Introduction

In this case I do not agree with the idea of the HR division of a medium-sized private company plans to create training materials for new hiring managers using information gathered from LinkedIn. The practice that is known as scrapping means collecting publicly available data from websites without getting consent from the people whose data is being collected. LinkedIn is a website that provides data such as Personal information, login credentials, and financial details which is why scraping on a site like LinkedIn is a huge problem if a company just starts taking it. Though I do understand that scraping is helpful for training and recruitment efforts since it makes more taxing and annoying tasks easier. However, just because we see how useful it is doesn’t mean that the department can ignore concerns regarding the moral and legal issues of exploiting this kind of practice. Tan states in his article the value of privacy, openness, and consent in the handling of personal data, explaining that gathering information without the proper authorization may be against the law and ethical standards. In this case analysis, I will argue that utilitarianism supports that business shouldn’t use data that has been scrapped because the potential privacy risks outweigh the benefits of using such data for training.

Central concepts from Zimmer

In “But the Data Is Already Public,” Zimmer examines the moral dilemmas raised by the illegal use of publicly available data from websites like LinkedIn. One important idea he emphasizes is “context collapse,” which refers to the discrepancy between a platform’s public face and people’s views of how their data is used. Zimmer explains that even when information is publicly accessible on a platform, users might not expect it to be gathered and used for purposes other than those for which they first gave their agreement. This concept is important because it shows the difference between what is visible to the public and what consumers expect in terms of privacy.

Additionally, Zimmer introduces the idea of “informational privacy,” highlighting the fact that people have the right to choose the use of their personal information, whether it is accessible to the public. He argues that gathering information without consent is against the moral concept of respecting people’s right to privacy. Since people might not have consented to the use of their data in this manner, the HR department’s plan to use scraped LinkedIn data for training purposes could be seen as a violation of these privacy standards.

Analysis of case using Zimmer’s concepts

When we apply Zimmer’s ideas to the case, we see a clear conflict between the company’s goals and the moral dilemmas caused by data scraping. LinkedIn users provide their information with the expectation that it will be used primarily for networking and job-hunting, not for private company training programs. The HR department is using publicly available data in ways that users may not have consented to through the collecting of this data. In this case, the ethical problem of violating people’s informational privacy arises from the lack of transparency and consent.

Furthermore, assessing the morality of the HR department’s actions is made more difficult by the reason why people put such information online. People likely expect their professional information to be used in specific ways, such as by networking peers or potential employers, when they post it on LinkedIn. Little do they know however the data purpose has been changed when the HR department uses it to create training materials, and this change may lead to a breach of confidence in LinkedIn. People whose information is gathered may feel exploited because they did not anticipate that their publicly available profiles would be used for corporate training.

Utilitarian Assessment Using Zimmer

From a utilitarian viewpoint, the choice to collect data and utilize it for training resources can be understood better by considering the possible advantages and disadvantages. Utilizing the data could benefit the business by helping to train new recruiting managers, which could improve hiring procedures and increase productivity. However, there could be significant harm to people’s confidence and privacy. Users may feel betrayed and have less faith in LinkedIn as a service if they believe their privacy has been violated. These negative effects extend beyond the individuals who are immediately impacted, as they may have greater consequences for LinkedIn’s user engagement and reputation.

The potential harm from violating privacy outweighs the benefit of using the gathered data for training, according to the utilitarian concept of increasing total pleasure. By using more ethical and transparent data collection techniques, such as using data that users have specifically consented to or creating their own data through surveys, the organization can achieve training outcomes that are comparable. Therefore, I feel like it would be more ethical for the company to avoid using data that has been scraped.

Central concepts from O’Neil

In “Weapons of Math Destruction,” O’Neil shows the risks associated with large data and algorithms, particularly to maintain bias and fairness. One of her main concepts is “opaque systems,” which is a system that is automated that has inner workings that are not understandable, for example what information to look for versus what information not to look for. O’Neil argues that unclear systems can lead to lot of problems since consumers don’t know how their data is being used or what influences automated decisions. Lack of openness can have negative impacts on society on a specific platform.

O’Neil also mentions “feedback loops,” in which biased data and automated judgments reinforce and extend inequality. When data is scraped without informed consent, there is a risk that the data used to train recruiting managers would reflect or continue biases found in the larger workforce. For example, if the scraped data comes primarily from a specific industries or demographics, it may greatly change the training content in ways that reinforce current imbalances rather than delivering a fair and inclusive recruiting method.

Analysis of the Case Using O’Neil’s Concepts

Examining the situation from O’Neil’s perspective highlights potential problems about transparency and fairness. The HR department’s decision to collect data for training creates an unclear system in which LinkedIn users have little to no control or knowledge into the use of their information. The lack of openness hinders people’s ability to make informed decisions about the sharing of their data, leading to ideas of exploitation by LinkedIn

Additionally, using scraped data could worsen biases in recruitment strategies. If the training data reflects specific ethical or professional patterns, Hiring Managers may accidentally learn to prefer candidates from certain backgrounds or industries, leading to existing problems. This feedback cycle may even result in less diversity in hiring procedures, which could have long-term negative consequences for the companies who are looking for more variety to hire or with specific specialties.

Utilitarian Assessment Using O’Neil

When utilitarianism is applied to the issue through O’Neil’s idea, the potential damage becomes clearer. The lack of transparency and equality in data scraping creates a system that may benefit some groups over others, resulting in disadvantages. Inaccurate information can create feedback loops that lead to unjust hiring practices, harming both individuals and departments as a whole. These consequences are severe, and the benefits of using scraped data—such as potentially more effective training—are outweighed by the negative effects on privacy, fairness, and equality.

From my own standpoint, the organization should avoid using scraped data. There are more ethical and transparent techniques for teaching hiring recruits, such as using properly private and consented-to data, which would reduce harm, while improving fairness. The company’s efforts must be focused on the well-being of individuals and communities, and gathering data without permission violates the Utilitarian standard.

Conclusion

In conclusion, Zimmer’s and O’Neil’s views provide compelling reasons to oppose the use of scraped data for training purposes. According to Zimmer, obtaining data from LinkedIn violates personal privacy and undermines trust, whereas O’Neil feels it establishes a non-transparent, biased framework that may increase inequality. In the framework of utilitarianism, the potential negative consequences for privacy, fairness, and equality outweigh the benefits of using this data. Although I do understand that an argument could be made that the company’s objectives are in good faith which would counter a point I made earlier, but the ethical and legal risks which make scraping LinkedIn data an unacceptable solution that could possibly even hurt that company’s reputation. I especially cannot agree with this when there are better alternatives than scarping. This department should try looking into more non-evasive methods that are more open and make better ethical choices for creating training tools that prioritize individuals’ rights.