing concern about AI bias and AI ethics, as negative events impact revenue, customer losses, and more.

What We Can Do About Biased AI

Biased artificial intelligence is a real issue. But how does it occur, what are the ramifications -- and what can we do about it?

How Fighting AI Bias Can Make Fintech Even More Inclusive

Digitized presumptions, encoded by very human creators, can introduce prejudice in new financial technology meant to be more accessible.

Im Not a Cat: The Human Side of Artificial Intelligence

Unconscious biases will be reflected in the data that feeds your AI and ML algorithms. Here are three simple actions to dismantle unconscious bias in AI.

When A Good Machine Learning Model Is So Bad

IT teams must work with managers who oversee data scientists, data engineers, and analysts to develop points of intervention that complement model ensemble techniques.

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Quick Study: Artificial Intelligence Ethics and Bias - InformationWeek

Artificial Intelligence has definitely made our lives easier in multiple ways. We can access multiple benefits right through our smartphones with the power of digital assistants like Google Assistant, Siri, Alexa, and more.

In todays world, multiple industries such as healthcare, e-commerce, financial services, etc., are leveraging the benefits of AI to the fullest of its potential. The technology has helped businesses grow in leaps and bounds with improved quality, security, and efficiency.

However, it is observed that engineering and construction are lagging behind in implementing artificial intelligence and machine learning solutions. The construction industry is worth more than $10 trillion a year.

Due to the complex challenges that the construction industry faces, the growth in the industry is severely limited. Dealing with challenges like cost and time overruns, labor shortage, health and safety, and productivity can bring revolution in the industry.

The construction industry has tremendous potential, and just by digitization, economically, the worth of the construction industry can be raised to $1.6 trillion a year. AI in construction can be instrumental in bringing this shift.

According to a report, Artificial Intelligence in the construction market is estimated to generate a revenue of $ 2,642.4 million by 2026, at a compound annual growth rate of 26.3% from 2019 to 2026. Technological advancements in AI and the Internet of Things (IoT) will create more opportunities for growth in construction and engineering.

Artificial Intelligence in the construction industry is undergoing a digital transformation. Focussing on technologies like artificial intelligence and machine learning at every stage of engineering and construction, from design to preconstruction to construction to operations and asset management, is exploiting the potential of the construction industry to new levels.

The areas where artificial intelligence in the construction industry is bringing impactful difference by getting the tasks done in a lesser amount of time and in a cost-effective manner.

Planning and designing sub-segment of construction are expected to benefit the most. In the global construction industry, the Europe market is anticipated to top the growth rate.

This technological shift is set to positively impact all the stakeholders across the project including contractors, owners, and service providers. With other adjacent industries such as transportation and manufacturing having already started working as an ecosystem, it becomes all the more important for the construction industry to adapt to the digitization of the processes.

As the technological shift is at a nascent stage in the engineering and construction industry, it will be advantageous for the companies that upgrade the technology. With artificial intelligence in construction, companies can comfortably tackle current issues while avoiding past mistakes.

With the use of statistical techniques of machine learning in construction, it becomes much more convenient and less time-consuming to scrutinize the data pertaining to changed orders, information requests, etc. This will help in proactively alerting the project leaders about the things that need critical attention. Safety monitoring also can be done with more efficiency.

We have established that AI is a critical component of modern engineering and construction approaches. Artificial intelligence in construction helps the industry solve its greatest challenges like cost and schedule overruns and safety issues. AI can be exploited throughout the construction project from inception and design, bidding, financing, transportation management, and operation and asset management.

Let us look in detail at how is AI being used in the construction industry:

AI-based Building Information Modeling (BIM) process has been helping architecture, engineering, and construction professionals make 3D model designs to plan efficiently, design, build, and repair the buildings and infrastructures.

With machine learning in construction, the industry uses AI-powered generative design to identify and collaborate the architecture, engineering, mechanical, electrical, and plumbing plan to ensure that there are no clashes within the sub-teams. Such measures mitigate the risk of rework. The algorithm of ML explores all the options and variations of the solutions to create design alternatives. Models with multiple variations are created and learned from each iteration, and this process is repeated until a perfect model is created.

It is expected that the planning and design sub-segment will grow exponentially with a CAGR of 28.9% between 2019 and 2026.

Construction companies can use AI-powered robots that are equipped with cameras. These robots can move autonomously through the construction site to capture 3D pictures.

