Best AI Humanizer for Students

Generative AI is a tool that holds great promise. Used wisely, generative AI can save knowledge workers an extraordinary amount of time on rote and repetitive tasks like reviewing legal documents, iterating through versions of marketing copy, or combing through lines of code to find a pesky bug. Generative AI can summarize research, draft documents, analyze information, and produce actionable recommendations in a fraction of the time it takes humans. But generative AI can also hallucinate, mimic human biases, and confidently regurgitate faulty logic.

For knowledge workers, the question is: can generative AI improve our working lives, and, if so, how? The answer is a resounding yes, as long as people are involved, know what they’re doing, and organizations create thoughtful rules for AI use.

Successful application of generative AI in the workplace hinges on human-in-the-loop involvement and strong AI literacy. Workers can save time on rote and repetitive tasks, drafting documents, summarizing information, or searching for patterns in data, gaining more time to conduct client interviews, test new ideas, and solve gnarly problems. But generative AI isn’t perfect. Errors can be costly. It’s the human-in-the-loop who notices an error and fixes it. And that takes knowledge and skill. More experienced subject-matter experts will get better results from generative AI. Less-trained workers are less likely to question answers. Leaving flawed information unchecked can spread errors exponentially.

For knowledge workers, generative AI could make repetitive, tedious, and thankless tasks a thing of the past. But some of those same tasks are critical for less experienced workers to learn the ropes. Organizations must be intentional in using generative AI so that entry-level employees aren’t overlooked and continue to have opportunities for professional growth. Automating those tasks could save organizations money in the short-term, but might cost them big-time down the road. Organizations should revisit how they onboard and train junior employees so that they’re learning how to question outputs from generative AI, validate facts, and make informed decisions, rather than just rubber-stamping AI-produced outputs.

To make generative AI an asset, organizations must develop policies that outline expectations around validation, confidentiality, and responsibility. Generative AI can’t keep secrets. Workers must understand what information to exclude from prompts. While generative AI can provide recommendations, people should be responsible for making consequential decisions that impact others’ livelihoods, finances, health, and/or rights. Generative AI has the potential to increase productivity, but output does not equal outcomes. Organizations must determine how to measure success beyond just increased output and production. Organizations must consider how generative AI impacts their employees, and, in turn, how employees’ questions, concerns, and/or ideas might improve implementation. Investing in employee training around generative AI leads to fairer, safer, and more effective use.

GENERATIVE AI WILL CHANGE KNOWLEDGE WORK FOR THE BETTER — IF WORKERS REMAIN IN CHARGE

Generative AI is changing knowledge work because generative ai systems can create documents, generate summaries of academic papers, generate code and analyze data quickly. However, some people are worried that this ability to create answers and summaries quickly could replace the need for human workers. It is true that there is reason to consider this possibility, however, many experts believe that generative AI will help knowledge workers become more efficient at completing knowledge work, if organizations see it as something that assists them in making judgments and not as a substitute for human decision-making.

Much knowledge work has involved performing repetitive tasks that do not require creativity. Lawyers review legal documents, marketers produce different versions of messages and software developers search for bugs. Each of these processes can be greatly improved using AI. For example, lawyers can use AI to find all relevant contracts in a document set, marketers can use AI to produce multiple versions of a message, and software developers can use AI to identify what line of code caused an error. Once these repetitive activities are completed much more efficiently, time is freed-up for higher-value work such as conducting client interviews, testing hypotheses and developing solutions to complex problems. As a result, it is not just possible to produce more work, but it is also possible to develop better work.

However, efficiency in the production process is not the only way to measure success. Generative AI can be used to create false information, replicate existing biases and present arguments based on weak reasoning with excessive confidence. Therefore, employers must establish policies regarding when employees should verify the accuracy of information produced by AI. In addition, employers must determine how employees will protect confidential information and who will ultimately be accountable for decisions affecting people's health, financial well-being, civil rights and livelihoods. Ultimately, humans must always remain accountable for decisions that affect others' lives.

Another criticism of generative AI is that it will limit job opportunities for lower-skilled employees. This argument is valid. Many junior employees learn how to complete their duties by performing the same tasks that AI can automate. If organizations eliminate these jobs without establishing additional mechanisms to teach new employees how to perform skills required in the organization, then organizations may experience increased efficiency in the near term, but at the expense of depleting their future talent pool. Organizations should design new junior positions that allow employees to evaluate AI output, formulate questions related to the validity of the output and understand the mechanics of the work. An example of this would be a new analyst position where instead of simply approving a report written entirely by a machine, the new analyst is trained to question and test the claims presented in the report.

In addition to potentially limiting access to education and training through eliminating low-skill jobs, AI may also disproportionately benefit certain types of workers. Workers who possess strong domain-specific knowledge will likely be able to leverage generative AI capabilities to enhance their performance and productivity. On the other hand, workers who lack formal training or education may have difficulty detecting errors generated by machines. Employers must invest in providing educational resources and technical support for their employees as well as provide employees with an opportunity to participate in decisions related to the introduction of new technologies. Doing so would increase the effectiveness of technology adoption and ensure fairness among all stakeholders.

Generative A.I. Will Lead to Improved Knowledge Work, As Long As People Remain In Control

Generative A.I. is capable of performing knowledge work by performing document generation, research, coding, and other activities requiring advanced logic. Fears surrounding A.I.s capability to perform knowledge work and replace employees have some basis, but they misrepresent the degree to which employees would still be in control. In the world where A.I.s are used for knowledge work, employees would have the responsibility of verifying the outputs and answers. A.I.s would act primarily as assistants to knowledge workers.

Knowledge work is made up of a great deal of repetitive tasks. A.I.s would be able to perform these repetitive tasks to give knowledge workers more time to perform higher order work such as meeting clients and helping to develop and validate solutions to business issues.

The spread of A.I.s to the workplace will require a significant adjustment period. A.I.s can form and present arguments that may be incorrect and rely on and spread biases. Employees need to verify A.I. output. Management will need to establish and clearly communicate policies regarding the verification of A.I. output. A.I.s should not be relied upon to make decisions that impact employees' health and finances.

Some worry AI will eliminate entry-level jobs. Young workers learn by performing tasks that AI is designed to do. Companies can increase their organizational efficiency by removing lower-level jobs. However, they may be harming their ability to develop future employees. Instead, companies can enhance the skills of their employees by teaching them how to interpret and analyze AI results, and how to formulate effective questions. Junior employees should not just accept AI results. Employees should be trained to evaluate AI results.

An individual’s ability to use AI in an appropriate and effective manner is ultimately dependent on the person’s background knowledge and expertise. This may exacerbate existing workplace inequalities. Employees should be trained on how to use AI in the workplace and should be allowed to have a say on how to integrate AI in the workplace. This may also improve the fairness of using AI in the workplace.

Knowledge workers can use AI to do lower-level tasks and be free to consider more complex questions, enabling them to do higher-order thinking. However, if companies use AI in a manner to solely decrease their labor costs, this may result in employees doing less-reliable work to reach a final result. This may also further constrain alternative career paths.