Tag:artificial intelligence
AI Data Centers and Rising Electric Bills
Electric bills are rising in many places, and the rapid expansion of AI data centers is adding new pressure to the power system. The big issue is how the electric grid pays for the infrastructure needed to serve rapidly growing electricity demand tied to AI. Serving that demand can require costly upgrades to the electric grid as well as securing additional electricity supply. When those costs are recovered through broadly applied rates instead of being assigned to the large new loads that triggered them, residential customers can see higher bills. State commissions and federal regulators influence these outcomes through tariffs, cost-allocation rules, and market design. As AI electricity use accelerates, questions of fairness and reliability have moved to the forefront of energy regulation.
To Compete Or Not to Compete: A Legal Question
Today, federal and state antitrust laws are as important as ever. However, modern courts struggle to apply the traditional interpretation and application of antitrust law to modern technology and related anti-competitive practices. This is particularly true in the realm of emerging technologies, where algorithms, automation, and artificial intelligence increasingly dominate. As a result, regulators face a host of unique challenges in an increasingly interconnected, data driven, and automated era. From business to finance, healthcare to housing, the importance of anti-competition law cannot be easily understated.
Work Related: AI Governance and Regulation in the Employment Law Context
Today, an explosion in Artificial Intelligence (AI) development is taking the U.S. and global economic systems by storm. Companies like Nvidia (the first company to reach an approximately 5 trillion valuation), Microsoft, Alphabet (Google), and Open AI (formerly a non-profit which still cites the common good as a core tenant of its charter) have kicked off what is widely understood to be an AI “Arms Race.” Investors- from venture capitalists to private equity behemoths- continue to pour billions of dollars into AI technology companies and associated ventures. As AI companies move from beta testing to widespread adoption and integration, debates on AI transparency, accountability, and regulation have risen to the forefront. As a result of this monumental shift and ongoing uncertainty, the necessity of properly understanding (and regulating) AI and automation technology is now more pressing than ever before. Further, the need for strong regulatory oversight, a broad regulatory consensus and clear guidance, a baseline code of ethics (at minimum), as well as strong federal and state regulation- has become one of the most important issues of our time.
America’s Fractured Approach to AI Regulation
Federal efforts to promote artificial intelligence (“AI”) innovation by avoiding comprehensive regulation has prompted state legislatures to fill the regulatory void, creating a fractured regulatory landscape. This threatens the very innovation AI was meant to create in a global race towards general AI. Today’s AI systems are examples of Artificial Narrow Intelligence, trained to perform specific tasks but are unable to operate outside their defined parameters. In contrast, Artificial General Intelligence, or Strong AI, is a theoretical form of AI capable of apply prior knowledge and skills to new contexts, enabling it to learn and perform any intellectual task a human can without additional human training of the underlying models. This pursuit has driven unprecedented investment, technology corporations have poured billions of dollars into AI capital expenditures with this number only continuing to rise. Compliance teams are left scrambling to manage an increasingly complex regulatory environment that is evolving faster than legal departments and regulators can effectively manage.
From Spreadsheets to Statutes: KPMG Enters into Law
The Arizona Supreme Court has approved the accounting firm Klynveld Peat Marwick Goerdeler (KPMG) to enter the practice of law. KMPG will be the first Big Four accounting firm to open its own law firm. This approval has created a stir in the legal community due to conflict and ethical compliance concerns. Although KPMG only has received approval in Arizona, there could be potential issues regarding conflicts, ethical challenges, and fair competition.
Will AI Make Trade Secrets No Longer Secret?
Most companies own valuable trade secrets, such as the recipe for Coca-Cola or Google’s algorithm. But can a company that develops AI have trade secrets? The Uniform Trade Secrets Act defines a trade secret as “information, including a formula, pattern, compilation, program, device, method, technique, or process,” that derives economic value and is the subject of efforts to maintain its secrecy. The protection of trade secrets is essential for companies to maintain their competitive edge and drive economic growth. As such, they are instrumental in both corporate governance and compliance. Companies already deal with the risks of employees using generative artificial intelligence (AI) and exposing trade secrets; however, recent AI regulations in Europe and the United States have further increased risks relating to trade secrets.
Generative AI- The Next Frontier in Fighting Financial Crime
Artificial intelligence (AI) is the latest tool in a financial institution’s arsenal to restrict the flow of money being channeled to fund illegal activities worldwide. As criminals get more innovative and sophisticated in using the latest technology to evade detection of their financial crimes, financial institutions must follow suit and utilize similar technology to root out these crimes or risk facing regulatory sanctions. Money laundering generally refers to financial transactions in which criminals, including terrorist organizations, attempt to disguise the proceeds of their illicit activities by making the funds appear to have come from a legitimate source. However, this is not a new phenomenon. Congress passed the Bank Secrecy Act (BSA) in 1970 to ensure financial institutions follow a set of guidelines known as KYC (Know Your Customer/Client) to detect and prevent money laundering through their systems.
Shein’s IPO: Stitching Profits with Controversy
In late 2023, fast-fashion retailer Shein filed to go public in the U.S. markets, which has been delayed because of tensions between the U.S. and China. On June 3, 2024, , which was predicted due to the delay in the U.S. markets. Although the company is well known its clothing prices and its value reported at $66 billion in 2023, the company faces controversy due to its ties to China, negative environmental impact, and alleged forced labor practices.
Who has ownership rights to AI generated content?
ChatGPT, like other generative AI technology, relies on what it’s “fed” when “spitting out” responses or data. For example, if ChatGPT briefs a case for a law student, this is because someone inputs all the relevant information into ChatGPT at an earlier time. If someone asks ChatGPT to brief that same case and another case in one response; the software would take the one case’s information from the place it was provided, and combines it with the information found in the other place where the second case was found. All in all, ChatGPT is limited in response to what it has been “told” at an earlier time. Think something like a Parrot. Parrots are well known as a species of bird that can repeat the sounds and words that someone says in their vicinity.
The I.R.S. is using AI to Crack Down on Tax Evasion
The Internal Revenue Service (I.R.S.) issued a press release on September 8, 2023, detailing how the agency plans to use at least part of the $80 million dollar allocation it received from the Inflation Reduction Act last year. I.R.S. Commissioner Danny Werfel plans to use the funds to make compliance enforcement efforts and tax evasion identification more effective and efficient. How does he plan to do this? The overwhelmed and perhaps overworked agency will be using artificial intelligence (AI) programs and features to expedite and assist with redundant processes as well as to audit parties that are too complicated or large for the I.R.S.’s current capabilities.