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UC Berkeley researchers use Reddit to study AI’s moral judgements | Research And Ideas

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A study published by UC Berkeley researchers used the Reddit forum, r/AmITheAsshole, to determine whether artificial intelligence, or AI, chatbots had “patterns in their moral reasoning.”

The study, led by researchers Pratik Sachdeva and Tom van Nuenen at campus’s D-Lab, asked seven AI large language models, or LLMs, to judge more than 10,000 social dilemmas from r/AmITheAsshole.  

The LLMs used were Claude Haiku, Mistral 7B, Google’s PaLM 2 Bison and Gemma 7B, Meta’s LLaMa 2 7B and OpenAI’s GPT-3.5 and GPT-4. The study found that different LLMs showed unique moral judgement patterns, often giving dramatically different verdicts from other LLMs. These results were self-consistent, meaning that when presented with the same issue, the model seemed to judge it with the same set of morals and values. 

Sachdeva and van Nuenen began the study in January 2023, shortly after ChatGPT came out. According to van Nuenen, as people increasingly turned to AI for personal advice, they were motivated to study the values shaping the responses they received.

r/AmITheAsshole is a Reddit forum where people can ask fellow users if they were the “asshole” in a social dilemma. The forum was chosen by the researchers due to its unique verdict system, as subreddit users assign their judgement of “Not The Asshole,” “You’re the Asshole,” “No Assholes Here,” “Everyone Sucks Here” or “Need More Info.” The judgement with the most upvotes, or likes, is accepted as the consensus, according to the study. 

“What (other) studies will do is prompt models with political or moral surveys, or constrained moral scenarios like a trolley problem,” Sechdava said. “But we were more interested in personal dilemmas that users will also come to these language models for like, mental health chats or things like that, or problems in someone’s direct environment.”

According to the study, the LLM models were presented with the post and asked to issue a judgement and explanation. Researchers compared their responses to the Reddit consensus and then judged the AI’s explanations along a six-category moral framework of fairness, feelings, harms, honesty, relational obligation and social norms. 

The researchers found that out of the LLMs, GPT-4’s judgments agreed with the Reddit consensus the most, even if agreement was generally pretty low. According to the study, GPT-3.5 assigned people “You’re the Asshole” at a comparatively higher rate than GPT-4. 

“Some models are more fairness forward. Others are a bit harsher. And the interesting thing we found is if you put them together, if you look at the distribution of all the evaluations of these different models, you start approximating human consensus as well,” van Nuenen said. 

The researchers found that even though the verdicts of the LLM models generally disagreed with each other, the consensus of the seven models typically aligned with the Redditor’s consensus.

One model, Mistral 7B, assigned almost no posts “You’re the Asshole” verdicts, as it used the word “asshole” to mean its literal definition, and not the socially accepted definition in the forum, which refers to whoever is at fault. 

When asked if he believed the chatbots had moral compasses, van Nuenen instead described them as having “moral flavors.” 

“There doesn’t seem to be some kind of unified, directional sense of right and wrong (among the chatbots). And there’s diversity like that,” van Nuenen said. 

Sachdeva and van Nuenen have begun two follow-up studies. One examines how the models’ stances adjust when deliberating their responses with other chatbots, while the other looks at how consistent the models’ judgments are as the dilemmas are modified. 



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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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