Effective January 1, 2023, New York City employers will be restricted from using artificial intelligence machine-learning products in hiring and promotion decisions. In advance of the effective date, employers who already rely upon these AI products may want to begin preparing to ensure that their use comports with the new law's vetting and notice requirements. The new law governs employers' use of "automated employment decision tools," defined as "any computational process, derived from machine learning, statistical modeling, data analytics, or artificial intelligence, that issues simplified output, including a score, classification, or recommendation, that is used to substantially assist or replace discretionary decision making for making employment decisions that impact natural persons." The law prohibits the use of such tools to screen a candidate or employee for an employment decision, unless it has been the subject of a "bias audit" no more than one year prior to its use. A "bias audit" is defined as an impartial evaluation by an independent auditor that tests, at minimum, the tool's disparate impact upon individuals based on their race, ethnicity, and sex.

Stephen Ritter is Chief Technology Officer at Mitek, a global leader in mobile deposit and digital identity verification solutions. When the director of the White House Office of Science and Technology Policy proposes an artificial intelligence (AI) bill of rights, you know the conversation is leaping from technologists' private discussions to mainstream thought. But, make no mistake, even though Facebook says it's backing away from the use of facial recognition, there's no turning back. In the coming years, AI-based systems are going to change everything about how we live and work. And, currently, there are no rules of the road.

Circa is excited to welcome Keith E. Sonderling, Commissioner, U.S. Equal Employment Opportunity Commission (EEOC) at 11 a.m. Circa collaborates with industry experts to provide monthly educational webinars focused on trends in, diversity, equity, and inclusion in the workplace, talent acquisition, and OFCCP compliance. One of the trends that continues to be seen for talent acquisition is redefining what top talent looks like as well as where and how employers are sourcing that talent. Employers are looking for new technologies and many are turning to AI to not only help with recruitment and retention but also to ensure they are making efficient and effective decisions. While AI has been around for a while, many employers still have some fear and unease with the use of AI because of the uncertainty of how it is used in the marketplace today.

Read on for CPW's highlights of the year's most significant events concerning biometric litigation, as well as our predictions for what 2022 may bring. One of the most critical consumer privacy statutes for biometric litigation has been Illinois' Biometric Information Privacy Act ("BIPA"), which regulates the collection, processing, disclosure, and security of the biometric information of Illinois residents. BIPA protects the "biometric information" of Illinois residents, which is any information based on "biometric identifiers" that identifies a specific person--regardless of how it is captured, converted, stored, or shared. Biometric identifiers are "a retina or iris scan, fingerprint, voiceprint, or scan of hand or face geometry." BIPA has found itself to be one of the most frequent targets for class actions, as it includes a private right of action with liquidated statutory damages, unlike many other data privacy statutes.

Carnegie Mellon University President Farnam Jahanian highlighted the collaboration among government, academia and industry that has propelled Pennsylvania's autonomous vehicle (AV) industry forward during an event Wednesday outlining new legislation regulating AVs in the commonwealth. The legislation, unveiled at CMU's Mill 19 facility at Hazelwood Green by state Sen. Wayne Langerholc Jr., chairman of the Senate Transportation Committee; and Yassmin Gramian, secretary of the Pennsylvania Department of Transportation, would update Pennsylvania's policies around autonomous vehicles to mirror requirements in other states. Jahanian said that the global market for the autonomous vehicle industry will reach about $7 trillion dollars by 2050, with the potential to create countless jobs for workers of all education and skill levels. "While the economic impact of AV promises to be extraordinary, it also holds remarkable potential to enhance quality of life for citizens across the nation and contribute to solving significant societal challenges," Jahanian said, adding that benefits could include improvements to traffic safety and infrastructure maintenance and reductions in carbon emissions. He also noted that the technology's implications could extend to logistics, sustainability, medical care and expanding opportunities for independent living.

Planet Earth is rapidly growing warmer, and scientists are looking for different ways to predict the tipping points in climate change. The phenomenon of climate change is chaotic. For years, researchers and scientists have looked for successful methods of batting global climate change but were unable to find a solution as effective as AI. The integration of AI-powered systems has given a chance to environmentalists to address key issues, including threats to sustainability, food and water shortages, loss of biodiversity, climate change, and other environmental problems. AI paired with data sciences and machine learning can help find rigorous patterns to reduce and eradicate carbon footprints.

We interviewed Karine Perset, from the OECD Directorate for Science, Technology and Innovation in France about the informational pillars that make up strong AI governance for governments worldwide. She offered us numerous insights into how the OECD developed the AI Principles and works with governing bodies to design policies that will keep AI safe and trustworthy into the future. Additionally, we discuss which types of policies are necessary for which types of AI software as well as the scale at which these policies should govern. This could range from city to city at the local level as well as an international level as governments around the world continue to work on global standards for AI governance. In our interview with Perset, we focused on the differences between local, international, and global policies for AI systems and products.

Detroit, Michigan, Jan. 04, 2022 (GLOBE NEWSWIRE) -- Artificial Intelligence Technology Solutions, Inc., (OTCPK:AITX), today announced successful completion of their SOC 2 Type 1 examination. This achievement reflects the company's stated goals of best-in-class data protection and internal processes. The audit, conducted by Geels Norton LLC, confirms that AITX's, and its subsidiary's practices, policies, procedures, and operations meet the SOC 2 categories for security, availability, and confidentiality. "This achievement required a sizable amount of effort in documenting and tightening our processes, and completes our transition from being a small, casual organization into a modern corporation," said Steve Reinharz, CEO of AITX. "We've now satisfied many of America's foremost businesses that require their partners and vendors adhere to the highest standards regarding their data and security."

Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.

The rocket ship trajectory of a startup is well known: Get an idea, build a team and slap together a minimum viable product (MVP) that you can get in front of users. However, today's startups need to reconsider the MVP model as artificial intelligence (AI) and machine learning (ML) become ubiquitous in tech products and the market grows increasingly conscious of the ethical implications of AI augmenting or replacing humans in the decision-making process. An MVP allows you to collect critical feedback from your target market that then informs the minimum development required to launch a product -- creating a powerful feedback loop that drives today's customer-led business. This lean, agile model has been extremely successful over the past two decades -- launching thousands of successful startups, some of which have grown into billion-dollar companies. From facial recognition technology that has a bias against people of color to credit-lending algorithms that discriminate against women, the past several years have seen multiple AI- or ML-powered products killed off because of ethical dilemmas that crop up downstream after millions of dollars have been funneled into their development and marketing.