Artificial Intelligence in hiring[edit]

Artificial Intelligence in hiring involves the use of technology to automate aspects of the hiring process. Advances in artificial intelligence, such as the advent of machine learning and the growth of big data, enable AI to be utilized to recruit, screen, and predict the success of applicants. Proponents of artificial intelligence in hiring claim it reduces bias, assists with finding qualified candidates, and frees up human resource workers' time for other tasks, while opponents worry that AI perpetuates inequalities in the workplace and will eliminate jobs.

Human resources has been identified as one of the ten industries most affected by AI. It is increasingly common for companies to use AI to automate aspects of their hiring process. Over 12 million interviewees have been screened by the over 700 companies that utilize HireVue, a leading company in the space's, algorithm. The hospitality, finance, and tech industries in particular have incorporated AI into their hiring processes to significant extents.

Background[edit]

Artificial Intelligence has been a fascination of researchers since the term was coined in the mid-1950s. Researchers have identified four main forms of intelligence that AI would need to possess to truly replace humans in the workplace: mechanical, analytical, intuitive, and empathetic. Automation follows a predictable progression in which it will first be able to replace the mechanical tasks, then analytical tasks, then intuitive tasks, and finally empathy based tasks. However, full automation is not the only potential outcome of AI advancements. Humans may instead work alongside machines, enhancing the effectiveness of both. In the hiring context, this means that AI has already replaced many basic human resource tasks in recruitment and screening, but has freed up time for human resource workers to do other more creative tasks that can not yet be automated or do not make fiscal sense to automate. It also means that the type of jobs companies are recruiting and hiring form will continue to shift as the skillsets that are most valuable change.

Human resources is fundamentally an industry based around making predictions. Human resource specialists must predict which people would make quality candidates for a job, which marketing strategies would get those people to apply, which applicants would make the best employees, what kinds of compensation would get them to accept an offer, what is needed to retain an employee, which employees should be promoted, what a companies staffing needs, among others. AI is particularly adept at prediction because it can analyze huge amounts of data. This enables AI to make insights many humans would miss and find connections between seemingly unrelated data points. This provides value to a company and has made it advantageous to use AI to automate or augment many human resource tasks.

Uses of AI in Hiring[edit]

Screeners[edit]

Screeners are tests that allow companies to sift through a large applicant pool and extract applicants that have desirable features. Companies commonly screen through the use of questionnaires, coding tests, interviews, and resume analysis. Artificial Intelligence already plays a major role in the screening process. Resumes can be analyzed using AI for desirable characteristics, such as a certain amount of work experience or a relevant degree. Interviews can then be extended to applicant's whose resumes contain these characteristics.

What factors are used to screen applicants is a concern to ethicists and civil rights activists. A screener that favors people who have similar characteristics to those already employed at a company may perpetuate inequalities. For example, if a company that is predominantly white and male uses its employees' data to train its screener it may accidentally create a screening process that favors white, male applicants. The automation of screeners also has the potential to reduce biases. Biases against applicants with African American sounding names have been shown in multiple studies. An AI screener has the potential to limit human bias and error in the hiring process, allowing more minority applicants to be successful.

Recruitment[edit]

Recruitment involves the identification of potential applicants and the marketing of positions. AI is commonly utilized in the recruitment process because it can help boost the number of qualified applicants for positions. Companies are able to use AI to target their marketing to applicants who are likely to be good fits for a position. This often involves the use of social media sites advertising tools, which rely on AI. Facebook allows advertisers to target ads based on demographics, location, interests, behavior, and connections. Facebook also allows companies to target a "look-a-like" audience, that is the company supplies Facebook with a data set, typically the company's current employees, and Facebook will target the ad to profiles that are similar to the profiles in the data set. Additionally, job sites like Indeed, Glassdoor, and ZipRecruiter target job listings to applicants that have certain characteristics employers are looking for. Targeted advertising has many advantages for companies trying to recruit such being a more efficient use of resources, reaching a desired audience, and boosting qualified applicants. This has helped make it a mainstay in modern hiring.

Who receives a targeted ad can be controversial. In hiring, the implications of targeted ads have to do with who is able to find out about and then apply to a position. Most targeted ad algorithms are proprietary information. Some platforms, like Facebook and Google, allow users to see why they were shown a specific ad, but users who do not receive the ad likely never know of its existence and also have no way of knowing why they were not shown the ad.

