Word embeddings have been shown to produce remarkable results in tackling a vast majority of NLP related tasks. Unfortunately, word embeddings also capture the stereotypical biases that are prevalent in society, affecting the predictive performance of the embeddings when used in downstream tasks. While various techniques have been proposed \cite{bolukbasi2016man, zhao2018learning} and criticized\cite{gonen2019lipstick} for static embeddings, very little work has focused on mitigating bias in contextual embeddings. In this paper, we propose a novel objective function for MLM(Masked-Language Modeling) which largely mitigates the gender bias in contextual embeddings and also preserves the performance for downstream tasks. Since previous works on measuring bias in contextual embeddings lack in normative reasoning, we also propose novel evaluation metrics that are straight-forward and aligned with our motivations in debiasing. We also propose new methods for debiasing static embeddings and provide empirical proof via extensive analysis and experiments, as to why the main source of bias in static embeddings stems from the presence of stereotypical names rather than gendered words themselves. All experiments and embeddings studied are in English, unless otherwise specified.\citep{bender2011achieving}.

Instruction-tuned LLMs can respond to explicit queries formulated as prompts, which greatly facilitates interaction with human users. However, prompt-based approaches might not always be able to tap into the wealth of implicit knowledge acquired by LLMs during pre-training. This paper presents a comprehensive study of ways to evaluate semantic plausibility in LLMs. We compare base and instruction-tuned LLM performance on an English sentence plausibility task via (a) explicit prompting and (b) implicit estimation via direct readout of the probabilities models assign to strings. Experiment 1 shows that, across model architectures and plausibility datasets, (i) log likelihood ($\textit{LL}$) scores are the most reliable indicator of sentence plausibility, with zero-shot prompting yielding inconsistent and typically poor results; (ii) $\textit{LL}$-based performance is still inferior to human performance; (iii) instruction-tuned models have worse $\textit{LL}$-based performance than base models. In Experiment 2, we show that $\textit{LL}$ scores across models are modulated by context in the expected way, showing high performance on three metrics of context-sensitive plausibility and providing a direct match to explicit human plausibility judgments. Overall, $\textit{LL}$ estimates remain a more reliable measure of plausibility in LLMs than direct prompting.

On Wednesday night, the actor Jeremy Allen White, of "The Bear," was working his way down a red carpet in Dallas. It was the première of "The White Claw," an A24 movie about the Von Erich clan of professional wrestlers. On the carpet, an "Entertainment Tonight" reporter informed White, "We just heard moments ago--the strike is over!" and stuck the mike in his face. "That's amazing," White said, seeming taken aback. Asked how he felt, he added, "I don't know the details of the deal, but I'm sure that SAG got what we wanted."

This story is part of Future Tense Fiction, a monthly series of short stories from Future Tense and Arizona State University's Center for Science and the Imagination about how technology and science will change our lives. Amanda sat at her desk, picking at the same $30 Little Gem salad she ordered daily, suffering a small burning sensation in her gut that was triggered either by acid reflux or the dying embers of her rapidly expiring conscience. Of course, it was standard procedure for her husband to demand that the security firm Dark Metal surveil potential new hires for any of his multibillion-dollar companies, but this was the first time Amanda had been involved in contracting the private intelligence agency herself. Seedlings is your venture, Reid had promised her, even though he'd named himself CEO. I want you to take the lead on this. Amanda was COO of Seedlings and reported to her husband, who dismissed Amanda's concerns about the legal ramifications of their actions. Worrying about the law was something poor people did, Reid insisted. Besides, she'd never seen Reid do anything that nefarious with this type of information. But Maggie Everett was the type of candidate that pleased Reid. Amanda had done her job, which was to find Maggie, and the people at Dark Metal had done theirs, which was to surveil her and create a comprehensive biographical profile. This seemed like overkill to Amanda. Maggie wasn't in the running to become a high-profile executive at one of Reid's billion-dollar firms. She was being interviewed to work at a preschool. Certainly, Seedlings differed from other private preschools--there was the possibility Maggie would be exposed to confidential information. But this was what NDAs were for. Unleashing a network of spies upon a poor teacher who would ultimately be responsible for 10 toddlers seemed like an absurd waste of resources. And this was just Phase 1. Phase 2 would have to wait until after Maggie was hired, of course. Amanda reopened Dark Metal's inch-thick dossier. The logline: Maggie was smart but stupid. Smart: She'd majored in English at Yale, then received an MFA in creative writing from Brown, and finally a master's in early childhood education from Columbia. Stupid: She'd accumulated $103,345 in student debt, which she'd never pay off unless she took a job somewhere like Seedlings.

