Mediaboss Marketing

May 23, 2022

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On May 12, 2022, more than six months after the Equal Employment Opportunity Commission (EEOC) announced its Initiative on Artificial Intelligence and Algorithmic Fairness,[1] the agency issued its first guidance regarding employers use of Artificial Intelligence (AI).[2]

The EEOCs guidance outlines best practices and key considerations that, in the EEOCs view, help ensure that employment tools do not disadvantage applicants or employees with disabilities in violation of the Americans with Disabilities Act (ADA). Notably, the guidance came just one week after the EEOC filed a complaint against a software company alleging intentional discrimination through applicant software under the Age Discrimination in Employment Act (ADEA), potentially signaling more AI and algorithmic-based enforcement actions to come.

The EEOCs AI Guidance

The EEOCs non-binding, technical guidance provides suggested guardrails for employers on the use of AI technologies in their hiring and workforce management systems.

Broad Scope. The EEOCs guidance encompasses a broad-range of technology that incorporates algorithmic decision-making, including automatic resume-screening software, hiring software, chatbot software for hiring and workflow, video interviewing software, analytics software, employee monitoring software, and worker management software.[3] As an example of such software that has been frequently used by employers, the EEOC identifies testing software that provides algorithmically-generated personality-based job fit or cultural fit scores for applicants or employees.

Responsibility for Vendor Technology. Even if an outside vendor designs or administers the AI technology, the EEOCs guidance suggests that employers will be held responsible under the ADA if the use of the tool results in discrimination against individuals with disabilities. Specifically, the guidance states that employers may be held responsible for the actions of their agents, which may include entities such as software vendors, if the employer has given them authority to act on the employers behalf.[4] The guidance further states that an employer may also be liable if a vendor administering the tool on the employers behalf fails to provide a required accommodation.

Common Ways AI Might Violate the ADA. The EEOCs guidance outlines the following three ways in which an employers tools may, in the EEOCs view, be found to violate the ADA, although the list is non-exhaustive and intended to be illustrative:

Tips for Avoiding Pitfalls. In addition to illustrating the agencys view of how employers may run afoul of the ADA through their use of AI and algorithmic decision-making technology, the EEOCs guidance provides several practical tips for how employers may reduce the risk of liability. For example:

Enforcement Action

As previewed above, on May 5, 2022just one week before releasing its guidancethe EEOC filed a complaint in the Eastern District of New York alleging that iTutorGroup, Inc., a software company providing online English-language tutoring to adults and children in China, violated the ADEA.[11]

The complaint alleges that a class of plaintiffs were denied employment as tutors because of their age. Specifically, the EEOC asserts that the companys application software automatically denied hundreds of older, qualified applicants by soliciting applicant birthdates and automatically rejecting female applicants age 55 or older and male applicants age 60 or older. The complaint alleges that the charging party was rejected when she used her real birthdate because she was over the age of 55 but was offered an interview when she used a more recent date of birth with an otherwise identical application. The EEOC seeks a range of damages including back wages, liquidated damages, a permanent injunction enjoining the challenged hiring practice, and the implementation of policies, practices, and programs providing equal employment opportunities for individuals 40 years of age and older. iTutorGroup has not yet filed a response to the complaint.

Takeaways

Given the EEOCs enforcement action and recent guidance, employers should evaluate their current and contemplated AI tools for potential risk. In addition to consulting with vendors who design or administer these tools to understand the traits being measured and types of information gathered, employers might also consider reviewing their accommodations processes for both applicants and employees.

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[1] EEOC, EEOC Launches Initiative on Artificial Intelligence and Algorithmic Fairness (Oct.28, 2021), available at https://www.eeoc.gov/newsroom/eeoc-launches-initiative-artificial-intelligence-and-algorithmic-fairness.

[2] EEOC, The Americans with Disabilities Act and the Use of Software, Algorithms, and Artificial Intelligence to Assess Job Applicants and Employees (May 12, 2022), available at https://www.eeoc.gov/laws/guidance/americans-disabilities-act-and-use-software-algorithms-and-artificial-intelligence?utm_content=&utm_medium=email&utm_name=&utm_source=govdelivery&utm_term [hereinafter EEOC AI Guidance].

[3] Id.

[4] Id. at 3, 7.

[5] Id. at 11.

[6] Id. at 13.

[7] Id. at 14.

