Shares of American Tower REIT inched 0.07% higher to $204.02 Monday, on what proved to be an all-around great trading session for the stock market, with the...

American Tower REIT stock rises Monday, still underperforms market - MarketWatch

American Tower (NYSE:AMT) has outperformed the market over the past 15 years by 1.23% on an annualized basis producing an average annual return of 13.57%. Currently, American Tower has a market capitalization of $95.04 billion.

Here's How Much $100 Invested In American Tower 15 Years Ago Would Be Worth Today By Benzinga

Shares of American Tower REIT slid 1.49% to $204.95 Wednesday, on what proved to be an all-around poor trading session for the stock market, with the S&P 500...

American Tower REIT stock falls Wednesday, underperforms market - MarketWatch

The Asian American Chamber of Commerce of Greater Philadelphia is ... when the team's lease at the Wells Fargo Center in South Philadelphia expires. A 20-story apartment tower above the proposed Center City arena was added to the plans in August.

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With their share prices down 44% and 24% from their all-time highs, American Tower (NYSE: AMT) and Tractor Supply (NASDAQ: TSCO) now offer investors the highest dividend yields th

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The city of Mendoza, Argentina, hosted the international event known as the "Feria de América," an industrial and continental exhibition that took place from January to April 1954. To provide a suitable setting for the exhibition,

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After years of planning and preparation, with part of the construction process delayed by the COVID-19 pandemic, the Native American garden opened on campus, across from the HUB in the University of California,

