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As the use of artificial intelligence recruitment and hiring tools becomes more prevalent, it is important to remember that such processes are subject to anti-discrimination laws. Employers have an obligation to inspect such tools and processes for bias based on any protected class (including disability and age) and should have plans to provide reasonable accommodations during the recruitment and hiring process. On May 12, 2022, the Equal Employment Opportunity Commission and Justice Department issued guidance for the first time regarding the use of algorithms and artificial intelligence in employment-related decision making and the ways such tools may violate disability discrimination laws. The guidance clarifies that employers are responsible for ensuring that their hiring technologies including any artificial intelligence used comply fully with disability discrimination laws, even if the technology is administered by a third party, and that employers provide reasonable accommodation as needed. The guidance further provides that regardless of intent, if the artificial intelligence tool has the effect of screening out applicants with disabilities or adversely affecting individuals with disabilities, the employer may be violating disability discrimination laws. The guidance directs employers to be critical of artificial intelligence hiring tools that they use and recommends asking vendors a number of questions and only develop and select tools that measure abilities and qualifications that are truly necessary for the job even for people who are entitled to an on-the-job reasonable accommodation. https://www.eeoc.gov/laws/guidance/americans-disabilities-act-and-use-software-algorithms-and-artificial-intelligence.

In addition, for the first time, the EEOC this month sued an employer related to its use of artificial intelligence hiring tools. Specifically, the EEOC sued three integrated companies providing English-language tutoring services under the iTutorGroup brand name for age discrimination for allegedly programming their online recruitment software to automatically reject older applicants because of their age. According to the EEOCs press release regarding the lawsuit: [The companies] hire thousands of tutors based in the United States each year to provide online tutoring from their homes or other remote locations. According to the EEOCs lawsuit, in 2020, [the companies] programmed their tutor application software to automatically reject female applicants age 55 or older and male applicants age 60 or older. [The companies] rejected more than 200 qualified applicants based in the United States because of their age.

Accordingly, employers need to examine their artificial intelligence recruitment and hiring tools now to ensure the algorithms in the tools do not unfairly screen out individuals based on their membership in a protected class.

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Beware of the Use of Artificial Intelligence Recruitment and Hiring Tools - JD Supra

Written by Brandi Vincent May 16, 2022 | FEDSCOOP

Editors note: This story was updated with comments from NATO.

More than 80 artificial intelligence experts from the U.S. and other nations are helping NATO explore the military implications and opportunities for leveraging the technology.

NATOs Science and Technology Organization (STO) and the NATO Communications and Information (NCI) Agency jointly hosted a workshop earlier this month in the Hague, Netherlands, marking the launch of a new strategic initiative to bolster the alliances AI approach.

AI is one of the key emerging and disruptive technologies identified by NATO as vital for the maintenance of its technological edge, NATO Chief Scientist Dr. Bryan Wells said in a press release Monday. By working together, the STO and the NCI Agency are able to bring together global experts to ensure the very best scientific expertise is available to advise NATO and its Allies and Partners on the latest scientific trends in this area.

AI scientists, ethicists and military operational experts from across Europe and North America participated in the workshop. The AI experts also met with NCI Agency scientists and engineers at the agencys lab based in the Netherlands, where they observed demonstrations of how artificial intelligence systems can be trained using NATO data to confront existing challenges, and several existing projects affiliated with the alliances different communities.

One example is the Resilience Assessment Project funded by Allied Command Transformation, a tool to assess resilience in seven key areas that NATO has defined, such as transportation, energy, communications, and others, NCI Agency Chief of Data Science and AI Dr. Michael Street told FedScoop in an email on Tuesday.

That project is based on open-source data from a wide variety of sources and is being developed in close collaboration with the end-users and subject matter experts, he explained. It enables users to better assess situations and conduct what if analyses to understand the impact of crises.

For example, the tool is being used to support exercises to understand the state of critical infrastructure and points of interest such as road networks or energy supplies, Street said.

The core tasks of the transatlantic military alliance between the U.S., Canada and 28 European nations involve collective defense, crisis management and cooperative security.

The organizations defense ministers officially signed on to an AI strategy in October, formalizing their intent to accelerate NATOs collective adoption of the technology, ensure it is deployed responsibly, and protect against threats it might pose. Building on that effort, the AI experts participating in the recent invite-only multinational workshop kicked off a strategic initiative to drive AI-focused horizon scanning a methodology for performing comprehensive assessments of possibilities and threats connected to a particular technology or other topics.

