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[paper] Evidence of a social evaluation penalty for using AI

Technology
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  • Significance

    As AI tools become increasingly prevalent in workplaces, understanding the social dynamics of AI adoption is crucial. Through four experiments with over 4,400 participants, we reveal a social penalty for AI use: Individuals who use AI tools face negative judgments about their competence and motivation from others. These judgments manifest as both anticipated and actual social penalties, creating a paradox where productivity-enhancing AI tools can simultaneously improve performance and damage one’s professional reputation. Our findings identify a potential barrier to AI adoption and highlight how social perceptions may reduce the acceptance of helpful technologies in the workplace.

    Abstract

    Despite the rapid proliferation of AI tools, we know little about how people who use them are perceived by others. Drawing on theories of attribution and impression management, we propose that people believe they will be evaluated negatively by others for using AI tools and that this belief is justified. We examine these predictions in four preregistered experiments (N = 4,439) and find that people who use AI at work anticipate and receive negative evaluations regarding their competence and motivation. Further, we find evidence that these social evaluations affect assessments of job candidates. Our findings reveal a dilemma for people considering adopting AI tools: Although AI can enhance productivity, its use carries social costs.

  • Significance

    As AI tools become increasingly prevalent in workplaces, understanding the social dynamics of AI adoption is crucial. Through four experiments with over 4,400 participants, we reveal a social penalty for AI use: Individuals who use AI tools face negative judgments about their competence and motivation from others. These judgments manifest as both anticipated and actual social penalties, creating a paradox where productivity-enhancing AI tools can simultaneously improve performance and damage one’s professional reputation. Our findings identify a potential barrier to AI adoption and highlight how social perceptions may reduce the acceptance of helpful technologies in the workplace.

    Abstract

    Despite the rapid proliferation of AI tools, we know little about how people who use them are perceived by others. Drawing on theories of attribution and impression management, we propose that people believe they will be evaluated negatively by others for using AI tools and that this belief is justified. We examine these predictions in four preregistered experiments (N = 4,439) and find that people who use AI at work anticipate and receive negative evaluations regarding their competence and motivation. Further, we find evidence that these social evaluations affect assessments of job candidates. Our findings reveal a dilemma for people considering adopting AI tools: Although AI can enhance productivity, its use carries social costs.

    This apparent tension between AI’s documented benefits

    That is one hell of an assumption to make, that AI is actually a benefit at work, or even a documented one, especially compared to a professional in the same job doing the work themselves.

  • This apparent tension between AI’s documented benefits

    That is one hell of an assumption to make, that AI is actually a benefit at work, or even a documented one, especially compared to a professional in the same job doing the work themselves.

    A rudimentary quick Internet search will provide a good bit of the "AI benefits at work" documentation for which you seek. 🤷♂

  • This apparent tension between AI’s documented benefits

    That is one hell of an assumption to make, that AI is actually a benefit at work, or even a documented one, especially compared to a professional in the same job doing the work themselves.

    a benefit of ai is that its faster than a human. on the other hand, its can be wrong

  • This apparent tension between AI’s documented benefits

    That is one hell of an assumption to make, that AI is actually a benefit at work, or even a documented one, especially compared to a professional in the same job doing the work themselves.

    I think its honestly pretty undeniable that AI can be a massive help in the workplace. Not all jobs sure but using it to automate toil is incredibly useful.

  • Significance

    As AI tools become increasingly prevalent in workplaces, understanding the social dynamics of AI adoption is crucial. Through four experiments with over 4,400 participants, we reveal a social penalty for AI use: Individuals who use AI tools face negative judgments about their competence and motivation from others. These judgments manifest as both anticipated and actual social penalties, creating a paradox where productivity-enhancing AI tools can simultaneously improve performance and damage one’s professional reputation. Our findings identify a potential barrier to AI adoption and highlight how social perceptions may reduce the acceptance of helpful technologies in the workplace.

    Abstract

    Despite the rapid proliferation of AI tools, we know little about how people who use them are perceived by others. Drawing on theories of attribution and impression management, we propose that people believe they will be evaluated negatively by others for using AI tools and that this belief is justified. We examine these predictions in four preregistered experiments (N = 4,439) and find that people who use AI at work anticipate and receive negative evaluations regarding their competence and motivation. Further, we find evidence that these social evaluations affect assessments of job candidates. Our findings reveal a dilemma for people considering adopting AI tools: Although AI can enhance productivity, its use carries social costs.

    This kind of work I find very important when talking about AI adoption.

    I've been generating (the boring) parts of work documents via AI, and even though I put a lot of thought into my prompts and I reviewed and adjusted the output each time, I kept wondering constantly if people would notice the AI parts, and if that made me look either more efficient and 'complete' (we are talking about some template document where some parts seem to be designed to be repetitive), or lazy and disrespectful.
    Because it's for sure that my own trust in content and a person drops when I notice auto-generated parts, which triggers that I use AI in turn, and I ask it to summarise all that verbose AI generated content.
    I'm not sure that's how decoder-encoders are meant to work 🙂

  • Significance

    As AI tools become increasingly prevalent in workplaces, understanding the social dynamics of AI adoption is crucial. Through four experiments with over 4,400 participants, we reveal a social penalty for AI use: Individuals who use AI tools face negative judgments about their competence and motivation from others. These judgments manifest as both anticipated and actual social penalties, creating a paradox where productivity-enhancing AI tools can simultaneously improve performance and damage one’s professional reputation. Our findings identify a potential barrier to AI adoption and highlight how social perceptions may reduce the acceptance of helpful technologies in the workplace.

