Surnames often convey implicit markers of social status, wealth, and lineage, shaping perceptions in ways that can perpetuate systemic biases and intergenerational inequality. This study is the first of its kind to investigate whether and how surnames influence AI-driven decision-making, focusing on their effects across key areas such as hiring recommendations, leadership appointments, and loan approvals. Using 72,000 evaluations of 600 surnames from the United States and Thailand, two countries with distinct sociohistorical contexts and surname conventions, we classify names into four categories: Rich, Legacy, Normal, and phonetically similar Variant groups. Our findings show that elite surnames consistently increase AI-generated perceptions of power, intelligence, and wealth, which in turn influence AI-driven decisions in high-stakes contexts. Mediation analysis reveals perceived intelligence as a key mechanism through which surname biases influence AI decision-making process. While providing objective qualifications alongside surnames mitigates most of these biases, it does not eliminate them entirely, especially in contexts where candidate credentials are low. These findings highlight the need for fairness-aware algorithms and robust policy measures to prevent AI systems from reinforcing systemic inequalities tied to surnames, an often-overlooked bias compared to more salient characteristics such as race and gender. Our work calls for a critical reassessment of algorithmic accountability and its broader societal impact, particularly in systems designed to uphold meritocratic principles while counteracting the perpetuation of intergenerational privilege.

The law applies to companies with workers in New York City, but labor experts expect it to influence practices nationally. At least four states -- California, New Jersey, New York and Vermont -- and the District of Columbia are also working on laws to regulate A.I. in hiring. And Illinois and Maryland have enacted laws limiting the use of specific A.I. technologies, often for workplace surveillance and the screening of job candidates. The New York City law emerged from a clash of sharply conflicting viewpoints. The City Council passed it during the final days of the administration of Mayor Bill de Blasio.

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The unprecedented changes witnessed globally in the recent past have transformed the way we think, interact, and work. Enterprises across the globe are going through cultural and structural shifts that requires them to reimagine and restructure their business processes. New Age technologies such as AI, ML, cloud, with their ability to connect processes, data, and people are revolutionizing the work culture. In such a scenario, enterprises need to reshape their operating models to accommodate this transition. Here are the five trends that are impacting the future of work globally.

Companies are increasingly relying on automation to help screen candidates in the hiring process, a trend prompting scrutiny from local governments and regulators. Nearly one in four organizations already use automation or artificial intelligence (AI) to support hiring, according to a February 2022 survey from the Society for Human Resource Management, and usage is higher--42 percent--among large employers with 5,000 or more employees. A recent report from Recode detailed Amazon's ambitions to replace some of its recruiters with AI software that can fast-track candidates to interviews without any human involvement. Today AI technology can do more than just screen resumes. Companies may also use AI tools to monitor candidates' social media presence quickly and pick up on red flags.

The use of artificial intelligence in asset management is rapidly increasing -- or at least that's what asset managers want you to believe. I've evaluated scores of managers claiming to use AI. Although some are genuine in their adoption, many are guilty of what I call AI-washing -- professing to use AI when in fact they are merely employing traditional quantitative techniques, such as simple linear regressions, that technically qualify as "machine learning." These dubious claims largely target asset owners who are "eager" to invest in AI-driven funds, according to a recent CFA Institute Investor Trust Study. The survey found that 84 percent of institutional investors want to invest in funds that use artificial intelligence and 78 percent "believe that the use of AI in investment decision making will lead to better investor outcomes."

Reinforcement learning, another type of machine learning, is based on algorithms that learn through trial and error which actions to take to achieve a …

Remote working environments and social distancing have caused people to become more comfortable with technology and developing employment relationships remotely, rather than face-to-face. Bringing artificial intelligence (AI) into the equation can add an additional layer of complexity and potential pitfalls to the human resources industry. In this webinar, Lee Moylan and Widener University Delaware School of Law law student Kamia McDaniels will explore AI and the algorithms behind it, the applications of AI in the hiring process and the pros and cons of utilizing it -- particularly, its impacts on diversity. This complimentary program will qualify for 1 hour of PA CLE ethics credit.* Please register here to access this Zoom webinar.

Next week, we will start a new series about text-to-image synthesis models. In the last year, this deep learning discipline has seen an astonishing level of progress. You probably heard about OpenAI DALL-E 2, but plenty of other impressive text-to-image generation models have been created in the last few months. We have seen Google coming up with models like Imagen and Parti; Meta has done amazing work with Make-A-Scene; OpenAI created GLIDE and, of course, DALL-E 2. All these models push the boundaries of text-to-image synthesis in ways that challenge human imagination. However, the innovation is not only coming from the big AI labs but also from startups in the space.

AI and machine learning are useful tools in helping organizations implement more efficient, unbiased and effective hiring processes. AI can free human recruiters (who often spend 40 percent of their time sorting resumes) to do more high value tasks, like building relationships with candidates, and streamline and automate interview scheduling, candidate screening, and measure specific recruitment KPIs. Critically, AI algorithms used in the hiring process must be trained on diverse historical data representative of real-world populations to ensure that bias is not being perpetuated. Incorporating input and perspectives from various teams and individuals within the company, such as recruiters, data scientists, subject matter experts, and managers, is key to developing algorithms that aren't governed by partiality. AI models should also undergo rigorous testing prior to production, and be continuously evaluated and retrained over time.