Native American garden opens on campus

American Tower

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In this series we’ll discuss the role of both operational and information technology in critical infrastructure from the point of view of an electric utility. We’ll then explore the role that Enterprise Resource Planning plays in this context. In doing so, we’ll demonstrate that, in addition to being an integral component of business operations, ERP systems also contain mission critical applications, crucial to maintaining safe and reliable critical infrastructure. We’ll expose the issues IT and Cybersecurity teams face in maintaining and protecting ERP systems, including a lack of transparency with business units, complexity as a result of high customization, and, most importantly, a persistent lack of adequate resources to maintain healthy and performant systems. Finally, we’ll demonstrate how a dedicated team of risk professionals can right these wrongs, gain a windfall of additional resources from federal and state governments, and build better, more transparent relationships with regulators. In the process, they’ll form lasting, collaborative relationships between IT and the rest of business, building a steadfast coalition dedicated to the highest purpose of all, greater public safety. ERP and Critical Infrastructure: A Series Electricity is the lifeblood of modern civilization, powering our homes, industries, and essential services. While electric, gas, and water utilities stand at the forefront, other forms of critical infrastructure include telecommunications, transportation systems, and healthcare facilities. Critical infrastructure refers to assets, systems, and networks foundational for our society to function. Electric utilities run on both Operational Technology (OT) and Information Technology (IT) networks. OT networks in electric utilities prioritize real-time operational functions, such as supervisory control and data acquisition (SCADA) systems and process control systems. These specialized networks facilitate the monitoring and control of physical devices and processes. IT networks, on the other hand, are the conventional user networks that the rest of the business uses to run its day-to-day operations and communicate with the outside world. Enterprise Resource Planning (ERP) systems are part of the IT network. They integrate various business processes, including finance, human resources, procurement, and inventory management. ERP systems streamline operations by providing a centralized platform for data management and decision-making, enhancing efficiency and resource utilization. … Can we safely and reliably provide electricity without it? That’s the question an electric utility asks itself when determining which of its assets are mission critical. In the context of an electric utility, and other critical infrastructure, mission critical assets are thought to live on the OT network. And they do. A failure or compromise of Operational Technology could result in the flooding of an entire city. This is because OT systems control the operation(s?) of dams. OT hydroelectric power generation systems exercise precise control over the flow of water, a process fundamental to achieving optimal power generation while maintaining safety standards and preserving environmental integrity. If compromised, such systems hold the potential to trigger a catastrophic event. The is provided by The Federal Energy Regulatory Commission (FERC) for utilities to determine the criticality of their tech-enabled dams based on the number of people at risk if compromised. flowchart below A failure or compromise of OT systems could also impact grid reliability, disrupting essential services and endangering public safety. OT bulk power systems control the power supply across North America. Due to the interconnected nature of the North American power grid, even a handful of utilities experiencing OT failures could have cascading effects throughout the entire region, or even across regions, causing widespread blackouts (region map below). This happened on November 9th, 1965, when a power failure in Ontario, Canada cut off power to over 30 million people in parts of Canada, New York, New Jersey and most of New England. And on August 14 and 15, 2003, the northeastern U.S. and southern Canada suffered the worst power blackout in history. Areas affected extended from New York, Massachusetts, and New Jersey west to Michigan, and from Ohio north to Toronto and Ottawa, Ontario. Approximately 50 million customers were impacted. So who regulates this? The Federal Energy Regulatory Commission (FERC) was chartered as a result of the Department of Energy Organization Act of 1977, signed by President Jimmy Carter. FERC serves as an independent agency within the US Department of Energy (DOE) that regulates the interstate transmission of electricity, natural gas, and oil. To maintain the reliability of the bulk power system, FERC reviews, approves, and enforces mandatory reliability standards developed by an organization called the North American Electric Reliability Corporation (NERC). FERC’s reliability jurisdiction primarily focuses on reliable operation of the grid. The aim is to ensure the real-time and day-to-day operations of the grid by developing and enforcing operational and business standards ( ). see “grid” as defined by FERC below According to FERC, grid reliability is based on two key elements: – A reliable power grid has the ability to withstand sudden electric system disturbances that can lead to blackouts. Reliable operation - Generally speaking, resource adequacy is the ability of the electric system to meet the energy needs of electricity consumers. This means having sufficient generation to meet projected electric demand. Resource adequacy OT systems are used to ensure “reliable operation” by isolating failures so they do not spread across the interconnected power grid. They are used to ensure “resource adequacy” by optimizing the distribution of electricity based on real-time demand and using automated load balancing to manage the flow of power to match consumption patterns, preventing overloading. OT systems also track the condition of equipment, analyzing performance data to predict potential failures, allowing proactive maintenance and protecting the lives of crew members in the field. They employ sensors and monitoring devices to detect faults, such as short circuits or equipment malfunctions that could cause disastrous and potentially deadly fires. And, In the event of emergencies like natural disasters, OT systems facilitate coordinated responses. They enable quick isolation of affected areas, rerouting power to minimize the impact of these life threatening events. A stable, protected, and defended electric utility is imperative to prevent disruptions that can have cascading effects on various sectors, including hospitals, elderly care homes, emergency response systems, and other critical infrastructure. When we think about IT technology in this context, we worry about adequate network segmentation to avoid cyber threats traversing from IT networks where they’re disruptive to OT networks where they’re potentially catastrophic (see illustration below). AT&T’s network segmentation But what if I told you that an attack on the IT network alone can also be catastrophic? That you could not safely and reliably provide electricity without ERP systems? And that these systems are far too often fragile, underprotected, under-resourced sitting ducks? More on that in part two

