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Alan Turing Institute axes research on getting more women working in AI – what does this mean for EDI in STEM?

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The Alan Turing Institute (ATI) has scrapped an initiative aimed at increasing the number of women working in data science and AI, following government pressure.  

The research project, called Women in Data Science and AI, had been established to investigate the “chilly” organisational cultures of technology companies and improve diversity and equity in data science and AI, where women make up only 22 per cent of the workforce.

The government-funded institute had warned that the “persistent under-representation” of women and marginalised groups in these industries could lead to an amplification of bias in AI algorithms. However, these issues will no longer be addressed through the institute’s research.

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Technology secretary Peter Kyle wrote to ATI bosses earlier this month, reportedly directing them to prioritise research on defence and security instead. 

The institute reviewed more than 100 of its existing projects earlier this year, with the aim of focusing on a smaller number of science and innovation research initiatives.

A spokesperson for the ATI said: “We’re shaping a new phase for the Turing Institute, and this requires substantial organisational change to ensure we deliver on the promise and unique role of the UK’s national institute for data science and AI. As we move forward, we’re focused on delivering real-world impact across society’s biggest challenges.”

‘Setting the tone’

Between 40,000 and 60,000 women leave the UK’s tech sector every year, according to WeAreTechWomen’s Lovelace Report, costing the economy an estimated £2bn to £3.5bn annually.

Paul Sesay, CEO and founder of Inclusive Companies, an organisation that promotes diversity within UK businesses, said this was just one example of how a lack of diversity is “severely hampering progress and skewing the views and input of tech companies working in AI”. 

“We are at risk of alienating a whole range of people if we don’t embed EDI into AI,” he added, warning that this could have a “knock-on effect” within the big tech companies, which are already rolling back their EDI programmes.

Aggie Yemurai Mutuma, CEO and founder of workplace consultancy Mahogany Inclusion Partners, argued that, as a flagship body, the ATI “sets the tone” for the technology sector. “By recommitting now, it can protect the UK’s talent pipeline, inspire other institutions to stay the course and show the world that equity and excellence go hand in hand,” she said.

However, Mutuma added: “If it steps back, universities, labs and start‑ups will feel pressure to do the same, slowing down the hard‑won progress we’ve made in the UK.” 

Geeta Nargund, chair of gender parity consultancy The Pipeline, agreed, highlighting that studies have shown that women make up less than a third of the workforce in the AI sector, resulting in the technology being “riddled with gender bias”. 

“That is exactly why projects such as the ATI’s initiative are so essential, not only to address this issue but work together to fix it – for the sake of the many younger women who may be incredibly passionate and talented, yet will feel a profession in STEM is out of reach to them, and unfairly have their dreams fade,” she said.

Ongoing backlash against EDI

The decision comes amid a wider rollback of EDI policies in the technology sector. On taking office, US president Donald Trump ordered all government diversity schemes to be shut down and he has pushed for the private sector to follow suit. 

Meta and Amazon were among the first to scale back their EDI programmes in response. 

Mutuma said: “We’re already seeing UK politicians adopt the same ‘anti‑woke’ language used in the US and media outlets are picking up stories about bans on bias training. Some UK organisations are also pulling back from diversity programmes after seeing their US counterparts do the same.”

Recalling how Amazon was forced to scrap an algorithm that was being tested as a recruitment tool in 2018 after it was found to be sexist, she pointed out: “It only stopped discriminating against women because a diverse team spotted the bias before it caused wider harm. Without that scrutiny, hidden flaws multiply – and both the financial and reputational costs are huge.”

Last year, more than 180 ATI staff signed a letter criticising the organisation’s approach to diversity after it appointed four men to senior roles. In the letter, they wrote: “This is an excellent time to reflect on whether all voices are being heard and if the institute’s commitment to inclusivity is being fully realised in our recruitment and decision-making practices.”

Shakil Butt, founder of HR Hero for Hire, pointed out that embedding EDI within all industries is vital for avoiding “group think” among senior leaders and “navigating constant change” as well as helping to address talent shortages. 

“Giving permission to marginalised groups to be themselves and having psychological safety frees up everyone to focus on the work and be more productive,” he added.

For more information, read the CIPD’s bitesize research on delivering inclusion and diversity from the top down



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Databricks at a crossroads: Can its AI strategy prevail without Naveen Rao?

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“Databricks is in a tricky spot with Naveen Rao stepping back. He was not just a figurehead, but deeply involved in shaping their AI vision, particularly after MosaicML,” said Robert Kramer, principal analyst at Moor Insights & Strategy.

“Rao’s absence may slow the pace of new innovation slightly, at least until leadership stabilizes. Internal teams can keep projects on track, but vision-driven leaps, like identifying the ‘next MosaicML’, may be harder without someone like Rao at the helm,” Kramer added.

Rao became a part of Databricks in 2023 after the data lakehouse provider acquired MosaicML, a company Rao co-founded, for $1.3 billion. During his tenure, Rao was instrumental in leading research for many Databricks products, including Dolly, DBRX, and Agent Bricks.



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NFL player props, odds: Week 2, 2025 NFL picks, SportsLine Machine Learning Model AI predictions, SGP

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The Under went 12-4 in Week 1, indicating that not only were there fewer points scored than expected, but there were also fewer yards gained. Backing the Under with NFL prop bets was likely profitable for the opening slate of games, but will that maintain with Week 2 NFL props? Interestingly though, four of the five highest-scoring games last week were the primetime games, so if that holds, then the Overs for this week’s night games could be attractive with Week 2 NFL player props.