[8] For more information, please see Gibson Dunns Client Alert, New York City Enacts Law Restricting Use of Artificial Intelligence in Employment Decisions.

[9] EEOC AI Guidance at 14.

[10] Id.

[11] EEOC v. iTutorGroup, Inc., No. 1:22-cv-02565 (E.D.N.Y. May 5, 2022).

The following Gibson Dunn attorneys assisted in preparing this client update: Harris Mufson, Danielle Moss, Megan Cooney, and Emily Maxim Lamm.

Gibson Dunns lawyers are available to assist in addressing any questions you may have regarding these developments. To learn more about these issues, please contact the Gibson Dunn lawyer with whom you usually work, any member of the firmsLabor and Employmentpractice group, or the following:

Harris M. Mufson New York (+1 212-351-3805, hmufson@gibsondunn.com)

Danielle J. Moss New York (+1 212-351-6338, dmoss@gibsondunn.com)

Megan Cooney Orange County (+1 949-451-4087, mcooney@gibsondunn.com)

Jason C. Schwartz Co-Chair, Labor & Employment Group, Washington, D.C.(+1 202-955-8242, jschwartz@gibsondunn.com)

Katherine V.A. Smith Co-Chair, Labor & Employment Group, Los Angeles(+1 213-229-7107, ksmith@gibsondunn.com)

2022 Gibson, Dunn & Crutcher LLP

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Keeping Up with the EEOC: Artificial Intelligence Guidance and Enforcement Action - Gibson Dunn

In financial services, it is important to gain any competitive advantage. Your competition has access to most of the same data you do, as historical data is available to everyone in your industry. Your advantage comes with the ability to exploit that data better, faster, and more accurately than your competitors. With a rapidly fluctuating market, the ability to process data faster gives you the opportunity to respond quicker than ever before. This is where AI-first intelligence can give you the leg up.

To implement AI infrastructure there are some key considerations to maximize your return on investment (ROI).

When designing for high utilization workloads like AI for financial analytics, it is best practice to keep systems on premise. On premise computing is more cost effective than cloud-based computing when highly utilized. Cloud service costs can add up quickly and any cloud outages inevitably leads to downtime.

You can leverage a range of networking options, but we typically recommend high speed fabrics like 100 gig Ethernet or 200 gig HDR InfiniBand.

You should also consider that the size of your data set is just as important as the quality of your model. So, you will want to allow for a modern AI focused storage design. This will allow you to scale as needed to maximize your ROI

It is also important to keep primary storage close to on premise computing resources to maximize network bandwidth while limiting latency. Keeping storage on premise also keeps your sensitive data safe. Let us look at how storage should be set up to maximize efficiency.

Traditional storage, like NAS (Network Attached Storage), cannot keep up. Bandwidth is limited to around 10 gigabits per second, and it is not scalable enough for AI workloads. Fast local storage does not work for modern parallel problems because it results in constantly copying data in and out of nodes which clogs the network.

AI optimized storage should be parallel and support a single namespace data lake. This enables the storage to deliver large data sets to compute nodes for model training.

Your AI optimized storage must also support high bandwidth fabrics. A good storage solution should enable object storage tiering to remain cost effective, and to serve as an affordable long term scale storage option for regulatory retention requirements.

With AI and machine learning, you can significantly reduce the number of false positives, leading to higher customer satisfaction. Automating minor insurance claims can often now be done by AI, allowing employees to focus on larger and more complex issues.

AI can also be used to review claims or flag cases for more thorough, in-depth analysis by detecting potential fraud or human error. Regular tasks prone to human error can either be reviewed, or in many cases performed entirely by applications with AI, often increasing both efficiency and accuracy.

The chat bot today is different from years past. They are more advanced and can now often replace menial tasks or requests and assist customers looking for self-service, thereby reducing both call volume and length.

AI provides a new future to financial analytics, increasing your ROI and allowing your employees to use their time more efficiently.

Learn more in this webinar.

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Leveraging Artificial Intelligence in the Financial Service Industry - HPCwire

Leptospirosis, a disease that dogs can get from drinking water contaminated with Leptospira bacteria, can cause kidney failure, liver disease and severe bleeding into the lungs. Early detection of the disease is crucial and may mean the difference between life and death.

