Academic literature on the topic 'AI recruitment'
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Journal articles on the topic "AI recruitment"
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Wilfred, Dennis. "AI in Recruitment." NHRD Network Journal 11, no. 2 (April 2018): 15–18. http://dx.doi.org/10.1177/0974173920180204.
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Liu, Jia, Shih-Hsuan Chang, Yu-Ci Xu, Guo-An Wu, and Shih-Feng Chang. "Using AI to Enhance Recruitment Effect." Journal of Physics: Conference Series 1827, no. 1 (March 1, 2021): 012150. http://dx.doi.org/10.1088/1742-6596/1827/1/012150.
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Upadhyay, Ashwani Kumar, and Komal Khandelwal. "Applying artificial intelligence: implications for recruitment." Strategic HR Review 17, no. 5 (October 8, 2018): 255–58. http://dx.doi.org/10.1108/shr-07-2018-0051.
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Purpose This paper aims to review the applications of artificial intelligence (AI) in the hiring process and its practical implications. This paper highlights the strategic shift in recruitment industry caused due to the adoption of AI in the recruitment process. Design/methodology/approach This paper is prepared by independent academicians who have synthesized their views by a review of the latest reports, articles, research papers and other relevant literature. Findings This paper describes the impact of developments in the field of AI on the hiring process and the recruitment industry. The application of AI for managing the recruitment process is leading to efficiency as well as qualitative gains for both clients and candidates. Practical implications This paper offers strategic insights into automation of the recruitment process and presents practical ideas for implementation of AI in the recruitment industry. It also discusses the strategic implications of the usage of AI in the recruitment industry. Originality/value This article describes the role of technological advancements in AI and its application for creating value for the recruitment industry as well as the clients. It saves the valuable reading time of practitioners and researchers by highlighting the AI applications in the recruitment industry in a concise and simple format.
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Raveendra, P. V., Y. M. Satish, and Padmalini Singh. "Changing Landscape of Recruitment Industry: A Study on the Impact of Artificial Intelligence on Eliminating Hiring Bias from Recruitment and Selection Process." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4404–7. http://dx.doi.org/10.1166/jctn.2020.9086.
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An emerging trend of implementing Artificial Intelligence (AI) technologies can be seen in such domains that were solely dominated by humans. Today, AI is utilized extensively in HR department to assist and accelerate recruitment and selection process (Martin, F.R., 2019. Employers Are Now Using Artificial Intelligence To Stop Bias In Hiring. Retrieved September 22, 2019, from analyticsindiamag. com: https://analyticsindiamag.com/employersare-using-ai-stop-bias-hiring/.). This paper attempts to present the impact of AI on recruitment and selection process, incorporation of AI in eliminating unconscious biases during hiring. The study addresses the rising questions such as how AI has changed the landscape of recruitment industry, role of AI in recruitment and selection process, whether AI can help in eliminating the unconscious bias during recruitment and selection process. In order to uncover the understanding and figure out the potential solutions that AI brings to the HR process, an extensive review of literature has been carried out. It is concluded by analyzing the past contributions that AI offers potential solution to recruitment managers in optimizing the recruitment and selection process and is able to negate human biases prevalent during hiring. The future waits for augmented intelligence technologies offering better results taking over repetitive administrative jobs completely.
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Kazim, Emre, Adriano Soares Koshiyama, Airlie Hilliard, and Roseline Polle. "Systematizing Audit in Algorithmic Recruitment." Journal of Intelligence 9, no. 3 (September 17, 2021): 46. http://dx.doi.org/10.3390/jintelligence9030046.
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Business psychologists study and assess relevant individual differences, such as intelligence and personality, in the context of work. Such studies have informed the development of artificial intelligence systems (AI) designed to measure individual differences. This has been capitalized on by companies who have developed AI-driven recruitment solutions that include aggregation of appropriate candidates (Hiretual), interviewing through a chatbot (Paradox), video interview assessment (MyInterview), and CV-analysis (Textio), as well as estimation of psychometric characteristics through image-(Traitify) and game-based assessments (HireVue) and video interviews (Cammio). However, driven by concern that such high-impact technology must be used responsibly due to the potential for unfair hiring to result from the algorithms used by these tools, there is an active effort towards proving mechanisms of governance for such automation. In this article, we apply a systematic algorithm audit framework in the context of the ethically critical industry of algorithmic recruitment systems, exploring how audit assessments on AI-driven systems can be used to assure that such systems are being responsibly deployed in a fair and well-governed manner. We outline sources of risk for the use of algorithmic hiring tools, suggest the most appropriate opportunities for audits to take place, recommend ways to measure bias in algorithms, and discuss the transparency of algorithms.