The new NATO initiative employs aeronautical scientist Theodore von Krmns foundational principle to bring armed forces and scientific personnel closer together to enhance collective knowledge and understanding, according to the press release. Such scans examine the state-of-the-art in the field, the outlook for the next decade, its relevance for the armed forces, potential avenues for investment, and more.

They have also been undertaken on laser weapons, quantum technologies and optronic 3D imaging systems.

Horizon scanning allows us to bring together technology experts and military leaders to define the medium-to-long term activities to fully benefit from a technology, such as artificial intelligence in this case, Street said. This NATO strategic initiative employs Von Karman horizon scan methodology to understand the impact of technology on defense, and vice versa.

Following the workshop, a group of experts from across the alliance will continue to work on these issues over the remainder of the year.

They will prepare a set of recommendations on how AI-based technologies could be further developed and applied for NATO use, and how defense use can contribute to AI development. The recommendations will be delivered to the NATO Science and Technology Board, the highest authority within the Science and Technology Organization, Street confirmed.

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Artificial intelligence experts helping NATO with new 'horizontal scanning' initiative - FedScoop

CAMBRIDGE, Mass.--(BUSINESS WIRE)--Iterative Scopes, a pioneer in precision medicine technologies for gastroenterology, announced today that its artificial intelligence platforms will be featured in three abstract presentations at the upcoming Digestive Disease Week 2022 (DDW 2022). The meeting will take place virtually and onsite at the San Diego Convention Center in San Diego, CA, from May 21 to May 24.

Experts in inflammatory bowel disease (IBD) and artificial intelligence (AI) will present two abstracts discussing data on the companys endoscopic scoring algorithms in ulcerative colitis (UC), a condition included in the umbrella of IBD, developed in collaboration with Eli Lilly and Company.

The data are drawn from an innovative partnership between Iterative Scopes and Lilly, focusing on studying the effectiveness of machine learning (ML) models to automatically score endoscopic disease severity in UC. Progress in IBD research is hindered by variability in the human interpretation of endoscopic severity. This unique ML approach incorporates novel methods of interpreting and integrating visual data into the assessment of clinical trial endoscopic endpoints. This data has the potential to be considered as a substitute to human central readers, which may reduce clinical trial costs and accelerate IBD research.

Aasma Shaukat, MD, MPH, Robert M. and Mary H. Glickman Professor of Medicine and Gastroenterology at NYU Grossman School of Medicine, and a leader of the Iterative Scopes advisory board, will present the first publicly available registration trial data on SKOUT, the companys automated polyp detection algorithm for colorectal cancer screening, in a plenary session on Late-Breaking Clinical Science Abstracts. The plenary sessions at DDW are the forum for highlighting some of the years best research abstracts as determined by the conference organizers. In her discussion, Dr. Shaukat will highlight results of a multicentered, randomized clinical trial in the US assessing whether SKOUT is superior to standard colonoscopy in increasing the adenomas per colonoscopy.

SKOUT has a pending 510(k) and is not available for sale in the United States. SKOUT received its CE Mark certification in 2021.

We founded Iterative Scopes four years ago to change the trajectory of GI drug development and clinical care, and we are extremely excited to share results of Iterative Scopes work in applying cutting edge, computational approaches towards achieving this goal, said Jonathan Ng, MBBS, the founder and CEO of Iterative Scopes. We are excited to share our work with the clinical community at DDW, through these presentations and the other events surrounding DDW.

Iterative Scopes Presentations at DDW 2022:

Endoscopic Scoring Solutions in Ulcerative Colitis

Title: Can a single central reader provide a reliable ground truth (GT) for training a machine learning (ML) model that predicts endoscopic disease activity in ulcerative colitis (UC)?Date & Time: May 21, 5:15-5:30 PM PDTSession Type: Research ForumPresenter: Klaus Gottlieb, MD, JD (Senior Medical Fellow, Lilly)Presentation No: 278Location: Room: 23 - San Diego Convention Center

Title: Development of a novel ulcerative colitis (UC) endoscopic activity prediction model using machine learning (ML)Date & Time; May 23, 12:30-1:30 PM PDTSession Type: PosterPresenter: David T. Rubin, MD (Joseph B. Kirsner Professor of Medicine and Chief, Section of Gastroenterology, Hepatology and Nutrition, UChicago Medicine and Chair of Iterative Scopes Advisory Board)Presentation No: Mo 1639Location: Poster Hall - San Diego Convention Center

