Technology

Scientists warn that AI encodes negative biases against black people

Published

3 months ago

September 4, 2024

A brand new study shows that sophisticated language models like ChatGPT are fraught with biases, including stereotypes about African Americans speaking English.

As more AI research delves into the inner workings of how the technology uses human language, as has develop into commonplace with innovations by OpenAI and other tech players, the anti-Black biases of those tools have gotten apparent.

Large language models (LLMs), corresponding to those utilized in Open AI’s ChatGPT, are subject to biases, in accordance with a paper published within the journal Laboratorium embedded of their programmingIn the article, the authors show that LLM used AND Alekt prejudice and perpetuate racial-linguistic stereotypes toward African-American English speakers, it was reported.

*In case you missed it*: This study shows that large-scale language (AI) models generate racist stereotypes about AAVE (African-American Vernacular English) speakers that are coupled with overtly negative stereotypes about black people, even once they are trained to not accomplish that. https://t.co/ZVTqsWOBoL

— uche blackstock, MD (@uche_blackstock) August 31, 2024

Models overwhelmingly associated AAVE with negative adjectives corresponding to dirty, silly, rude, ignorant, and lazy.

Remember, AI isn’t a panacea. It can reinforce systemic racism and other inequalities, resulting in further harm.

— uche blackstock, MD (@uche_blackstock) August 31, 2024

According to the article, “dialect bias can have harmful consequences: language models are more likely to suggest that AAE speakers will be assigned to less prestigious positions, punished for crimes, and sentenced to death.”

Nicole Holliday, a linguist on the University of California, Berkeley, said that the conclusions of the article should be heard and thoroughly understood.

“Anyone working on generative AI needs to understand this document,” Holliday also warned that while the businesses behind the LLM have tried to handle racial bias, “when bias is implicit… that’s something they haven’t been able to check.”

Despite efforts to correct racial biases in these language models, the biases remain. The authors of the paper argue that using human preference matching to handle racial biases only serves to cover the racism that these models maintain of their protocols.

As the article notes, “As the stakes of decisions entrusted to language models grow, so do concerns that they reflect or even reinforce human biases embedded in the data on which they were trained, thereby perpetuating discrimination against racial, gender, and other minority groups.”

The paper connects the potential biases of those LLMs against AAE speakers to real-world examples of discrimination. “For example, researchers have previously found that landlords engage in housing discrimination based solely on the auditory profiles of speakers, and that voices that sounded black or Chicano were less likely to secure employment in housing in predominantly white neighborhoods than in predominantly black or Mexican-American neighborhoods,” the report reads.

As the paper states, “Our experiments show that these stereotypes are similar to archaic human stereotypes about African Americans that predate the civil rights movement, are even more negative than the most negative human stereotypes about African Americans recorded experimentally, and differ both qualitatively and quantitatively from previously reported explicit racial stereotypes in linguistic models, indicating that they are a fundamentally different type of prejudice.”

The article also warned that, just as American society is becoming less overtly racist, attitudes ingrained within the subprocesses of AI programs will allow anti-black racism to persist inside more acceptable parameters for AI.

The authors of the paper proceed: “Disconcertingly, we also observe that larger language models and language models trained with HF exhibit stronger implicit but weaker explicit biases…There is therefore a real possibility that the allocational harm caused by dialectical biases in language models will become even more severe in the future, perpetuating racial discrimination experienced by generations of African Americans.”

This article was originally published on : www.blackenterprise.com

Technology

Flipkart co-founder Binny Bansal is leaving PhonePe’s board

Published

2 hours ago

November 22, 2024

IAM

Flipkart co-founder Binny Bansal has stepped down three-quarters from PhonePe’s board after making an identical move on the e-commerce giant.

Bengaluru-based PhonePe said it has appointed Manish Sabharwal, executive director at recruitment and human resources firm Teamlease, as an independent director and chairman of the audit committee.

Bansal played a key role in Flipkart’s acquisition of PhonePe in 2016 and has since served on the fintech’s board. The Walmart-backed startup, which operates India’s hottest mobile payment app, spun off from Flipkart in 2022 and was valued at $12 billion in funding rounds that raised about $850 million last 12 months.

Bansal still holds about 1% of PhonePe. Neither party explained why they were leaving the board.