There’s a Monday Night Football doubleheader featuring star pass catchers like Nico Collins, Mike Evans and Brock Bowers. The games also feature promising rookies such as Ashton Jeanty, Omarion Hampton and Emeka Egbuka. Prop lines are usually all over the place early in the season as sportsbooks attempt to establish a player’s potential, and you could take advantage of this with the right NFL picks. If you are looking for NFL prop bets or NFL parlays for Week 2, SportsLine has you covered with the top Week 2 player props from its Machine Learning Model AI.

Built using cutting-edge artificial intelligence and machine learning techniques by SportsLine’s Data Science team, AI Predictions and AI Ratings are generated for each player prop. 

Now, with the Week 2 NFL schedule quickly approaching, SportsLine’s Machine Learning Model AI has identified the top NFL props from the biggest Week 2 games.

Week 2 NFL props for Sunday’s main slate

After analyzing the NFL props from Sunday’s main slate and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model AI says Lions receiver Amon-Ra St. Brown goes Over 63.5 receiving yards (-114) versus the Bears at 1 p.m. ET. Detroit will host this contest, which is notable as St. Brown has averaged 114 receiving yards over his last six home games. He had at least 70 receiving yards in both matchups versus the Bears a year ago.

Chicago allowed 12 receivers to go Over 63.5 receiving yards last season as the Bears’ pass defense is adept at keeping opponents out of the endzone but not as good at preventing yardage. Chicago allowed the highest yards per attempt and second-highest yards per completion in 2024. While St. Brown had just 45 yards in the opener, the last time he was held under 50 receiving yards, he then had 193 yards the following week. The SportsLine Machine Learning Model projects 82.5 yards for St. Brown in a 4.5-star pick. See more Week 2 NFL props here.

Week 2 NFL props for Vikings vs. Falcons on Sunday Night Football

After analyzing Falcons vs. Vikings props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model AI says Falcons running back Bijan Robinson goes Over 65.5 rushing yards (-114). Robinson ran for 92 yards and a touchdown in Week 14 of last season versus Minnesota, despite the Vikings having the league’s No. 2 run defense a year ago. The SportsLine Machine Learning Model projects Robinson to have 81.8 yards on average in a 4.5-star prop pick. See more NFL props for Vikings vs. Falcons here

You can make NFL prop bets on Robinson, Justin Jefferson and others with the Underdog Fantasy promo code CBSSPORTS2. Pick at Underdog Fantasy and get $50 in bonus funds after making a $5 wager:

Week 2 NFL props for Buccaneers vs. Texans on Monday Night Football

After analyzing Texans vs. Buccaneers props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model AI says Bucs quarterback Baker Mayfield goes Under 235.5 passing yards (-114). While Houston has questions regarding its offense, there’s little worry about the team’s pass defense. In 2024, Houston had the second-most interceptions, the fourth-most sacks and allowed the fourth-worst passer rating. Since the start of last year, and including the playoffs, the Texans have held opposing QBs under 235.5 yards in 13 of 20 games. The SportsLine Machine Learning Model forecasts Mayfield to finish with just 200.1 passing yards, making the Under a 4-star NFL prop. See more NFL props for Buccaneers vs. Texans here

You can also use the latest FanDuel promo code to get $300 in bonus bets instantly:

Week 2 NFL props for Chargers vs. Raiders on Monday Night Football

After analyzing Raiders vs. Chargers props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model AI says Chargers quarterback Justin Herbert goes Under 254.5 passing yards (-114). The Raiders’ defense was underrated in preventing big passing plays a year ago as it ranked third in the NFL in average depth of target allowed. It forced QBs to dink and dunk their way down the field, which doesn’t lead to big passing yardages, and L.A. generally prefers to not throw the ball anyway. Just four teams attempted fewer passes last season than the Chargers, and with L.A. running for 156.5 yards versus Vegas last season, Herbert shouldn’t be overly active on Monday night. He’s forecasted to have 221.1 passing yards in a 4.5-star NFL prop bet. See more NFL props for Chargers vs. Raiders here

How to make Week 2 NFL prop picks

SportsLine’s Machine Learning Model has identified another star who sails past his total and has dozens of NFL props rated 4 stars or better. You need to see the Machine Learning Model analysis before making any Week 2 NFL prop bets.

Which NFL prop picks should you target for Week 2, and which quarterback has multiple 5-star rated picks? Visit SportsLine to see the latest NFL player props from SportsLine’s Machine Learning Model that uses cutting-edge artificial intelligence to make its projections.





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In the News: Thomas Feeney on AI in Higher Education – Newsroom

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“I had an interesting experience over the summer teaching an AI ethics class. You know plagiarism would be an interesting question in an AI ethics class … They had permission to use AI for the first written assignment. And it was clear that many of them had just fed in the prompt, gotten back the paper and uploaded that. But rather than initiate a sort of disciplinary oppositional setting, I tried to show them, look, what you what you’ve produced is kind of generic … and this gave the students a chance to recognize that they weren’t there in their own work. This opened the floodgates,” Feeney said.

“I think the focus should be less on learning how to work with the interfaces we have right now and more on just graduate with a story about how you did something with AI that you couldn’t have done without it. And then, crucially, how you shared it with someone else,” he continued.



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