SKOUT, Polyp Detection in Colonoscopy

Title: Increased Adenoma Detection with the use of a novel computer aided detection device, SKOUTTM: Results of a multicenter randomized clinical trial in the USDate & Time: May 24, 8:15-8:30 AM PDTSession Type: PlenaryPresenter: Aasma Shaukat, MD, MPH (Robert M and Mary H. Glickman Professor of Medicine and Gastroenterology, Department of Medicine, NYU Grossman School of Medicine and a leader of Iterative Scopes Advisory Board)Presentation No: 5095Location: Room 3 - San Diego Convention Center

Iterative Scopes was founded in 2017 as a spin out of the Massachusetts Institute of Technology (MIT) by Dr. Ng, a physician-entrepreneur, who developed the companys foundational concepts while he was in school at MIT and Harvard. In December 2021, the company and its investors closed a $150 million Series B financing, which attracted a roster of A-list venture capitalists, big pharmaceutical companies venture arms, and individual leaders in healthcare.

About Iterative Scopes

Iterative Scopes is a pioneer in the application of artificial intelligence-based precision medicine for gastroenterology with the aim of helping to optimize clinical trials investigating treatment of inflammatory bowel disease (IBD). The technology is also designed to potentially enhance colorectal cancer screenings. Its powerful, proprietary artificial intelligence and computer vision technologies have the potential to improve the accuracy and consistency of endoscopy readings. Iterative Scopes is initially applying these advances to impact polyp detection for colorectal cancer screenings and working to standardize disease severity characterization for inflammatory bowel disease. Longer term, the company plans to establish more meaningful endpoints for GI diseases, which may be better predictors of therapeutic response and disease outcomes. Spun out of MIT in 2017, the company is based in Cambridge, Massachusetts.

About Digestive Disease Week (DDW)

Digestive Disease Week (DDW) is the largest international gathering of physicians, researchers and academics in the fields of gastroenterology, hepatology, endoscopy and gastrointestinal surgery. Jointly sponsored by the American Association for the Study of Liver Diseases (AASLD), the American Gastroenterological Association (AGA) Institute, the American Society for Gastrointestinal Endoscopy (ASGE) and the Society for Surgery of the Alimentary Tract (SSAT), DDW is an in-person and virtual meeting from May 21-24, 2022. The meeting showcases more than 5,000 abstracts and hundreds of lectures on the latest advances in GI research, medicine and technology. More information can be found at http://www.ddw.org.

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Iterative Scopes to Present Three Abstracts on Artificial Intelligence Applications for GI Endoscopy at DDW 2022 - Business Wire

The pandemic has impacted every industry in one way or the other. The insurance industry is no different. However, the silver lining is that it has reinforced the importance of technology more firmly, especially Artificial Intelligence (AI) and Cloud Computing for this specific sector.

Artificial Intelligence in the insurance market size is valued at USD 6.92 billion by 2028 and is expected to grow at a compound annual growth rate of 24.08% in the forecast till 2028.

Based on a survey, 21% of insurance organizations report they are preparing their workforce for collaborative, interactive, and explainable AI-based systems. It is predicted that investment in AI Insurance is ranked high on decision-makers agenda.

The growing need to offer personalized insurance services leads to the requirement of insurance automation for operational processes. AI does exactly the same by automating the operational tasks performed by humans and are done without fatigue and error in a shorter span of time.

AI has brought in a revolutionary change in the way the insurance industry performed a few years ago. Insurance was normally associated with loads of paperwork, time-consuming meetings, filing complicated claims, and waiting for months for a decision.

AI in insurance has brought in automation that has started rebuilding the trust toward insurance providers. Not only this, insurance automation helps in stimulating business growth, lowering risks and frauds, and automating various business processes to reduce overall costs.

To cut it short, it is helping insurers and policyholders alike. Heres how:

The machine learning algorithms help underwriters to gauge risk with more information that helps them to offer better and tailored premium pricing. Additionally, AI in the insurance industry is streamlining the process to connect applicants with carriers directly making the process more efficient.

With the need of the hour and to stay competitive, it has become imperative for the insurance industry to adopt the latest technologies like machine learning, robotic process automation, and more. Let us understand how can the adoption of the latest technologies add value to the existing tedious and exhaustive insurance process.

Processing claims is a complicated process. Agents are required to assess various policies and comprehend them with every detail to determine how much the customer will receive for the claim. There are many steps that are repetitive and standard tasks to be performed. Machine learning in insurance can take up such automated tasks to reduce errors and the time taken to process the claim.

