This paper's primary goal is to provoke thoughtful discussion about the relationship between bias and fundamental properties of large language models. We do this by seeking to convince the reader that harmful biases are an inevitable consequence arising from the design of any large language model as LLMs are currently formulated. To the extent that this is true, it suggests that the problem of harmful bias cannot be properly addressed without a serious reconsideration of AI driven by LLMs, going back to the foundational assumptions underlying their design.

The extensive scope of large language models (LLMs) across various domains underscores the critical importance of responsibility in their application, beyond natural language processing. In particular, the randomized nature of LLMs, coupled with inherent biases and historical stereotypes in data, raises critical concerns regarding reliability and equity. Addressing these challenges are necessary before using LLMs for applications with societal impact. Towards addressing this gap, we introduce REQUAL-LM, a novel method for finding reliable and equitable LLM outputs through aggregation. Specifically, we develop a Monte Carlo method based on repeated sampling to find a reliable output close to the mean of the underlying distribution of possible outputs. We formally define the terms such as reliability and bias, and design an equity-aware aggregation to minimize harmful bias while finding a highly reliable output. REQUAL-LM does not require specialized hardware, does not impose a significant computing load, and uses LLMs as a blackbox. This design choice enables seamless scalability alongside the rapid advancement of LLM technologies. Our system does not require retraining the LLMs, which makes it deployment ready and easy to adapt. Our comprehensive experiments using various tasks and datasets demonstrate that REQUAL- LM effectively mitigates bias and selects a more equitable response, specifically the outputs that properly represents minority groups.

The president speaks after meeting with AI experts in effort to manage its risks. The Biden administration is actively seeking to use artificial intelligence to promote a woke, progressive ideology with left-wing activists leading the effort, according to research from a conservative watchdog group. The American Accountability Foundation conducted research into the administration's plans for AI and is now warning in a memo that top U.S. officials under President Biden are seeking to inject "dangerous ideologies" into AI systems. "Under the guise of fighting'algorithmic discrimination' and'harmful bias,' the Biden administration is trying to rig AI to follow the woke left's rules," AAF president Tom Jones told Fox News Digital. "Biden is being advised on technology policy, not by scientists, but by racially obsessed social academics and activists. We're already seen the biggest tech firms in the world, like Google under Eric Schmidt, use their power to push the left's agenda. This would take the tech/woke alliance to a whole new, truly terrifying level."

Doctors believe Artificial Intelligence is now saving lives, after a major advancement in breast cancer screenings. A.I. is detecting early signs of the disease, in some cases years before doctors would find the cancer on a traditional scan. California's reparations task force is recommending as part of its set of proposals to make amends for slavery and anti-Black racism that state lawmakers address what it calls "racially biased" artificial intelligence used in health care. The task force, created by state legislation signed by Gov. Gavin Newsom in 2020, formally approved last weekend its final recommendations to the California Legislature, which will decide whether to enact the measures and send them to the governor's desk to be signed into law. The recommendations include several proposals related to health care, including some concerning medical artificial intelligence (AI), which the task force describes as "racially biased" and contributing to alleged systemic racism against Black Californians.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. For decades, researchers have used benchmarks to measure progress in different areas of artificial intelligence such as vision and language. Especially in the past few years, with deep learning becoming very popular, benchmarks have become a narrow focus for many research labs and scientists. But while benchmarks can help compare the performance of AI systems on specific problems, they are often taken out of context, sometimes to harmful results. In a paper accepted at the NeurIPS 2021 conference, scientists at University of California, Berkeley, University of Washington, and Google outline the limits of popular AI benchmarks.

As AI becomes more pervasive, AI-based discrimination is getting the attention of policymakers and corporate leaders but keeping it out of AI-models in the first place is harder than it sounds. According to a new Forrester report, Put the AI in "Fair" with the Right Approach to Fairness, most organizations adhere to fairness in principle but fail in practice. "Fairness" has multiple meanings: "To determine whether or not a machine learning model is fair, a company must decide how it will quantify and evaluate fairness," the report said. "Mathematically speaking, there are at least 21 different methods for measuring fairness." Sensitivity attributes are missing: "The essential paradox of fairness in AI is the fact that companies often don't capture protected attributes like race, sexual orientation, and veteran status in their data because they're not supposed to base decisions on them," the report said.

We're releasing an API for accessing new AI models developed by OpenAI. Unlike most AI systems which are designed for one use-case, the API today provides a general-purpose "text in, text out" interface, allowing users to try it on virtually any English language task. You can now request access in order to integrate the API into your product, develop an entirely new application, or help us explore the strengths and limits of this technology. Given any text prompt, the API will return a text completion, attempting to match the pattern you gave it. You can "program" it by showing it just a few examples of what you'd like it to do; its success generally varies depending on how complex the task is.

Artificial Intelligence (AI) is already re-configuring the world in conspicuous ways. Data drives our global digital ecosystem, and AI technologies reveal patterns in data. Smartphones, smart homes, and smart cities influence how we live and interact, and AI systems are increasingly involved in recruitment decisions, medical diagnoses, and judicial verdicts. Whether this scenario is utopian or dystopian depends on your perspective. The potential risks of AI are enumerated repeatedly.

Facial recognition software that struggles to see black faces. A risk assessment algorithm with embedded racial biases. While artificial intelligence promises efficiency, and will likely determine which company wins market leadership, the technology also has an ugly side. Human hands can transfer prejudice onto the algorithms they create. But AI products don't become bias-infused at random, analysts and executives say.

Eminent industry leaders worry that the biggest risk tied to artificial intelligence is the militaristic downfall of humanity. But there's a smaller community of people committed to addressing two more tangible risks: AI created with harmful biases built into its core, and AI that does not reflect the diversity of the users it serves. I am proud to be part of the second group of concerned practitioners. And I would argue that not addressing the issues of bias and diversity could lead to a different kind of weaponized AI. The good news is that AI is an opportunity to build technology with less human bias and built-in inequality than has been the case in previous innovations.