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Kot, Sebastian, Hafezali Iqbal Hussain, Svitlana Bilan, Muhammad Haseeb, and Leonardus W. W. Mihardjo. "THE ROLE OF ARTIFICIAL INTELLIGENCE RECRUITMENT AND QUALITY TO EXPLAIN THE PHENOMENON OF EMPLOYER REPUTATION." Journal of Business Economics and Management 22, no. 4 (May 14, 2021): 867–83. http://dx.doi.org/10.3846/jbem.2021.14606.
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The prime contribution of current research entails the explanation of role of artificial intelligence based human resource management function to determine the employer reputation among pharmaceutical industry of Indonesia. The study intends to examine the empirically investigation the role and impact of artificial intelligence-based recruitment and artificial intelligence-based quality to determine the employer reputation with mediating role of artificial intelligence adoption. The study contributes to the body of knowledge and claims to be novel in explaining the AI based HR function to explain the phenomenon of employer reputation. The study examined the empirical investigation between AI based recruitment and AI based quality to influence the AI adoption that further predicts the phenomenon of employer reputation. The study was conducted on pharmaceutical industry of Indonesia and convenience sampling was used for data collected and Smart-PLS was utilized for data analysis. The study found that AI based recruitment and quality significantly influences the AI adoption and further it influences the employer reputation. The mediation role of artificial intelligence adoption is significant where it is found that artificial intelligence mediates the relationship between artificial intelligence recruitment and employer reputation, with similar significant mediation role between artificial intelligence quality and employer reputation.
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Wang, Xuhui, Md Jamirul Haque, Wenjing Li, Asad Hassan Butt, Hassan Ahmad, and Hamid Ali Shaikh. "AI-Enabled E-Recruitment Services Make Job Searching, Application Submission, and Employee Selection More Interactive." Information Resources Management Journal 34, no. 4 (October 2021): 48–68. http://dx.doi.org/10.4018/irmj.2021100103.
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Personnel recruitment and selection is changing rapidly with the adoption of artificial intelligence (AI) tools. This chapter looks at how job applicants perceive AI in recruitment. The results show that AI tools encourage a larger number of quality application submissions and for two reasons. First, AI entrains a perception of a novel approach to job searching. Second, AI is perceived to be able to interactively tailor the application experience to what the individual applicant expects and has to offer. These perceptions increase the likelihood the user will submit a job application and so improves the size and quality of the pool from which to recruit personnel.
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Albert, Edward Tristram. "AI in talent acquisition: a review of AI-applications used in recruitment and selection." Strategic HR Review 18, no. 5 (October 14, 2019): 215–21. http://dx.doi.org/10.1108/shr-04-2019-0024.
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Purpose The purpose of this study is to explore the current use of artificial intelligence (AI) in the recruitment and selection of candidates. More specifically, this research investigates the level, rate and potential adoption areas for AI-tools across the hiring process. Design/methodology/approach To fulfill that purpose, a two-step approach was adopted. First, the literature was extensively reviewed to identify potential AI-application areas supporting the recruitment and selection (R&S) process. Second, primary research was carried out in the form of semi-structured thematic interviews with different types of R&S specialists including HR managers, consultants and academics to evaluate how much of the AI-applications areas identified in the literature review are being used in practice. Findings This study presents a multitude of findings. First, it identifies 11 areas across the R&S Process where AI-applications can be applied. However, practitioners currently seem to rely mostly on three: chatbots, screening software and task automation tools. Second, most companies adopting these AI-tools tend to be larger, tech-focussed and/or innovative firms. Finally, despite the exponential rate of AI-adoption, companies have yet to reach an inflection point as they currently show reluctance to invest in that technology for R&S. Research limitations/implications Due to the qualitative and exploratory nature behind the research, this study displays a significant amount of subjectivity, and therefore, lacks generalisability. Despite this limitation, this study opens the door to many opportunities for academic research, both qualitative and quantitative. Originality/value This paper addresses the huge research gap surrounding AI in R&S, pertaining specifically to the scarcity and poor quality of the current academic literature. Furthermore, this research provides a comprehensive overview of the state of AI in R&S, which will be helpful for academics and practitioners looking to rapidly gain a holistic understanding of AI in R&S.