    Abstract

    Despite the rapid proliferation of AI tools, we know little about how people who use them are perceived by others. Drawing on theories of attribution and impression management, we propose that people believe they will be evaluated negatively by others for using AI tools and that this belief is justified. We examine these predictions in four preregistered experiments (N = 4,439) and find that people who use AI at work anticipate and receive negative evaluations regarding their competence and motivation. Further, we find evidence that these social evaluations affect assessments of job candidates. Our findings reveal a dilemma for people considering adopting AI tools: Although AI can enhance productivity, its use carries social costs.

    I don't think that people who use AI tools are idiots. I think that some of my coworkers are idiots and their use of AI has just solidified that belief. They keep pasting AI results to nuanced questions and not validating the response themselves.

  • This apparent tension between AI’s documented benefits

    That is one hell of an assumption to make, that AI is actually a benefit at work, or even a documented one, especially compared to a professional in the same job doing the work themselves.

    It's nice for hints while programming. But that's mostly, because search engines suck.

  • 146 Stimmen
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    venusaur@lemmy.worldV
    What LLM you using?
  • 872 Stimmen
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    softestsapphic@lemmy.worldS
    You know copying literal brushstrokes and traces identifiable from real artists is different than be8ng inspired, it's amazing the level of denial you cultists will self induce to keep it making sense. Your god is not valuable enough to give more rights than human beings. Sorry I don't care what techbro conmen told you. AI will never be a replacement for acrual creativity, and is already being legislated against properly in civilized countries.
  • 298 Stimmen
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    kolanaki@pawb.socialK
    Internet access should be a utility like electricity and water until all three, along with housing, medicine, and food, can be free to all.
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    F
    IMO stuff like that is why a good trainer is important. IMO it's stronger evidence that proper user-centered design should be done and a usable and intuitive UX and set of APIs developed. But because the buyer of this heap of shit is some C-level, there is no incentive to actually make it usable for the unfortunate peons who are forced to interact with it. See also SFDC and every ERP solution in existence.
  • 42 Stimmen
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    B
    Yesterday on reddit I saw a photo a patient shot over the shoulder of his doctor of his computer monitor. It had ChadGPT full with diagnosis requests. https://www.reddit.com/r/ChatGPT/comments/1keqstk/doctor_using_chatgpt_for_a_visit_due_to_knife_cut/
  • Everyone Is Cheating Their Way Through College

    Technology technology
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    L
    i can this for essay writing, prior to AI people would use prompts and templates of the same exact subject and work from there. and we hear the ODD situation where someone hired another person to do all the writing for them all the way to grad school( this is just as bad as chatgpt) you will get caught in grad school or during your job interview. might be different for specific questions in stem where the answer is more abstract,
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    D
    I bet every company has at least one employee with right-wing political views. Choosing a product based on some random quotes by employees is stupid.
  • Microsoft's AI Secretly Copying All Your Private Messages

    Technology technology
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    S
    Forgive me for not explaining better. Here are the terms potentially needing explanation. Provisioning in this case is initial system setup, the kind of stuff you would do manually after a fresh install, but usually implies a regimented and repeatable process. Virtual Machine (VM) snapshots are like a save state in a game, and are often used to reset a virtual machine to a particular known-working condition. Preboot Execution Environment (PXE, aka ‘network boot’) is a network adapter feature that lets you boot a physical machine from a hosted network image rather than the usual installation on locally attached storage. It’s probably tucked away in your BIOS settings, but many computers have the feature since it’s a common requirement in commercial deployments. As with the VM snapshot described above, a PXE image is typically a known-working state that resets on each boot. Non-virtualized means not using hardware virtualization, and I meant specifically not running inside a virtual machine. Local-only means without a network or just not booting from a network-hosted image. Telemetry refers to data collecting functionality. Most software has it. Windows has a lot. Telemetry isn’t necessarily bad since it can, for example, help reveal and resolve bugs and usability problems, but it is easily (and has often been) abused by data-hungry corporations like MS, so disabling it is an advisable precaution. MS = Microsoft OSS = Open Source Software Group policies are administrative settings in Windows that control standards (for stuff like security, power management, licensing, file system and settings access, etc.) for user groups on a machine or network. Most users stick with the defaults but you can edit these yourself for a greater degree of control. Docker lets you run software inside “containers” to isolate them from the rest of the environment, exposing and/or virtualizing just the resources they need to run, and Compose is a related tool for defining one or more of these containers, how they interact, etc. To my knowledge there is no one-to-one equivalent for Windows. Obviously, many of these concepts relate to IT work, as are the use-cases I had in mind, but the software is simple enough for the average user if you just pick one of the premade playbooks. (The Atlas playbook is popular among gamers, for example.) Edit: added explanations for docker and telemetry