To increase operational efficiency, companies have been adopting emerging technologies like AI, RPA, and the Internet of Things (IoT). The increase in connectivity, smart home assistants, fitness trackers, telematics, healthcare wearable devices, and other types of IoT devices, now allow insurers to stay connected and collect comprehensive data automatically. This data can then be imbibed into the underwriting process and claim management tasks that will help in better decision-making with reduced risks.

The process of underwriting was largely dependent on the data provided by the applicant manually by filling up regular forms. There is always a possibility of the applicant being dishonest or making mistakes that may lead to inaccurate risk assessment.

The rise in connectivity and increased use of IoT devices, help you fetch the larger datasets with correct information. Natural Language Processing (NLP) enables insurers to assess through the abstract resource to fetch apropos information to better assess the risk.

Research analysis shows that the benefits of the AI in insurance, specifically in underwriting, include the ability to:

The massive insurance industry collects approximately $ 1 trillion in premiums every year. With the size, the fraud ratio too is high. The total cost of non-health insurance fraud is estimated to be more than $40 billion per year that in turn increases the premium cost from $400 to $700 per year per family. Read below to understand how artificial intelligence in insurance claim fraud can be prevented.

Adding value to the existing process only makes sense if it brings along the visible benefits. AI in insurance brings a sigh of relief by revolutionizing it in many ways:

AI in insurance claims can handle the first notice of loss without or with minimal intervention from humans where insurers can report, route, triage, and assign claims. Chatbots can efficiently facilitate the claim reporting process as the customers can report their incidents from any device, any place, and at any time. The AI-enabled chatbots can further disperse the information for further processing.

By regulating all the processes of data capturing, claims creation, authorizations, approvals, payment tracking, and recovery tracking through AI, can be paired with other applications to streamline the fraud detection process, thus saving time and costs.

Artificial intelligence in insurance claims can reduce claims regulation costs by 20-30%, processing costs by 50-65%, and processing time by 50-90% while improving the customer service experience.

The power of Artificial Intelligence in the insurance industry has brought in a revolutionary change in the level of customer service. As mentioned above as well, chatbots are the easiest way to initiate the process and further disseminate the information to the next aligned process without human intervention making the process smooth, quick, and error-free.

AI-powered chatbots can cross-sell and upsell products based on the customers profile and history. By automating the repetitive process, scaling up the operations can be done easily while utilizing the human resources in more strategic roles.

With the advent of disruptive AI technology, Machine Learning, Deep Learning, and OCR, assessing the damage has become easier and quicker as the same can be easily done by uploading a picture of the damaged object.

Predicting the potential loss and providing recommendations make the loss estimation process quick and efficient.

With the power of AI, the fraud detection system addresses the shortcomings of hand-filled applications and provides valuable details on tips for better human judgments. Machine learning and deep learning algorithms are well equipped to identify repeated patterns that might be abnormal or fishy.

From the above-mentioned benefits and value adds, an inference can be drawn that there are fundamentally three areas in which artificial intelligence technology in insurance can bring the revolution claims process, risk assessment, and forecasting. It becomes easier to understand it with examples. Mentioned below are some of them:

Lemonade is an InsureTech startup that uses AI technology to run end-to-end insurance tasks. This has helped them in saving the operational costs that leverage them to offer reduced prices, increase customer acquisition, and elevate customer experience and engagement.

With AI, it lets the lenders assess the traditional and non-traditional data leveraging them to better gauge the risk. A better and automated underwriting process helps the company to boost profitability while reducing the risk.

Nauto is a driverless car company. Its aim is to avoid collision of the commercial fleets by reducing distracted driving. The AI-driven driver safety system uses the dual-facing camera, CV, and other algorithms to prevent risky behaviors in real-time

Implementing chatbots, NLP, and OCR are just the first step toward automation of the insurance industry. The pandemic has virtually forced us to adopt new technologies to stay in the business. This technological wave is certainly going to continue. Deep learning techniques and artificial intelligence are yet to be exploited to their potential. The scenarios will surely advance to machines mimicking the perception, reasoning, learning, and problem solving of the human mind,

It is expected that in the next decade, insurance will shift from its current state of detect and repair to predict and prevent. The users too are getting accustomed to using the advanced technologies to enhance productivity, lower costs, enhanced decision making, and enhance customer satisfaction.