Challenges:[edit]

Artificial Intelligence in hiring confers many benefits, but it also has some challenges which have concerned experts. AI is only as good as the data it is using. Biases can inadvertently be baked into the data used in AI. Often companies will use data from their employees to decide what people to recruit or hire. This can perpetuate bias and lead to more homogenous workforces. It can also be hard to quantify what makes a good employee. This poses a challenge for training AI to predict which employees will be best. Commonly used metrics like performance reviews can be subjective and have been shown to favor white employees over Black employees and men over women. Another challenge is the limited amount of available data. Employers only collect certain details about candidates during the initial stages of the hiring process. This requires AI to make determinations about candidates with very limited information to go off of. Many employers do not hire employees frequently and so have limited firm specific data to go off. To combat this, many firms will use algorithms and data from other firms in their industry. AI's reliance on applicant and current employees personal data raises privacy issues. These issues effect both the applicants and current employees, but also may have implications for third parties who are linked through social media to applicants or current employees. For example, a sweep of someone's social media will also show their friends and people they have tagged in photos or posts.

Mary Jane 404 Peer review[edit]

This is where you will complete your peer review exercise. Please use the following template to fill out your review.

General info[edit]

Whose work are you reviewing? (provide username) Eddyd101
Link to draft you're reviewing: https://wiki.riteme.site/wiki/User:Eddyd101/Artificial_intelligence_in_hiring?action=edit

Lead[edit]

Guiding questions:

Has the Lead been updated to reflect the new content added by your peer? Yes
Does the Lead include an introductory sentence that concisely and clearly describes the article's topic? Yes
Does the Lead include a brief description of the article's major sections? No
Does the Lead include information that is not present in the article? No
Is the Lead concise or is it overly detailed? The lead is concise

Lead evaluation[edit]

The lead is concise and informative, just needs a summary of the topics.

[edit]

Guiding questions:

Is the content added relevant to the topic? Yes
Is the content added up-to-date? Yes
Is there content that is missing or content that does not belong? No
Does the article deal with one of Wikipedia's equity gaps? Does it address topics related to historically underrepresented populations or topics? No

Content evaluation[edit]

The article's content is relevant to the topic, up-to date. I would suggest being more specific like saying which activist groups are against screeners and which studies have shown bias.

Tone and Balance[edit]

Guiding questions:

Is the content added neutral?
Are there any claims that appear heavily biased toward a particular position?
Are there viewpoints that are overrepresented, or underrepresented?
Does the content added attempt to persuade the reader in favor of one position or away from another?

Tone and balance evaluation[edit]

"AI is only as good as the data it is using" is a good point but I would try to phrase it in a more encyclopedic way. Though the article isn't biased, it seems to be driven by a specific viewpoint. I would try to cover the positive and negative aspects equally.

Sources and References[edit]

Guiding questions:

Is all new content backed up by a reliable secondary source of information?
Are the sources thorough - i.e. Do they reflect the available literature on the topic?
Are the sources current?
Are the sources written by a diverse spectrum of authors? Do they include historically marginalized individuals where possible?
Check a few links. Do they work?

Sources and references evaluation[edit]

I couldn't see the sources and there were only a few links. They work but maybe consider adding more.

Organization[edit]

Guiding questions:

Is the content added well-written - i.e. Is it concise, clear, and easy to read? yes
Does the content added have any grammatical or spelling errors? No
Is the content added well-organized - i.e. broken down into sections that reflect the major points of the topic? Yes

Organization evaluation[edit]

The content is well written, well organized, and easy to read.

Images and Media[edit]

Guiding questions: If your peer added images or media

Does the article include images that enhance understanding of the topic? N/A
Are images well-captioned? N/A
Do all images adhere to Wikipedia's copyright regulations? N/A
Are the images laid out in a visually appealing way? N/A

Images and media evaluation[edit]

The article does not include images.

For New Articles Only[edit]

If the draft you're reviewing is a new article, consider the following in addition to the above.

Does the article meet Wikipedia's Notability requirements - i.e. Is the article supported by 2-3 reliable secondary sources independent of the subject?
How exhaustive is the list of sources? Does it accurately represent all available literature on the subject?
Does the article follow the patterns of other similar articles - i.e. contain any necessary infoboxes, section headings, and any other features contained within similar articles?
Does the article link to other articles so it is more discoverable?

New Article Evaluation[edit]

Overall impressions[edit]

Guiding questions:

Has the content added improved the overall quality of the article - i.e. Is the article more complete?
What are the strengths of the content added?
How can the content added be improved?

Overall evaluation[edit]

It's a solid article that is easy to read and interesting. The tone is a little opinionated. Maybe try to be more detailed and add other view points.