research and critical analysis march 1

Notes for Two More Research Articles

Your Name Goes Here

Department of Name of Your Major, King Graduate School

KG 604: Research & Critical Analysis

Professor Ramlochan

2/14/2022

Notes for Two More Research Articles

W(5)H(1): New Research Article #1: Hiring Algorithms: An Ethnography of Fairness in Practice

1. Who conducted the research?

The study was conducted by Elmira van den Broek, Anastasia Sergeeva and Marleen Huysman

2. Why was the study completed (purpose / what researchers hoped to learn) ?

The purpose of the study was to investigate the perceived fairness in use of algorithms in hiring.

3. When was data collected (not the publication year) ?

Data was collected between October 2018 and April 2019.

4. Where was data collected (physical location) ?

Data was collected at multinational company in Europe called MultiCo.

5. How was data collected (methodology)? Cut as paste the paragraph below that describes the methodology and HIGHLIGHT the indicator words that specifically show you the methodology :

The researchers used an ethnographic study design in the research process.

“We conducted an ethnographic in-depth study at the HR department of a large multinational company in Europe, “MultiCo” (pseudonym), that recently implemented AI to enable a fair recruitment process .”

“ We have conducted 7 months (726 hours) of non-participant observation of the work around the AI application in graduate recruitment - including 110 meetings and 27 selection events - in the period between October 2018 and April 2019” (van den Broek et al., 2019).

6. What were the findings? Cut as paste the paragraph below that describes the findings and HIGHLIGHT the sentences that specifically show you the summary of findings:

The findings revealed that all groups involved in the study (HR, candidates, managers and AI team) contested the idea of fairness in using the algorithms in the hiring process.

“Our analysis of the specific practices and interactions of multiple stakeholders in the workplace shows that enabling fairness with AI can take a very different shape from what it promised, when put into practice. In particular, before the use of the AI application, the meaning of fairness was considered unproblematic and shared between stakeholder groups. However, as the various groups started working with AI in practice, they experienced mismatches between those notions of fairness that were inscribed and those implicit understandings of fairness that were important for daily work” (van den Broek et al., 2019).

W(5)H(1): New Research Article #2: The impact of artificial intelligence within the recruitment industry: Defining a new way of recruiting.

1. Who conducted the research?

James Wright and Dr David Atkinson

2. Why was the study completed (purpose / what researchers hoped to learn) ?

The research examined the impact of AI in recruiting employees.

3. When was data collected (not the publication year) ?

The study was conducted in 2019.

4. Where was data collected (physical location) ?

Data was collected in the UK.

5. How was data collected (methodology)? Cut as paste the paragraph below that describes the methodology and HIGHLIGHT the indicator words that specifically show you the methodology :

The research featured use of three methodologies which were interviews, online surveys and observation.

“To obtain findings considering the impact of AI in the recruitment process for both employers and candidates, three research methods were used. These are highlighted below.

Research Method: Interviews Online Survey Observation” (Wright & Atkinson, 2019).

6. What were the findings? Cut as paste the paragraph below that describes the findings and HIGHLIGHT the sentences that specifically show you the summary of findings:

The findings showed that there are uncertainties in use of AI with the candidate group majorly opposed to it.

“These are predominately preconceived ideologies on automation rather than AI. There are significant knowledge gaps in the industry, with many recruiters not understanding the technologies available to them. Candidates have a very negative opinion of the recruitment process and their considerations of automation follow this trend. There is clear motivation to change recruitment processes to appease candidates however it is contested whether AI is the solution to these complaints” (Wright & Atkinson, 2019).

W(5)H(1): New Research Article #3: Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter?

1. Who conducted the research?

Manjul Gupta, Carlos M. Parra and Denis Dennehy

2. Why was the study completed (purpose / what researchers hoped to learn) ?

The purpose of the study was to explore the role of Hoftede’s cultural values in influencing the inquiry of use of AI due perceived bias.

3. When was data collected (not the publication year) ?

The study was conducted in 2021.

4. Where was data collected (physical location) ?

Data was collected in the US.

5. How was data collected (methodology)? Cut as paste the paragraph below that describes the methodology and HIGHLIGHT the indicator words that specifically show you the methodology :

Data was collected through use of surveys.

“We first conducted a pilot survey of 60 MTurk users to ensure the readability and clarity of the seven scenarios pertaining to racial and gender bias. Following this, the main study was administered, and 387 completed responses were collected using MTurk in the United States” (Gupta et al., 2021).

6. What were the findings? Cut as paste the paragraph below that describes the findings and HIGHLIGHT the sentences that specifically show you the summary of findings:

The findings showed that AI is likely to lead to biased recommendations.

“ Indeed, AI-based recommendations may discriminate against some members of society more than others, and this we contend ought to be one of the most worrisome aspects of ubiquitous computing and generalized automation. Even though scholars have also been concerned, albeit recently, with proposing governance mechanisms to prevent AI-related misuses and abuses (Floridi & Cowls, 2019; Zuiderveen Borgesius, 2020), there still are reasons for concern. One such concern that we examine in this study is the extent to which individuals, owing to their individual-level cultural values, would be likely to question AI-based recommendations when perceived as racially or gender biased” (Gupta et al., 2021).

References

Gupta, M., Parra, C. M., & Dennehy, D. (2021). Questioning racial and gender bias in AI-based recommendations: Do espoused national cultural values matter?.  Information Systems Frontiers, 1-17. 10.1007/s10796-021-10156-2

van den Broek, E., Sergeeva, A., & Huysman, M. (2019). Hiring algorithms: An ethnography of fairness in practice. Association for Information Systems https://core.ac.uk/download/pdf/301385085.pdf

Wright, J., & Atkinson, D. (2019). The impact of artificial intelligence within the recruitment industry: Defining a new way of recruiting.  Carmichael Fisher, 1-39. https://www.cfsearch.com/wp-content/uploads/2019/10/James-Wright-The-impact-of-artificial-intelligence-within-the-recruitment-industry-Defining-a-new-way-of-recruiting.pdf