Enterprise Resource Planning & Critical Infrastructure

The digital yuan’s proposed impact on domestic Chinese and international financial markets is one that simply cannot be disputed. The has gone so far as to draw parallels between China’s original invention of paper currency to the digital yuan, proposing that it is a “re-imagination of money that could shake a pillar of American power.” Wall Street Journal The digital yuan certainly has implications for soft and hard power projections within the Great Power Competition (GPC) space, with significant potential to alter the dollar’s steadfast foundation within the global financial system. In the same vein, the digital yuan is also significantly poised to affect the way global anti-money laundering (AML) initiatives are enforced. As the People’s Republic of China (PRC) continues to grow its global presence, the hope is that its native central bank digital currency (CBDC) system will help the PRC increasingly enforce global AML standards. The simple truth is that the reality of a digital yuan may be much different, with the digital yuan having the potential to act as a money laundering mechanism for threat entities throughout the world. Money laundering in China Money laundering within the PRC has become a notable problem as China increasingly seeks to attain “developed country” status. The PRC has attempted to placate private and public leaders in the AML space in the past by making money laundering a crime in and passing its first anti-money laundering law in . More recent efforts have been represented through actions like China’s symbolic heading of the Financial Action Task Force (FATF) from and the PRC issuance of over $25.9 million dollars in AML-related fines in , a nearly 400% increase than was issued in 2019. 1997 2006 2019 to 2020 Q1 2020 Despite these efforts, the PRC has consistently remained a hub for significant money laundering activity on a global scale. China has been called the “world’s biggest source of ‘dirty’ funds,” with China accounting for nearly 50% of the $858.8 billion of illicit funds flowing into tax havens and western banks in . Spanish prosecutors went so far as to call the PRC-owned Industrial and Commercial Bank of China (ICBC), the world’s largest bank by assets, a “conduit for laundering tens of millions of euros in illegal funds from tax fraud and smuggling” in . Furthermore, make money laundering a relatively low-risk crime, providing tacit approval by the central government for individuals to engage in such activities. Additionally, while the PRC does exercise strict foreign currency controls and limited foreign exchange transactions on its citizens, informal remittance networks such as the 飛錢 ( ) system continue to allow for money laundering by criminal organizations and other threat actors. 2010 2017 weakness within the PRC’s judicial system flying money Money laundering remains a significant problem in China, and theoretically, the digital yuan will enable a government crackdown of such crimes. However, China’s CBDC may instead serve as a money laundering tool, enabling entities to transact without fear of foreign sanctions, taxes, or other governmental intervention. Why would the PRC endorse money laundering? The PRC’s official stance is that money laundering in China is unacceptable, with the PRC “fighting corruption, money laundering, illegal cash flows, and tax avoidance” as benefits of using the digital yuan. The PRC’s actions and partnerships reflect a much different reality, with the PRC’s ambitions for becoming the next world leader resulting in various actions that highlight the need for the digital yuan as a money laundering device. highlighting China’s with threat actors to include North Korea and Iran in the political, military, and economic realms support the use of the digital yuan to circumvent global AML initiatives. The implementation of the digital yuan would sustain the PRC’s relationships with these entities by enabling