Large language models (LLMs) offer promising opportunities for organizational research. However, their built-in moderation systems can create problems when researchers try to analyze harmful content, often refusing to follow certain instructions or producing overly cautious responses that undermine validity of the results. This is particularly problematic when analyzing organizational conflicts such as microaggressions or hate speech. This paper introduces an Elo rating-based method that significantly improves LLM performance for harmful content analysis In two datasets, one focused on microaggression detection and the other on hate speech, we find that our method outperforms traditional LLM prompting techniques and conventional machine learning models on key measures such as accuracy, precision, and F1 scores. Advantages include better reliability when analyzing harmful content, fewer false positives, and greater scalability for large-scale datasets. This approach supports organizational applications, including detecting workplace harassment, assessing toxic communication, and fostering safer and more inclusive work environments.

All the sessions from Transform 2021 are available on-demand now. According to a new report released by the Pew Research Center and Elon University's Imaging the Internet Center, experts doubt that ethical AI design will be broadly adopted within the next decade. In a survey of 602 technology innovators, business and policy leaders, researchers, and activists, a majority worried that the evolution of AI by 2030 will continue to be primarily focused on optimizing profits and social control and that stakeholders will struggle to achieve a consensus about ethics. Implementing AI ethically means different things to different companies. For some, "ethical" implies adopting AI -- which people are naturally inclined to trust even when it's malicious -- in a manner that's transparent, responsible, and accountable. For others, it means ensuring that their use of AI remains consistent with laws, regulations, norms, customer expectations, and organizational values.

As algorithms become entrenched into society, the debate about their effects rages on. In essence, algorithms are sequences of instructions used to solve problems and perform functions in computer programming. As mathematical expressions, algorithms existed long before modern computers. While they vary in application, all algorithms have three things in common: clearly-defined beginning and ending points, discrete sets of "steps," and design meant to address a specific type of problem. On the one hand, algorithms play the role of prime suspect -- responsible for the recent UK pound's Brexit-induced flash crash, used for political and informational manipulation on social networks, and part of what Harvard Professor Shoshanna Zuboff calls "surveillance capitalism".

Elon Musk is worried about the perils of artificial intelligence. The year is 2030, and artificial intelligence has changed practically everything. Is it a change for the better or has AI threatened what it means to be human, to be productive and to exercise free will? You've heard the dire predictions from some of the brightest minds about AI's impact. Tesla and SpaceX chief Elon Musk worries that AI is far more dangerous than nuclear weapons.

Artificial intelligence, augmented reality and virtual reality are here to stay, but what impact will they have on jobs and training? A new study by Pew Research Center and Elon University's Imagining the Internet Center asked more than 1,400 technologists, futurists and scholars whether well-prepared workers be able to keep up in the race with artificial intelligence tools, and what impact this development will have on market capitalism. According to Elon University, most of the experts said they hope to see education and jobs-training ecosystems shift in the next decade to exploit liberal arts-based critical-thinking-driven curriculums; online courses and training amped up by artificial intelligence, augmented reality and virtual reality; and scaled-up apprenticeships and job mentoring. However, some expressed fears that education will not meet new challenges or -- even if it does -- businesses will implement algorithm-driven solutions to replace people in many millions of jobs, leading to a widening of economic divides and capitalism undermining itself. "The vast majority of these experts wrestled with a foundational question: What is special about human beings that cannot be overtaken by robots and artificial intelligence?" said Lee Rainie, director of internet, science and technology research at Pew Research Center and co-author of the report.