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Willis, Kate, and Chris Woods. "Managing invasive Styela clava populations: inhibiting larval recruitment with medetomidine." Aquatic Invasions 6, no. 4 (December 2011): 511–14. http://dx.doi.org/10.3391/ai.2011.6.4.16.
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FraiJ, JihaD, and Várallyai László. "literature Review: Artificial Intelligence Impact on the Recruitment ProcessA LITERATURE REVIEW: ARTIFICIAL INTELLIGENCE IMPACT ON THE RECRUITMENT PROCESS." International Journal of Engineering and Management Sciences 6, no. 1 (May 13, 2021): 108–19. http://dx.doi.org/10.21791/ijems.2021.1.10.
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This paper aim is to review the implementation of artificial intelligence (AI) in the Human Resources Management (HRM) recruitment processes. A systematic review was adopted in which academic papers, magazine articles as well as high rated websites with related fields were checked. The findings of this study should contribute to the general understanding of the impact of AI on the HRM recruitment process. It was impossible to track and cover all topics related to the subject. However, the research methodology used seems to be reasonable and acceptable as it covers a good number of articles which are related to the core subject area. The results and findings were almost clear that using AI is advantages in the area of recruitment as technology can serve best in this area. Moreover, time, efforts, and boring daily tasks are transformed to be computerized which makes a good space for humans to focus on more important subjects related to boosting performance and development. Acquiring automation and cognitive insights as well as cognitive engagement in the recruitment process would make it possible for systems to work similarly to the human brain in terms of data analysis and the ability to build an effective systematic engagement to process the data in an unbiased, efficient and fast way.
Dissertations / Theses on the topic "AI recruitment"
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Savola, Hannimari, and Bijona Troqe. "Recruiters just wanna have...AI? : Implications of implementing AI in HR recruitment." Thesis, Linköpings universitet, Företagsekonomi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158480.
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The development and implementation of AI is rapidly growing in the Nordic countries, yet the perception and information of AI is still limited. This paper will look deeper into the managerial aspects of implementing AI as part of the recruitment system, specifically the selection process and machine learning in text mining. The data gathering of this research has been conducted via interviews with Linköping's municipality, as well as collecting secondary data from public reports and scientific articles. Afterwards, the data was then scrutinized through theoretical analysis, using frameworks from different academic researches. A set of aspects was found, which affects the implementation of AI in an organisation in Sweden. A managerial view was taken to find a deeper significance on why an understanding of these aspects is necessary when implementing AI as a part of company's recruitment processes. However, while other elements other than the ones identified in this thesis do exist, a coherent picture of the process and the affecting variables can be thoroughly explained through these specifically chosen viewpoints. The paper concludes with drawing a bigger image of the AI in recruitment and selection processes, and the implications of it to an organisation considering to implement AI as part of these processes in near future. The thesis can be seen as a recommendation to any establishment that is making the decision of adopting the usage of AI as part of recruitment.
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Brishti, Juthika Kabir, and Ayesha Javed. "THE VIABILITY OF AI-BASED RECRUITMENT PROCESS : A systematic literature review." Thesis, Umeå universitet, Institutionen för informatik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172311.
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Paramita, Dhyana. "Digitalization in Talent Acquisition : A Case Study of AI in Recruitment." Thesis, Uppsala universitet, Institutionen för samhällsbyggnad och industriell teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413081.