transactions between all parties, free from external government scrutiny through taxes, sanctions, or other forms of interference. The digital yuan’s centrally held status would act as a guarantee for these threat actors to conduct transactions, circumventing any AML regulations wholeheartedly. constant dealings Furthermore, another use case of the digital yuan is to solidify China’s status as a significant purveyor of illicit drugs such as and . Historically, the PRC has offered only “ ” cooperation in stopping cross-border drug smuggling, often refusing to cooperate with law enforcement around the world. With well-known drug distribution pipelines spanning every continent throughout the globe, Chinese drug traffickers could utilize the digital yuan to ensure cross-border payments without fear of interdiction by law enforcement entities. This ability to bypass AML initiatives showcases another way the digital yuan could be used to global AML initiatives. meth fentanyl limited Central bank digital currencies have many use cases, with compliance with global AML laws being a significant benefit of CBDCs. However, the PRCs authoritarian nature and implied approval of otherwise-banned transactions will create an environment where the digital yuan will be used to circumvent global AML initiatives. Recent history: modern cases of PRC money laundering The PRC is known to have utilized its state-owned banking structure to funnel money to threat actors to include and , among others. These cases represent just some of the known instances of the PRC utilization of its banks to support threat actors, with the full-fledged implementation of a PRC-backed CBDC serving only to further such efforts. Iran’s Quds Force North Korea’s nuclear development program Other significant examples of money laundering through digital means, with an emphasis on cryptocurrencies, include a case in , where Chinese nationals utilized cryptocurrencies, banks, and an informal Chinese network to launder over $30 million in drug profits for the Sinaloa Cartel. Another case in saw Chinese nationals laundering over $100 million dollars in stolen cryptocurrency from a 2018 North Korean cyberattack on cryptocurrency exchanges, with $68 million dollars of these funds flowing through nine Chinese banks. A third case In originated from the usage of popular Chinese e-commerce sites like Pinduoduo to make fraudulent purchases for the purpose of money laundering, with Chinese individuals making online purchases in amounts that were subsequently credited to their offshore gambling accounts. 2021 2020 2020 The PRC has often facilitated the movement of illicit cash between borders through its banks, with some of this money even being laundered through American banks, as was described by documents detailing such  transactions between North Korea, China, and the U.S. from , with this one known example amounting to over $174 million dollars. The implementation of a digital yuan would serve only to further ease such money laundering efforts between different threat actors and nations, with the PRC acting as the facilitator for such transactions. 2008 to 2017 Conclusion The digital yuan is being touted as one of the world’s first developed CBDCs, with one of its most significant features being monetary access to individuals typically unserved by the traditional banking system. Another of the digital yuan’s purported benefits is to enable the PRC to ensure further compliance with global anti-money laundering laws. However, it is doubtful that the status quo within the PRC will change, with the digital yuan instead acting as the perfect mechanism of transaction between different threat actors and groups. The PRC’s past facilitation of such enterprises provides evidence to this point, with the PRC regularly acting as a facilitator of illegal remittances, skirting international anti-money laundering initiatives for its own gain and influence within the realm of Great Power Competition.