If I'd known a movie version of Uncharted was soon coming out, I would have been a bit more guarded about admitting I'm a huge fan of the game to my editor. I am a fan, but I'm also a parent now, and I can't just leave the house on a whim for some entertainment. I need to hire a nanny to watch my kid, and like ordering popcorn and Twizzlers, that factors into the cost of catching a flick. This was this the first movie we've seen in a theater since Musa was born. I'm still waiting for the new Spider-Man to make it to streaming platforms, which is the only way my wife and I get to scratch our movie itch these days.

With artificial intelligence becoming more and more a part of our everyday lives, it is important to consider how this evolving technology will affect the way we parent. It seems that most discussions about parenting and AI are focused on privacy issues, but there are many other ways in which these two worlds intersect. Angela J. Kim is the creator of the popular lifestyle blog and podcast Mommy Diary, and the leading Asian-American voice on motherhood and lifestyle. Kim has worked with companies like Google, Microsoft and Amazon Alexa on their latest AI gadgets designed for families like Google family, Microsoft edge kids mode, and Alexa, and believes this will be a growing trend for families. "What I learned in the early 2000s when I started Mommy Diary, was that in the parenting relationship, it is the parents that need help more. I started Mommy Diary after going through severe postpartum depression from my first two kids and it was meant to be a community that helped the moms get better, mentally fit, and stronger," says Kim. "I knew that this was the only way they could parent better. Artificial intelligence engineers should take a similar perspective; we need more tools that help the parents learn, grow, and stay aware than we need tech for the kids" Angela's point is one that has been echoed by AI experts as well.

NEW DELHI--India's middle-class households are culling their armies of domestic helpers amid the Covid-19 pandemic, eliminating a crucial source of jobs and spurring an appliance-buying binge. Ila Rallan used to have five different home assistants troop through her apartment in Mumbai every day: one cook, three cleaners and one nanny. At the beginning of India's lockdown, they all returned to their villages. Additionally, her building wouldn't let in outsiders. For the first time in her life, she had to do everything around the house without the help of staff.

Dang robots are crummy at so many jobs, and they tell lousy jokes to boot. In two new studies, these were common biases human participants held toward robots. The studies were originally intended to test for gender bias, that is, if people thought a robot believed to be female may be less competent at some jobs than a robot believed to be male and vice versa. The studies' titles even included the words "gender," "stereotypes," and "preference," but researchers at the Georgia Institute of Technology discovered no significant sexism against the machines. There was only a very slight difference in a couple of jobs but not significant.

The plot revolves around a potential future where a group of "synths" who look identical to humans gain consciousness and seek liberation from their subservient societal roles as owned property. In the first season, the lead synth character Anita is assigned as a nanny to a family whose human mother struggles with alcoholism. After being away from the house a number of nights in a row, the mother comes home and tells her nursery-school aged daughter she'd like to read her a bedtime story. "I want Anita to read to me." Just a mic drop of one little girl's truth--that she prefers a robot nanny to her human mom.

A London-based start-up is aiming to become the'match.com "The relationship between child carers and parents is very intimate," said Cibej, who quit her six-figure salaried job with a global firm to set up the company in October 2017. "It's not like the normal employer-employee relationship; it requires compatibility on both a professional and emotional level. "Parents are essentially inviting someone into their home to attend to – and help raise – a person most precious to them. "It is essential that they be able to build trust and seamless communication with their nanny."