With the help of neural networks, these pictures can be cross-checked with reference to the information from BIM and the bill of materials. The engineers managing large projects utilize this information to keep track of the progress of the work. It also helps identify quality errors at an early stage and keep a tap on financial information and time schedules.

It will not be an exaggeration to say that robotics and AI in the construction industry are ensuring the delivery of the best construction projects while saving costs and time.

Construction companies are exploiting the features of the Internet of Things (IoT) to manage the fleets of equipment and vehicles. With the inputs from the AI metrics, IoT provides solutions like location awareness, predictive maintenance capabilities, fuel and battery consumption, and much more.

With IoT devices and tags, it is now possible to predict the equipment breakdown possibility, which is an invaluable tool that saves time and money.

Construction sites are prone to accidents for various reasons. Analyzing and predicting risks with machine learning can avoid many such accidents. Monitoring the sources like photos and videos through the software can flag the potential risk that the site manager can address at the right time.

Reports pertaining to potential safety risks, such as unsafe scaffolding, waterlogging, and personnel missing protective equipment like gloves, helmets, and safety glasses, can be accessed by the user to rank the projects.

As mentioned in the introduction, there exists huge scope for tapping the potential of AI in construction management. There always is a dearth of labor in the construction industry as it involves risks and is a physically demanding job. The average turnover rate in the construction field is way more than in any other industry.

In such a scenario, AI-powered robots empower the project managers to oversee the real-time situation and resource requirements of multiple job sites. Based on the requirements, labor can be shifted to either a different part of the project or a different job site. The robots monitor the site to find the pain areas.

Companies that want to stay ahead in this competitive edge should quickly upgrade their technology. Smart construction can be enabled by incorporating AI. Lets explore how AI in construction is significant.

The current process of construction design is outdated and thus slow. By taking insights from building data, material data, and environmental data, you can optimize your project design.

When done manually, the building process tasks are tedious, time-consuming, and error-prone. The project manager spends most of his time assigning work and managing employee records.

However, Artificial intelligence can automate many such mundane tasks that can be performed with minimal or zero errors. AI automation can additionally take care of task delegation based on the data gathered from the employees. This not only streamlines the workflow process but also encourages workers to focus on their field of expertise.

With camera-enabled robots or AI-enabled construction equipment, data can be collected in different formats. By feeding these details into the deep neural network, the projects progress can automatically be classified from various aspects.

Such data empowers the management to know and address the minutest error or problem in the initial stage mitigating the subsequent major issues.

Employing self-driving construction machines can perform repetitive tasks tirelessly, efficiently, and quickly, such as welding, bricklaying, pouring concrete, etc.

Similarly, you can employ automated or semi-automated bulldozers for excavation and pre-work. Once the exact specifications are fed into these machines, they complete the job exactly as per the specifications. You can free up your human workforce for actual construction work and reduce the human risks involved in performing these tasks.

You can dramatically reduce the time in performing the land surveys in detail and taking aerial photos of the job site for better project management. With the help of drones, Geospatial Information System (GIS) and Geospatial AI (GeoAI) will help you keep track of project progress status and problems on the construction site while leveraging you with better decision making for efficient project management.

With the inception of technology, the construction industry will have cobots and robots working alongside workers. Robots take over the tasks that can be automated, and cobots are designed to work autonomously or with limited guidance.

Such an arrangement will help speed up construction, reduce costs, injuries, and better decision-making. AI in construction will not only overcome the labor shortage issue but will also lead to alterations in business models, and reduce expensive errors, making building operations more efficient. Thus, it is advised that business leaders at construction companies should focus on investment based on areas where AI can have the maximum impact based on needs.

Early adopters of this digital transformation are sure to gain the lead in the business. Getting an edge will make them leaders in setting the direction and reaping short-term and long-term benefits.

The construction industry is lagging behind in technology adoption. Now is the right time to get your processes automated and leverage AI in civil engineering.

Harness the power of automation with Appinventiv and take your construction goals to next level with functionally beautiful building designs.

As an AI development company, our team believes in molding their expertise based on our clients requirements as we have successfully done in the past helping business transformations.

Talk to our experts to tap the full potential of Artificial Intelligence in construction with Appinventive AI and ML development services.

From planning to designing to construction, AI has been spreading its benefits in all the sub-segments of construction. Future of construction with AI will oversee the complete construction project while advising on risk management, schedule adherence, structural integrity, and much more. Harnessing the potential of AI in construction will boost the profits, and reduce the injuries and risks involved.