Veterinarians and researchers at the University of California, Davis, School of Veterinary Medicine have discovered a technique to predict leptospirosis in dogs through the use of artificial intelligence. After many months of testing various models, the team has developed one that outperformed traditional testing methods and provided accurate early detection of the disease. The groundbreaking discovery was published in Journal of Veterinary Diagnostic Investigation.

Traditional testing for Leptospira lacks sensitivity early in the disease process, said lead author Krystle Reagan, a board-certified internal medicine specialist and assistant professor focusing on infectious diseases. Detection also can take more than two weeks because of the need to demonstrate a rise in the level of antibodies in a blood sample. Our AI model eliminates those two roadblocks to a swift and accurate diagnosis.

The research involved historical data of patients at the UC Davis Veterinary Medical Teaching Hospital that had been tested for leptospirosis. Routinely collected blood work from these 413 dogs was used to train an AI prediction model. Over the next year, the hospital treated an additional 53 dogs with suspected leptospirosis. The model correctly identified all nine dogs that were positive for leptospirosis (100% sensitivity). The model also correctly identified approximately 90% of the 44 dogs that were ultimately leptospirosis negative.

The goal for the model is for it to become an online resource for veterinarians to enter patient data and receive a timely prediction.

AI-based, clinical decision making is going to be the future for many aspects of veterinary medicine, said School of Veterinary Medicine Dean Mark Stetter. I am thrilled to see UC Davis veterinarians and scientists leading that charge. We are committed to putting resources behind AI ventures and look forward to partnering with researchers, philanthropists, and industry to advance this science.

Leptospirosis is a life-threatening zoonotic disease, meaning it can transfer from animals to humans. As the disease is also difficult to diagnose in people, Reagan hopes the technology behind this groundbreaking detection model has translational ability into human medicine.

My hope is this technology will be able to recognize cases of leptospirosis in near real time, giving clinicians and owners important information about the disease process and prognosis, said Reagan. As we move forward, we hope to apply AI methods to improve our ability to quickly diagnose other types of infections.

Reagan is a founding member of the schools Artificial Intelligence in Veterinary Medicine Interest Group comprising veterinarians promoting the use of AI in the profession. This research was done in collaboration with members of UC Davis Center for Data Science and Artificial Intelligence Research, led by professor of mathematics Thomas Strohmer. He and his students were involved in the algorithm building. The center strives to bring together world-renowned experts from many fields of study with top data science and AI researchers to advance data science foundations, methods, and applications.

Reagans group is actively pursuing AI for prediction of outcome for other types of infections, including a prediction model for antimicrobial resistant infections, which is a growing problem in veterinary and human medicine. Previously, the group developed an AI algorithm to predict Addisons disease with an accuracy rate greater than 99%.

Other authors include Shaofeng Deng, Junda Sheng, Jamie Sebastian, Zhe Wang, Sara N. Huebner, Louise A. Wenke, Sarah R. Michalak and Jane E. Sykes. Funding support comes from the National Science Foundation.

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Using Artificial Intelligence to Predict Life-Threatening Bacterial Disease in Dogs - University of California, Davis

New York, May 23, 2022 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Artificial Intelligence In Drug Discovery Market Size, Share & Trends Analysis Report By Application, By Therapeutic Area, By Region And Segment Forecasts, 2022 - 2030" - https://www.reportlinker.com/p06277978/?utm_source=GNW

Artificial Intelligence In Drug Discovery Market Growth & Trends

The global artificial intelligence in drug discovery market size is expected to reach USD 9.1 billion by 2030. It is expected to expand at a CAGR of 29.4% from 2022 to 2030. The pandemic has made the adoption of AI more widespread in the pharma industry. AI and its related platforms aim to enhance medical imaging and diagnostics, management of chronic diseases, and drug designing. The overall human hours spent would be far more in comparison to the AI system scanning the same data, which reduces overall cost and is a more feasible approach.

The drug optimization and repurposing application segment held the largest revenue share in 2021.AI platforms help in the identification of target proteins for drugs to determine adverse events and possible side effects the drug can have.

Drug molecules can be repurposed to make them more effective and with minimum side effects. Portfolio drugs for a company can be altered and studied using AI platforms at a much faster pace so as to hasten new drug development.

The oncology therapeutic area segment accounted for the largest revenue share in 2021.The majority of the pharmaceutical companies are pairing up with AI start-ups to optimize their cancer research, which is still an unchartered territory and there is a lot to be discovered.