The Digital Yuan is China's Biggest Pushback Against Global AML Initiatives

1) HyperLedger HyperLedger is an open source collaborative effort between enterprise institutions and corporations to support the development of , to date Hyperledger has including . HyperLedger’s goal is to form a consortium with the intention of supporting the development of enterprise-grade and cross-industry platforms to utilize open distributed ledger technology. Hyperledger can be thought of as an umbrella project that intends to supply the necessary framework, standards, and support necessary to build open sourced blockchains that become widely adopted. blockchain technology over 250 members Accenture, Deloitte, Fujitsu, Hitachi, Huawei, Intel, IBM, Moscow Exchange, SAP, Samsung SDS, Xiaomi, Cisco, and more 2) Ethereum Ethereum is a decentralized project backed by a consortium known as the Ethereum Enterprise Alliance (EEA) that is joined by corporations and institutions such as _Accenture, BlockApps, BNY Mellon, CME Group, ConsenSys, IC3, Intel, J.P. Morgan, Microsoft, Nuco, Banco Santande_r and many more. Since EEA’s inception in 2017, the consortium has added into their fray. EEA’s goal is to connect Fortune 500 companies, startups, and government institutions with Ethereum developers and experts, today the group has over . Mitsubishi UFJ, DTCC, Deloitte, Samsung SDS, Infosys, and the National Bank of Canada 300 members 3) Stellar Stellar is a blockchain project dedicated to connecting payment providers, banks, and people together. Backed by a strategic partnership with IBM, Stellar has become a popular rival to Ripple, another leading bank and financial services related blockchain project. The team behind Stellar have been busy and have collaborated with over to date including: . Stripe is Stellars most interesting partnerships as the payment system provides support to over 100,000 businesses including OpenTable and Warby Parker. expanding their clientele 48 organization SHIFT Markets, Lightyear, STRIPE, and Wanxiang Group 4) Ripple Ripple is a blockchain project designed to connect banks, payment providers, digital exchanges and corporations together. Ripple is the leading project to collaborate and form strategic partnerships with numerous banks around the world such as; . Focusing on growing a clientele of banks, Ripple has also begun to establish strong ties with payment processors, exchanges and remittance services such as Western Union. Commonwealth Bank of Australia, CBW Bank, Cross River Bank, Santander, UniCredit, UBS, ReiseBank, CIBC, National Bank of Abu Dhabi(NBAD), Standard Chartered, National Australia Bank (NAB), Siam Commercial Bank and Shanghai Huarui Bank 5) Aergo An upcoming blockchain platform Aergo aims to provide an enterprise-ready solution for business, large corporations, and institutions alike. Aergo is backed by Blocko — a commercial blockchain startup — recognized as South Korea’s #1 commercial private blockchain systems provider that has built an impressive list of partnerships over time. It’s important to note that companies are not merely testing blockchain technology but have actually implemented and are currently utilizing it in their operations, therefore Blocko is considered to be one of the most active and advanced enterprise blockchain solution providers in this space. 6) Vechain Vechain is a blockchain-as-a-service (BaaS) platform that focuses on alleviating problems in supply-chain management. Vechain has been in development since 2015 and has managed to team up with that include the likes of . The project has also established a partnership with China’s largest insurance company, the People’s Insurance Company of China (PICC). Here is a complete 24 companies Deloitte, Microsoft, Renault, PricewaterhouseCooper, DB Schenker, TCL communication, China Unicom, BMW and LV China list of VeChain’s partnerships 7) ICON A blockchain project based from South Korea that plans to hyper-connect private and public institutions by developing a decentralized network of communities that consists of Financial institutions, insurance, universities & hospitals. ICON, WAN and AION announced in December 2017 that the three independent projects will come together to , a advocacy group centered around promoting interoperability between the disparate networks. Up to date, ICON’s biggest partnership is with Line, the popular Japanese messaging app that has over 200 million users signed up for the social platform form the BIA 8) Qtum Qtum has established a strategic partnership with China’s Amazon Web Services (AWS) division. The partnership will allow AWS users to develop and launch smart contracts using an Amazon Machine Image (AMI). This strategic partnership between QTUM and AWS will allow blockchain startups to utilize Amazon’s hosting service to develop smart contracts. QTUM will be able to access Amazon’s database thus sharing important data between startups such as marketing, sales data, and technical resources. 9) OmiseGo Headquartered in Thailand, OmiseGo is a company that plans on providing an open source payment platform with a decentralized exchange that is built on top of Ethereum. Essentially, companies establishing a partnership with Omise, the parent company of OmiseGo will provide payment gateways for Thailand, Japan, and Singapore. Omise and OmiseGo are separate entities, however due to the strong establishment of the parent company OmiseGo has a long slew of merchants who are interested in utilizing their blockchain-based payment service. 10) Brave Brave software is a web browser that plans on revolutionizing the internet with an ad-blocking private browser that also rewards readers and content publishers. On April 18, 2018, the Dow Jones Media Group announced a strategic partnership with Brave. Popular sites such as the and became verified publishers that intend to utilize Brave’s Basic Attention Token (BAT) platform, a blockchain-based system that allows consumers and eventually, advertisers to pay publishers directly for their content. Barron’s MarketWatch 11) PowerLedger Power Ledger, a renewable energy-trading platform, has partnered with BCPG, a Thailand based electric utility company, to allow users to trade renewable energy from solar panels via use of a secure banking interface. The project has announced that it will also be collaborating with _Silicon Valley Power, City of Santa Clara, Helpanswers, and American PowerNe_t. This green-initiative has also received backing from the Australian government and was recently endorsed by billionaire, Richard Branson when the team won the 2018 Extreme Tech Challenge that was held on Branson’s Necker Island. If you know of any further projects that have strong enterprise partnerships and collaborations, feel free to leave a comment below so I can add them onto the list!