Sudeep Srivastava

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AI in Construction - How Artificial Intelligence is Paving the Way for Smart Construction - Appinventiv

LONDON, May 31, 2022 (GLOBE NEWSWIRE) -- According to The Business Research Companys research report on the laser weapon systems market, growth in artificial intelligence is expected to drive the laser weapon systems market in the forecast period. The integration of artificial intelligence is gaining popularity among the laser weapon systems market trends. Artificial intelligence-powered systems are the battlefield's future. They can be deployed quickly and easily without being discovered, and they can wreak havoc with opposing fire. They are undetectable and quite effective. As previously reported by Financial Express Online, the military is expected to begin using artificial intelligence (AI) in the near future in order to become a totally network-centric force. It will take three to four years for the AI technology to be used in the Indian military. The Ministry of Defense has already established a Defense Artificial Intelligence Council with the defense minister as chairman and the three service chiefs, plus the defense secretary and the secretary of defense production, as members. The Defense Research and Development Organization (DRDO) has a specialized laboratory called the Centre for Artificial Intelligence and Robotics (CAIR), which employs about 150 scientists who work on AI Robotics, Control Systems, Command Control Communications and Intelligence (C3I), Networking, and Communications Secrecy. They've developed a robot family for surveillance and reconnaissance purposes. RoboSen is the name for a mobile robot for reconnaissance and surveillance systems. Moreover, the Indian Army during Army Day in 2021 demonstrated a Swarm Attack by drones on multiple targets.

Request for a sample of the global laser weapon systems market report

The global laser weapon systems market size is expected to grow from $4.81 billion in 2021 to $5.39 billion in 2022 at a compound annual growth rate (CAGR) of 11.9%. The growth in the market is mainly due to the companies resuming their operations and adapting to the new normal while recovering from the COVID-19 impact, which had earlier led to restrictive containment measures involving social distancing, remote working, and the closure of commercial activities that resulted in operational challenges. The laser weapon systems industry growth is expected to reach $8.53 billion in 2026 at a CAGR of 12.1%.

North America was the largest region in the laser weapon systems market and was worth $1.64 billion in 2021. The market accounted for 0.006% of the region's GDP. In terms of per capita consumption, the market accounted for $3.3, $2.7 higher than the global average. The growth of the Laser Weapon Systems market in the North American region can be attributed to the growing development of military drones, increased threats of aerial attacks, and increasing investment in military and defense. For instance, the military and defense budget of the USA for 2020 is USD 743.7 billion. Such a high budget will increase the demand to use more advanced products and weapons.

Major players in the laser weapon systems market are Applied Technology Associates, Boeing, Elbit Systems Ltd., General Atomics, BAE Systems, Lockheed Martin Corporation, MBDA, Northrop Grumman Corporation, Raytheon Technologies Corporation, Rheinmetall AG, Thales Group, Kratos, Leidos, Leonardo SpA, and Rafael Advanced Defense Systems.

The global laser weapon systems market is segmented by product into laser designator, LIDAR, 3D laser scanning, laser range finder, ring laser gyro, laser altimeter; by technology into solid state laser, chemical laser, free electron laser, chemical oxygen iodine laser, tactical high energy laser, others; by application into air-based, ground-based, sea-based.

The regions covered in the laser weapon systems market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, and Africa.

Laser Weapon Systems Global Market Report 2022 Market Size, Trends, And Global Forecast 2022-2026 is one of a series of new reports from The Business Research Company that provide laser weapon systems market overviews, laser weapon systems market analyze and forecast market size and growth for the whole market, laser weapon systems market segments and geographies, laser weapon systems market trends, laser weapon systems market drivers, laser weapon systems market restraints, laser weapon systems market leading competitors revenues, profiles and market shares in over 1,000 industry reports, covering over 2,500 market segments and 60 geographies.

The report also gives in-depth analysis of the impact of COVID-19 on the market. The reports draw on 150,000 datasets, extensive secondary research, and exclusive insights from interviews with industry leaders. A highly experienced and expert team of analysts and modelers provides market analysis and forecasts. The reports identify top countries and segments for opportunities and strategies based on market trends and leading competitors approaches.