The future of the insurance industry will take a steep curve to achieve new heights with the implementation of various AI technologies. It will not only impact the insurance companies but will also impact the people with insurance. Lets explore some of the trends:

We are experiencing this even today. With IoT, the number of various devices that are connected is increasing day by day. With AI, this connectivity will lead to the collection of comprehensive data. Understanding consumer behavior with this data will enable the insurance industries to come up with new product categories, more personalized pricing, and increasingly real-time service delivery.

Extended reality is the advanced form of virtual reality. It will not be necessary for the object to be insured to be physically present at the spot. Inspection will be done virtually with the help of AI technology after the claim is filed. It will be easier to provide better quotes based on the safety feature of the vehicle to be insured.

Data is the king in AI. Collecting the data from various sources and making sense of it is what AI technology is. However, ensuring that the data is precise and accurate will help in making better business decisions. Insurance companies can use accurate data to mitigate risks and frauds even before they take place.

Artificial intelligence in insurance is all set to transform the future of the insurance industry. Appinventiv can be your reliable development partner that helps you harness the benefits of automation in the insurance sector.

With our expertise in AI software development services. We have successfully helped businesses in transforming their business capabilities.

For instance, Appinventiv has successfully automated the banking process for a leading bank in Europe. The automation process helped the bank in improving the accuracy by 50% and the ATM service levels by 92% .

Also, with the help of conversational AI in banking, the client is able to handle over 50% of customer service requests through chatbot, thus reducing the manpower costs by 20%

You can also exploit the expertise of the seasoned team at Appinventiv to take a leap into the future of insurance.

AI is today and the future of insurance. Leveraging various tools of AI technology will automate the insurance processing from application state the claim settlement in no time that too with no human interventions. Saving this cost and time will help the insurance industry to come up with better product categories and personalized premium quotes that will be generated on data collected from various sources.

AI insurance is at a very nascent stage right now. It will change dramatically in the next decade.

FAQs

Q. What are the advantages of applying AI for Insurers and policyholders?

A. The benefits of applying AI for insurers are listed below-

The benefits of applying AI for policyholders are listed below-

Q. Which function of the insurance industry is expected to exploit AI in the future?

A. Although the impact of AI is holistic aiming to automate the process/functions to increase efficiency and save costs and time. However, with the use of predictive analytics, the underwriting process is the one that will adopt it to the maximum.

Q. What are the emerging AI use cases for auto insurance?

A. Primarily following are the emerging AI use cases for auto insurance

Sudeep Srivastava

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AI in insurance - How is artificial intelligence impacting the insurance sector? - Appinventiv

Policymakers, technologists, and business leaders must work together to ensure that the prosperity from artificial intelligence is shared throughout society and the unintended harms are addressed and mitigated, said experts at the U.S. Chamber AI Commission field hearing in Palo Alto, CA.

Were seeing a growth in AI systems that can function across multiple domains for the last decade...this can lead to unanticipated and harmful outcomes, said Rep. Anna Eshoo (CA-18), kicking off the hearing with words of caution. Policymakers, researchers, and leaders in the private sector need to collaborate to address these issues to ensure that AI advancement accrues to the benefit of society, not at the cost of it.

She added, As AI becomes more powerful, we have to keep refocusing technological development on our values to ensure that technology improves society. Many experts testifying throughout the hearing echoed similar points, advocating for widening the shared prosperity that would result from AI and cautioning the Commission on AIs potential harm to workers and marginalized communities.

1/2Rep. Anna Eshoo (CA-18) and Rep. Ro Khanna (CA-17) provided remarks at the U.S. Chamber AI Commission field hearing in Palo Alto, CA, on May 9, 2022.

2/2Rep. Anna Eshoo (CA-18) and Rep. Ro Khanna (CA-17) provided remarks at the U.S. Chamber AI Commission field hearing in Palo Alto, CA, on May 9, 2022.

Erik Brynjolfsson, Senior Fellow at the Stanford Institute for Human-Centered AI (HAI) and Director of the Stanford Digital Economy Lab, articulated the difference between automation and augmentation when it comes to jobs: Economists have made a distinction between economic substitute and economic complement, he testified. Substitutes tend to worsen economic inequality and increase concentration of economic and political power.

Moreover, he stressed that Most of the progress over time has come not from automating things we are already doing, but from doing new things...When technology complements humans...it increases wages and leads to more widely shared prosperity.

Katya Klinova, Head of Al, Labor and the Economy at The Partnership on AI, also advocated for the path of AI augmenting and complementing the skills of a much broader group of workers, making them more valuable for the labor market, boosting their wages, improving economic inclusion, and ultimately creating a more competitive economy, she said.