11 Blockchain Projects Flaunting High-Profile Partnerships & Collaborations

The Big Fat Compendium Of Russiagate Debunkery

THE NEW REPUBLIC

Poring through university admissions applications takes immense manual labor, and human bias is often the subject of questionable headlines because of poor decision-making. AI could solve this problem, but bias oversights are still plentiful enough to negatively impact a student’s future. How Is AI Used in College Admissions? AI does more than scan college applications to see if they meet requirements, though this is its primary application. Application Screening Algorithms are customizable depending on a university’s standards for seeking applications that meet the criteria. AI can scan numbers from standardized tests, identify the value of extracurricular commitments and judge this against coursework. Predictive Analytics Machine learning can compare previous applicants to incoming ones to judge their chances of success. This is helpful from an administrative perspective because it gives boards insights into potential retention rates in the coming years. The process becomes more accurate the longer a university uses AI. Personalized Communication During and after the application process, competent chatbots could be invaluable resources for students. If they have questions about submitting forms or what a prompt means, a chatbot has the knowledge to give helpful responses. Bias Mitigation AI algorithms are just as capable of defeating bias as formulating it. For years, humans have turned the other way to accept some applications even if they did not meet a metric. An AI will not. Aspects of the admissions process become more objective because it removes some chances for human-based bias to compromise the process. How Could AI Develop Bias? Universities are responsible for collaborating with AI engineers and data scientists to filter out the chance of bias. This is an around-the-clock job, as the algorithms constantly change as they learn from themselves. Data Bias These repetitions could cause data bias, which is one of the biggest oversights for admissions. This is when the dataset includes information that promotes harmful training, such as discriminating against certain demographics because of historical tendencies. Algorithmic Bias Algorithmic bias is another red flag. This occurs when criteria carry different weights throughout the training process. Is the model starting to prioritize test scores over GPA, or is it skewed to prefer families with alumni? Experts must adjust how the AI views each metric to ensure consistency after repeated learning cycles. The University of California made a landmark decision to remove standardized test scores from its considerations because of systemic racism and ableism causing disadvantages to students. Years of historical trends like this do not bode well for training AI because it will need to learn how to weigh this metric if necessary in some places. Additionally, the depth of the bias will take a lot of work to overcome. Feedback Loops This hints at how dangerous feedback loops can be. Depending on the learning environment AI overseers employ, the model may become more extreme in its outcomes. Positive reinforcement could favor students with specific backgrounds or demographics. Researchers see this bias problem impacting universities, but graduates will find this extends into job applications. The crossover is immense, proven when researchers enter fake but demographically distinct names in ChatGPT 3.5 against a real job posting. AI put names culturally associated with Black Americans lower than names tied to white men and Asian women. Emotional Gaps An estimated 90% of students with over a 4.0 GPA and flawless SAT scores still fail to get into America’s top 10 universities. Adding AI oversights could make matters worse or inconsistent. AI has limited contextual understanding of an applicant’s experiences with too much weight on quantitative interpretations. It may not know how to judge a compelling story in an application essay or students who have to apply using nontraditional methods. What Can Admissions Officials Do? Some universities have already been privy to biased AI, and avoiding these fiascos in the future is crucial for maintaining reputation and keeping applicants interested. What proactive and preventive measures can schools take to keep students safe and be judged accurately? Many AI enthusiasts wonder if curating historical data, potentially skewing it or making the past inaccurate, would improve modern bias. Diverse and representative training alongside continuous monitoring is a more proactive approach to designing bias-free AI for admissions. This includes input from inclusive, interdisciplinary populations and datasets to fill gaps in socioeconomic, ethnic, educational and demographic experiences. Incorporating bias detection and mitigation tools will complement these efforts. AI professionals should help university leaders become literate in AI bias. They can offer workshops for noticing anomalies and providing feedback for further training. This helps if the admissions AI has integrated transparency, forcing it to explain where it derived its decisions. These insights are roadmaps for all AI users because they trace the source of problematic and biased information. Universities can also take a mixed approach to admissions, including human interactivity, to judge qualitative aspects. A recent study revealed an AI model was a poor predictor of educational outcomes because of its racial biases. It was incorrect at predicting the academic success of students with Asian backgrounds 73% of the time. AI can look at numbers effectively with minimal error, but humans may be better judges when reading personal statements or conducting interviews. Students in the Hands of AI Removing AI bias from college admissions is a deceptively critical issue, as poor data management could disrupt many students’ bright futures. Data scientists and university leaders must prioritize this, as the next-generation workforce relies on its accuracy and integrity to give them a chance at education. Neglect will cause numerous problems for all industries that need experienced professionals walking in the door. Poring through university admissions applications takes immense manual labor, and human bias is often the subject of questionable headlines because of poor decision-making. AI could solve this problem, but bias oversights are still plentiful enough to negatively impact a student’s future. Poring through university admissions applications takes immense manual labor, and