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Artificial Intelligence Global Market Report 2022 By Offering (Hardware, Software, Services), By Technology (Machine Learning, Natural Language Processing, Context-Aware Computing, Computer Vision, Others (Image Processing, Speech Recognition)), By End-User Industry (Healthcare, Automotive, Agriculture, Retail, Marketing, Telecommunication, Defense, Aerospace, Media & Entertainment) Market Size, Trends, And Global Forecast 2022-2026

Autonomous Military Weapons Global Market Report 2022 By Type (Autonomous, Semi-Autonomous), By Product (Missiles, Rockets, Guided Bombs, Target Pods), By Platform (Land, Airborne, Naval) Market Size, Trends, And Global Forecast 2022-2026

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Interested to know more about The Business Research Company?

The Business Research Company is a market intelligence firm that excels in company, market, and consumer research. Located globally it has specialist consultants in a wide range of industries including manufacturing, healthcare, financial services, chemicals, and technology.

The Worlds Most Comprehensive Database

The Business Research Companys flagship product, Global Market Model, is a market intelligence platform covering various macroeconomic indicators and metrics across 60 geographies and 27 industries. The Global Market Model covers multi-layered datasets which help its users assess supply-demand gaps.

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Growth In Artificial Intelligence Is Expected To Drive The Laser Weapon Systems Market At A Rate Of 12% As Per The Business Research Company's Laser...

In marketing, 'creative impact' was once too subjective to measure. But with modern AI solutions, creative efforts can now be effectively analyzed. As part of The Drum's Creativity in Focus Deep Dive, Meta's Maria Pavlova (marketing science partner), Karen Chui (creative partner manager, EMEA) and Safia Dawood (marketing science partner) look at how AI tools can help marketing professionals develop more effective advertising strategies through creative effectiveness insights.

Data-driven optimization is widely adopted in digital advertising. Todays marketing professionals have perfected audience profiles and campaign logistics. Yet, the uncomfortable truth is that creative effectiveness is often overlooked despite being the most important campaign element. Creative quality accounts for 70% of an ads success, eclipsing procedural elements like flighting or platform choice.

Audiences want to be dazzled and amazed, so its important to offer them exciting content. Unfortunately, only 60% of advertisers measure creative effectiveness. Of those, the majority collect feedback using post-campaign focus groups or surveys, making the data somewhat unreliable.

At first glance, creative effectiveness appears too subjective to measure. However, todays AI (artificial intelligence) solutions can effectively analyze your creative impact and are becoming increasingly accessible to marketers.

Solutions from Meta are optimizing campaigns creative prowess and advancing AI-enabled creative workflows. Read on to learn how, and discover case studies on brands that gained sales using creative effectiveness insights.

Cutting-edge marketers already use AI to maximize the potential within their advertising strategy. AI simplifies the creative process by helping to produce new creative assets and adjust existing ones at scale.

For example, automated software can translate content across languages, crop images to frame subjects better and generate image descriptions to make visual assets more accessible. As a result, marketing professionals are uncovering new artistic heights and reimagining how to convey messages across mediums.

However, AI is able to take an even larger role within the advertising life cycle and assist marketing teams in planning and optimizing content like never before.

AI tools can help marketing professionals develop more effective advertising strategies based on audience interaction data. This continuous approach to ad optimization is the future of marketing and will help brands lead with content that audiences love.

Measuring creative effectiveness begins with Illumination, which provides initial insights and builds a model to predict future campaign performance. The results help uncover your next big campaign idea and guide your creative process in later stages.

Algorithms begin by analyzing anonymized and aggregated historical data on consumer behavior and identifying which creative elements are most common among your highest-performing ads.

The result is a framework that helps marketers compare different advertising approaches and discover the most effective one before the campaign begins. In turn, marketing professionals can generate new creative assets with more confidence and exceed audience expectations more effectively.

AI can also help steer the ongoing creative strategy once your campaign begins.

Changes within the digital ecosystem mean advertisers must adopt a new playbook. By leveraging continuous optimization through a test-and-learn process, marketers can focus on what audiences like rather than who they are, and avoid privacy concerns from targeted advertising.

Performance evaluation using machine learning can highlight the potential of each creative element within your existing assets. Automated testing solutions can also enable brands to experiment with dynamic business outcomes more efficiently. As a result, creative teams can devise more effective strategic approaches while campaigns are in flight.

Businesses with multiple locations often have to balance variations in brand guidelines to connect with local audiences effectively. Consistent color palettes and tones of voice help brands feel unified and cultivate a consistent public image.

Yet, these variations present a challenge to marketers as communication must be consistent over time.

Todays AI tools measure how creative assets and copy fit within existing brand guidelines and identify areas for improvement. These insights help brands stay true to their guidelines, both between and within sub-brands, and ensure that public-facing communications are curated and consistent.