The regular discourse is overwhelmingly focused on how workers should prepare for the age of AI, and how governments and institutions can help them to prepare, Klinova testified. By putting all the burden of adjustment on the workers and the government, we are forgetting that the technology too can and should adjust to the needs and realities faced by communities and the workforce.

However, The issue is that in practice, it is often quite difficult to tell apart worker-augmenting technologies from worker-replacing technologies. Because of that, Klinova asserted, any company today that wants to claim their technology augments workers can just do it. Its a free-for-all claim that is not necessarily substantiated by anything.

Alka Roy, Founder of the Responsible Innovation Project and RI Labs, underscored a trust gap that results from this kind of discrepancy between having best practices, audits, and governance, and how and where they are actually used. Some reports...cite that even companies that have AI principles and ethics, only 9% to 20% of them publicly admit to having operationalizing these principles, Roy said.

To address these issues, Klinova advocated for invest[ing] in alternative benchmarks...and in building institutions that allow for empowered participation of workers in the development and deployment of AI. Adding that, Workers are ultimately the best people to tell apart which technologies help them and make their day better, and which ones look good on paper in marketing materials, but in practice enable exploitation or over surveillance.

In talking about the impact of AI on workers, Doug Bloch, Political Director at Teamsters Joint Council 7, referenced his time serving on Governor Newsoms Future of Work Commission, I became convinced that all the talk of the robot apocalypse and robots coming to take workers jobs was a lot of hyperbole. I think the bigger threat to the workers I represent is the robots will come and supervise through algorithms and artificial intelligence.

We have to empower workers to not only question the role of technology in the workplace, but also to use tools such as collective bargaining and government regulation to make sure that workers also benefit from its deployment, he said.

In his testimony, Bloch emphasized that workers arent afraid of technology, but they will question its purpose and make sure that its regulated, and that workers have a voice in the process. The biggest question for organized labor and worker advocates right now...is how does all of this technology relate to production standards, to production, and to discipline?

Bloch referenced an existing contract to show how AI and labor may co-exist. Terms provided a safety net for workers by ensuring that they cant be fired by surveillance technology or an algorithm. A supervisor has to directly observe dishonest behavior to allow a firing. He also underlined the importance that the data workers generate, which helps to inform decisions and increase profits for the company, won't be used against them.

Bloch closed by stating, If the fight of the last century was for workers to have unions and protections like OSHA, I honestly believe that the fight of this century for workers will be around data, and that workers should have a say in what happens with it and to share in the profit with it.

Jacob Snow, Staff Attorney for the Technology and Civil Liberties Program at the ACLU of Northern California, told the Commission that the critical discussions on AI are, not narrow technical questions about how to design a product. They are social questions about what happens when a product is deployed to a society, and the consequences of that deployment on peoples lives.

He explained why he believed facial recognition should be on the other side of the technological red line: There are applications of facial recognition, which I think at least officially seem like they might be valuable finding a missing person or tracking down a dangerous criminal, for example. But...any tool that can find a missing person, can find a political dissident. Any tool that can pick a criminal out of a crowd, can do same for an undocumented person or a person who has received reproductive healthcare. He cautioned, Were living in a time when its not necessary for civil rights and privacy advocates to say just imagine if the technology fell into the wrong hands. Its going directly into the wrong hands after its been built.

We can think a little bit more broadly about what constitutes AI regulation worker protections, housing support, private laws all those frameworks put in place deeper social, health-related, and economic protections that limit the harm about algorithms, Snow testified.

Rep. Ro Khanna (CA-17), who provided concluding remarks, talked about the disparate impacts that AI will have in different communities across the United States. This challenge is the central challenge for the country: How do we both create economic opportunity in places that have been totally left out, how do we build and revitalize a new middle class, and how do we have the benefits of technology be more widely shared? In summary, the Congressman stated, There's going to be 25 million of these new jobs in every field from manufacturing to farming to retail to entertainment. The question is, how do we make sure that they are a possibility for people in every community?"

To continue exploring critical issues around AI, the U.S. Chamber AI Commission will host further field hearings in the U.S. and abroad to hear from experts on a range of topics. The next hearing will be held in London, UK, on June 13. Previous hearings took place in Austin, TX, and Cleveland, OH.

Learn more about the AI Commission here.

Director, Policy, U.S. Chamber of Commerce Technology Engagement Center (C_TEC)

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AI for All: Experts Weigh In on Expanding AI's Shared Prosperity and Reducing Potential Harms - uschamber.com