human bias is often the subject of questionable headlines because of poor decision-making. AI could solve this problem, but bias oversights are still plentiful enough to negatively impact a student’s future. How Is AI Used in College Admissions? How Is AI Used in College Admissions? AI does more than scan college applications to see if they meet requirements, though this is its primary application. AI Application Screening Application Screening Algorithms are customizable depending on a university’s standards for seeking applications that meet the criteria. AI can scan numbers from standardized tests, identify the value of extracurricular commitments and judge this against coursework. Predictive Analytics Predictive Analytics Machine learning can compare previous applicants to incoming ones to judge their chances of success. This is helpful from an administrative perspective because it gives boards insights into potential retention rates in the coming years. The process becomes more accurate the longer a university uses AI. insights into potential retention rates Personalized Communication Personalized Communication During and after the application process, competent chatbots could be invaluable resources for students. If they have questions about submitting forms or what a prompt means, a chatbot has the knowledge to give helpful responses. Bias Mitigation Bias Mitigation AI algorithms are just as capable of defeating bias as formulating it. For years, humans have turned the other way to accept some applications even if they did not meet a metric. An AI will not. Aspects of the admissions process become more objective because it removes some chances for human-based bias to compromise the process. How Could AI Develop Bias? How Could AI Develop Bias? Universities are responsible for collaborating with AI engineers and data scientists to filter out the chance of bias. This is an around-the-clock job, as the algorithms constantly change as they learn from themselves. Data Bias Data Bias These repetitions could cause data bias, which is one of the biggest oversights for admissions. This is when the dataset includes information that promotes harmful training, such as discriminating against certain demographics because of historical tendencies. Algorithmic Bias Algorithmic Bias Algorithmic bias is another red flag. This occurs when criteria carry different weights throughout the training process. Is the model starting to prioritize test scores over GPA, or is it skewed to prefer families with alumni? Experts must adjust how the AI views each metric to ensure consistency after repeated learning cycles. The University of California made a landmark decision to remove standardized test scores from its considerations because of systemic racism and ableism causing disadvantages to students. Years of historical trends like this do not bode well for training AI because it will need to learn how to weigh this metric if necessary in some places. Additionally, the depth of the bias will take a lot of work to overcome. remove standardized test scores remove standardized test scores Feedback Loops This hints at how dangerous feedback loops can be. Depending on the learning environment AI overseers employ, the model may become more extreme in its outcomes. Positive reinforcement could favor students with specific backgrounds or demographics. Researchers see this bias problem impacting universities, but graduates will find this extends into job applications. The crossover is immense, proven when researchers enter fake but demographically distinct names in ChatGPT 3.5 against a real job posting. AI put names culturally associated with Black Americans lower than names tied to white men and Asian women. culturally associated with Black Americans lower culturally associated with Black Americans lower Emotional Gaps An estimated 90% of students with over a 4.0 GPA and flawless SAT scores still fail to get into America’s top 10 universities. Adding AI oversights could make matters worse or inconsistent. AI has limited contextual understanding of an applicant’s experiences with too much weight on quantitative interpretations. It may not know how to judge a compelling story in an application essay or students who have to apply using nontraditional methods. estimated 90% of students estimated 90% of students What Can Admissions Officials Do? Some universities have already been privy to biased AI, and avoiding these fiascos in the future is crucial for maintaining reputation and keeping applicants interested. What proactive and preventive measures can schools take to keep students safe and be judged accurately? Many AI enthusiasts wonder if curating historical data , potentially skewing it or making the past inaccurate, would improve modern bias. Diverse and representative training alongside continuous monitoring is a more proactive approach to designing bias-free AI for admissions. This includes input from inclusive, interdisciplinary populations and datasets to fill gaps in socioeconomic, ethnic, educational and demographic experiences. Incorporating bias detection and mitigation tools will complement these efforts. curating historical data AI professionals should help university leaders become literate in AI bias. They can offer workshops for noticing anomalies and providing feedback for further training. This helps if the admissions AI has integrated transparency, forcing it to explain where it derived its decisions. These insights are roadmaps for all AI users because they trace the source of problematic and biased information. Universities can also take a mixed approach to admissions, including human interactivity, to judge qualitative aspects. A recent study revealed an AI model was a poor predictor of educational outcomes because of its racial biases. It was incorrect at predicting the academic success of students with Asian backgrounds 73% of the time. AI can look at numbers effectively with minimal error, but humans may be better judges when reading personal statements or conducting interviews. students with Asian backgrounds 73% students with Asian backgrounds 73% Students in the Hands of AI Removing AI bias from college admissions is a deceptively critical issue, as poor data management could disrupt many students’ bright futures. Data scientists and university leaders must prioritize this, as the next-generation workforce relies on its accuracy and integrity to give them a chance at education. Neglect will cause numerous problems for all industries that need experienced professionals walking in the door.

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