As a leading beer brand, Heineken needed a way to assess its creative effectiveness across its 300+ sub-brands. By partnering with Meta and CreativeX, a creative analytics company, Heineken uncovered new insights into its creative effectiveness and strategic potential.

[The] creative elements we measured are foundational to driving effectiveness in the Metaecosystem without restricting the creative process and flow, says Sander Bosch, global CMI manager communication effectiveness at Heineken.

Dominos Pizza is one of the worlds most recognized pizza brands, offering a seemingly infinite number of combinations of toppings and crust fillings.

Dominos wanted to improve their video creative using technology and AI and turned to Spirables creative intelligence suite. The two worked together to create three unique ad variants for a multi-cell split test on Facebook and Instagram across a four-week period.

Spirable determined that while motion was beneficial, moving the pizza out of the frame was not.

By utilizing these new insights in future ads, Dominoes increased return on ad spend by 20%, click-through rate by 6%, and reduced cost per result by 9%.

Have you considered how to measure your creative effectiveness?

Creative effectiveness is just the solution to help marketers measure campaign performance and brand recognition in the transition away from targeted online advertising. AI tools from Meta, CreativeX, Spirable, and others are available today and can elevate your creative effectiveness and remove the guesswork from future ad strategies.

By maximizing your creative effectiveness, you can mitigate signal loss and deliver high-impact content to followers at the same time. You can also establish creative best practices for future campaigns and curate your brand portfolio more effectively.

Moreover, automating routine creative tasks using AI allows you to focus on your campaigns long-term health and ensure ad budgets go further.

Discover tools that can help your business improve creative effectiveness with key insights from Meta Foresights.

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How Artificial Intelligence Is Helping To Measure Creative Effectiveness In Marketing - The Drum

As the use of artificial intelligence wedges its way into every side of business and culture, government regulation is (perhaps too slowly) moving to build legal boundaries around its use.

On May 12, 2022, the Equal Employment Opportunity Commission issued a new comprehensive technical assistance guidance entitled The Americans with Disabilities Act and the Use of Software, Algorithms, and Artificial Intelligence to Assess Job Applicants and Employees. The guidance, which covers a number of areas, defines algorithms and artificial intelligence (AI); gives examples of how AI is used by employers; answers the question of employer liability for use of vendor AI tools; requires reasonable accommodation in deploying AI in this context; addresses the screen out problem of AI rejecting candidates who would otherwise qualify for the job with reasonable accommodation; requires limitations to avoid asking disability-related and medical questions; promotes promising practices for employers, job applicants, and employees alike; and gives many specific examples of disability discrimination pitfalls in using AI tools.

Here are some primary takeaways from the new guidance:

Per the guidance: A disability could have this [screen out] effect by, for example, reducing the accuracy of the assessment, creating special circumstances that have not been taken into account, or preventing the individual from participating in the assessment altogether.

Per the guidance: An assessment includes disability-related inquiries if it asks job applicants or employees questions that are likely to elicit information about a disability or directly asks whether an applicant or employee is an individual with a disability. It qualifies as a medical examination if it seeks information about an individuals physical or mental impairments or health. An algorithmic decision-making tool that could be used to identify an applicants medical conditions would violate these restrictions if it were administered prior to a conditional offer of employment.

Per the guidance: [E]ven if a request for health-related information does not violate the ADAs restrictions on disability-related inquiries and medical examinations, it still might violate other parts of the ADA. For example, if a personality test asks questions about optimism, and if someone with Major Depressive Disorder (MDD) answers those questions negatively and loses an employment opportunity as a result, the test may screen out the applicant because of MDD.

There are a number of best practices employers can follow to manage the risk of using AI tools. The guidance calls them Promising Practices. Primary points:

Per the guidance: Examples of reasonable accommodations may include specialized equipment, alternative tests or testing formats, permission to work in a quiet setting, and exceptions to workplace policies.

With the increasing reliance on AI in the private employer sector, employers will have to expand their proactive risk management so as to control for the unintended consequences of this technology. The legal standards remain the same, but AI technology may push the envelope of compliance. In addition to making a best effort in that direction, employers should closely review other means of risk management such as vendor contract terms and insurance coverage.

This article was prepared with the assistance of 2022 summer associate Ayah Housini.

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New EEOC Guidance: The Use of Artificial Intelligence Can Discriminate Against Employees or Job Applicants with Disabilities - JD Supra