AI systems can identify cancer much earlier than a regular scan would indicate to even a thoroughly trained radiologist.This, in turn, can increase life expectancy and can also help identify markers to be studied for cancer research and drug development.

Patients can be prescribed treatments suited to their genetic composition.

North America dominated the market in 2021.Many tech companies are now investing their money and efforts toward the use of AI in pharmaceutical companies.

Companies like IBM, Microsoft, and other tech giants have formed collaborations with research institutes for faster drug development and clinical trials for multiple indications. Developing countries are also finding cost-effective measures to implement AI technology for their drug development and disease understanding.

Artificial Intelligence In Drug Discovery Market Report Highlights The drug optimization and repurposing application segment dominated the market and accounted for a revenue share of over 50.0% in 2021 By therapeutic area, the oncology segment held the largest revenue share of over 20.0% in 2021. The infectious diseases segment is expected to register the fastest growth rate during the forecast period Asia Pacific is expected to expand at the fastest CAGR of 32.2% from 2022 to 2030. This can be attributed to the increasing adoption of AI among the developing countries in this region as a means to understand diseases and aid drug discovery North America led the market and accounted for a revenue share of over 55.0% in 2021. The U.S. is significantly contributing to the regional market growth as the country has been the forerunner in the artificial intelligence technologyRead the full report: https://www.reportlinker.com/p06277978/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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Artificial Intelligence In Drug Discovery Market Size, Share & Trends Analysis Report By Application, By Therapeutic Area, By Region And Segment...

In the third of a series of blogs from our global offices, we provide a overview of key trends in artificial intelligence in the United Arab Emirates.

What is the UAEs strategy for Artificial Intelligence?

In 2017, the UAE appointed a Minister of State for Artificial Intelligence, H.E. Omar Bin Sultan Al Olama, and issued a national AI strategy seeking to become one of the world leaders in AI by 2031. The strategy sets out 8 objectives including building the UAEs reputation as an AI destination, developing an ecosystem for AI development and deployment, and providing strong governance and effective regulation of AI.

To implement the national AI strategy, the UAE established an Office for Artificial Intelligence, which is responsible for enhancing government performance by investing in AI technologies and tools for application across various sectors.

The UAE Council for Artificial Intelligence and Blockchain was also established to provide advice to the government on the adoption and use of AI, to design policies that promote an AI-friendly ecosystem, to advance research and to promote privatepublic collaboration in that space.

To develop local capabilities in AI and encourage the adoption of AI across government, the UAE has developed a national AI training program, the Artificial Intelligence Program, in collaboration with University of Oxford. This is targeted at government employees but also open generally to UAE residents.

Which are the leading UAE research institutions for Artificial Intelligence?

The worlds first dedicated AI university, the Mohamed Bin Zayed University for Artificial Intelligence or MBZUAI, was established in 2020. It is the first graduate-level, research-based university offering specialised graduate programmes and supporting applied research in AI.

What laws and regulations is the UAE developing for Artificial Intelligence?

There is no specific legislation governing AI or addressing the ethical and legal issues arising from the use of AI (such as liability, privacy, discrimination and data bias). However, recent sector-specific regulations, such as the Federal Data Protection Law, the DIFC Data Protection Law and the Health Data law, all deal with privacy implications of decisions made through machine learning tools. Dubai has implemented non-binding guidelines to provide some regulation and guidance on the development and ethical use of AI. Dubais Ethical AI Toolkit aims to support the development and use of AI in ways that is responsible, boosts innovation and delivers human benefit.

Which are the leading UAE companies for Artificial Intelligence?

The UAE has major industrial and manufacturing sectors, especially in the oil, gas and petrochemicals sector, which are currently the powerhouse of the national economy. By adopting the right AI technologies, these, and other related sectors, are looking to improve their productivity, quality, efficiency, and cost effectiveness.

There are several companies operating in this space in the UAE. One of the leading companies is Group 42 or G42 which is an artificial intelligence and cloud computing company founded in the UAE in 2018. The company is focused on research, development and deployment of AI technologies and partnering across a wide range of sectors including healthcare, finance, oil and gas, aviation and hospitality. The UAEs national oil company, ADNOC, and Dubais water and electricity provider, DEWA, and the flagship carrier, Emirates Airline, are already using AI to optimise their operations.

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Artificial Intelligence in the UAE - Lexology