“I would like to express my heartfelt gratitude to Binny Bansal for being one of the first and staunchest supporters of PhonePe,” Sameer Nigam, co-founder and CEO of PhonePe, said in a press release. His lively involvement, strategic advice and private mentoring have profoundly enriched our discussions. We will miss Binny!”

This article was originally published on : techcrunch.com

Technology

The company is currently developing washing machines for humans

Published

9 hours ago

November 22, 2024

IAM

Forget about cold baths. Washing machines for people may soon be a brand new solution.

According to at least one Japanese the oldest newspapersOsaka-based shower head maker Science has developed a cockpit-shaped device that fills with water when a bather sits on a seat in the center and measures an individual’s heart rate and other biological data using sensors to make sure the temperature is good. “It also projects images onto the inside of the transparent cover to make the person feel refreshed,” the power says.

The device, dubbed “Mirai Ningen Sentakuki” (the human washing machine of the longer term), may never go on sale. Indeed, for now the company’s plans are limited to the Osaka trade fair in April, where as much as eight people will have the option to experience a 15-minute “wash and dry” every day after first booking.

Apparently a version for home use is within the works.

This article was originally published on : techcrunch.com

Technology

Zepto raises another $350 million amid retail upheaval in India

Published

16 hours ago

November 21, 2024

IAM

Zepto, snagging $1 billion in 90 days, projects 150% annual growth

Zepto has secured $350 million in latest financing, its third round of financing in six months, because the Indian high-speed trading startup strengthens its position against competitors ahead of a planned public offering next yr.

Indian family offices, high-net-worth individuals and asset manager Motilal Oswal invested in the round, maintaining Zepto’s $5 billion valuation. Motilal co-founder Raamdeo Agrawal, family offices Mankind Pharma, RP-Sanjiv Goenka, Cello, Haldiram’s, Sekhsaria and Kalyan, in addition to stars Amitabh Bachchan and Sachin Tendulkar are amongst those backing the brand new enterprise, which is India’s largest fully national primary round.

The funding push comes as Zepto rushes so as to add Indian investors to its capitalization table, with foreign ownership now exceeding two-thirds. TechCrunch first reported on the brand new round’s deliberations last month. The Mumbai-based startup has raised over $1.35 billion since June.

Fast commerce sales – delivering groceries and other items to customers’ doors in 10 minutes – will exceed $6 billion this yr in India. Morgan Stanley predicts that this market shall be value $42 billion by 2030, accounting for 18.4% of total e-commerce and a pair of.5% of retail sales. These strong growth prospects have forced established players including Flipkart, Myntra and Nykaa to cut back delivery times as they lose touch with specialized delivery apps.

While high-speed commerce has not taken off in many of the world, the model seems to work particularly well in India, where unorganized retail stores are ever-present.

High-speed trading platforms are creating “parallel trading for consumers seeking convenience” in India, Morgan Stanley wrote in a note this month.

Zepto and its rivals – Zomato-owned Blinkit, Swiggy-owned Instamart and Tata-owned BigBasket – currently operate on lower margins than traditional retail, and Morgan Stanley expects market leaders to realize contribution margins of 7-8% and adjusted EBITDA margins to greater than 5% by 2030. (Zepto currently spends about 35 million dollars monthly).

An investor presentation reviewed by TechCrunch shows that Zepto, which handles greater than 7 million total orders every day in greater than 17 cities, is heading in the right direction to realize annual sales of $2 billion. It anticipates 150% growth over the following 12 months, CEO Aadit Palicha told investors in August. The startup plans to go public in India next yr.

However, the rapid growth of high-speed trading has had a devastating impact on the mom-and-pop stores that dot hundreds of Indian cities, towns and villages.

According to the All India Federation of Consumer Products Distributors, about 200,000 local stores closed last yr, with 90,000 in major cities where high-speed trading is more prevalent.

The federation has warned that without regulatory intervention, more local shops shall be vulnerable to closure as fast trading platforms prioritize growth over sustainable practices.

Zepto said it has created job opportunities for tons of of hundreds of gig employees. “From day one, our vision has been to play a small role in nation building, create millions of jobs and offer better services to Indian consumers,” Palicha said in an announcement.

Regulatory challenges arise. Unless an e-commerce company is a majority shareholder of an Indian company or person, current regulations prevent it from operating on a listing model. Fast trading corporations don’t currently follow these rules.

This article was originally published on : techcrunch.com