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The trend of changing technology has affected different sectors including human resources in the process of recruitment and selection. Different technology solutions offer various benefit for recruitment practices especially in terms of efficiency outcome while it seems to overlook the relationship outcomes. Whether or not to have a balance approach depends on how a firm views their own recruitment process. The purpose of this study is to understand firm’s orientation towards its approach in performing recruiting practices. The analysis and discussion is articulated through the phenomenon of AI in recruiting with the interplay of different views especially from human resources and operations management. This study follows an inductive qualitative single case study that involves 11 HR professionals to participate in semi-structured interviews. The data analysis is performed with thematic analysis to develop grounded theory which is based on approach introduced by Gioia (see Corley and Gioia, 2004; Gioia et al., 2013). The findings proposed by this study is TOP framework which covers competitive advantage through operations, redefining customer orientation, and process enhancement through collaboration.
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Lisa, Aysha Khatun, and Simo Valerie Rostan Talla. "An in-depth study on the stages of AI in recruitment process of HRM and attitudes of recruiters and recruitees towards AI in Sweden." Thesis, Umeå universitet, Företagsekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184521.
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With the constant technological changes globally, organizations are now at the forefront of changing their business processes to be more competitive. These technological developments have brought in several shifts within business processes. Human resource management (HRM) has been dramatically affected by such changes more than ever, especially the recruitment process. As such, there is rising concern to shift from a traditional to an AI recruitment process. The adoption of AI in the recruitment process has remained relatively unexplored, especially within Swedish organizations. Despite its great discussion within academia and organizations, the limited amount of literature on the subject makes it interesting and current. Therefore, the main aims of this research are: (1) to analyze in which stages till date organizations are using AI in recruitment practices in Sweden and (2) to ascertain the attitudes of recruiters and recruitees towards the use of AI in the recruitment process in Sweden organizational context. This research adopted a qualitative approach with semi-structured approach interviews conducted with three recruiters and five recruitees in Sweden. The empirical findings of the study reveal that organizations have not fully implemented AI in the recruitment process. Factors such as timeframe, recruitment cost, work efficiency, and human biases were considered the top challenges of the traditional recruitment process. AI in the recruitment process can help reduce the lengthy time while increasing work efficiency with faster-recruiting methods. Organizations can share recruitment costs. Human biases can significantly be reduced with the use of AI at the pre-screening and selection stages. It was also discovered that the attitudes of recruiters and recruiters were seemingly positive towards the acceptance of AI in the recruitment process. Furthermore, AI was not seen as a threat to human jobs instead as a complementary role. This leads to the conclusion that AI can complement the recruitment process and AI cannot take human jobs since humans will still be needed for software development. This research provides contributions towards theoretical, practical, and social. This research offers an extent of the existing knowledge on the subject matter. It will help recruiters understand the importance of AI in the recruitment process. Furthermore, recruitees will be more accustomed to the idea of AI. In addition, the findings of this research can assist in the curriculum adjustment of educational institutions to best serve the needs of the changing business climate. At the government level, the findings can be used to encourage continuous innovation and learning. Furthermore, this research can be a starting point for other future research.
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Johansson, Jennifer, and Senja Herranen. "The application of Artificial Intelligence (AI) in Human Resource Management: Current state of AI and its impact on the traditional recruitment process." Thesis, Högskolan i Jönköping, Internationella Handelshögskolan, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-44323.
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Background: The world is constantly becoming more prone to technology due to globalization which implies organizations have to stay up to date in order to be competitive. Human Resource Management (HRM) is more important than ever, especially with a focus on the recruitment of new employees which will bring skills and knowledge to an organization. With technological advances also comes the opportunity to streamline activities that previously have had to be carried out by humans. Therefore, it is of the highest importance to consider and evaluate the impact technology might have on the area of HRM and specifically the recruitment process. Purpose: The purpose of this thesis is to research the implications that technological advancements, in particular Artificial Intelligence (AI), have for the recruitment process. It aims to investigate where AI can be implemented in the traditional recruitment process and possibly make the process more effective, as well as what the implications would be of having AI within recruitment. Method: This thesis uses a qualitative study with semi-structured interviews conducted with eight international companies from all over the world. It is viewed through an interpretivism research philosophy with an inductive research approach. Conclusion: The results show that the area of AI in recruitment is relatively new and there are not many companies that utilize AI in all parts of their recruitment process. The most suitable parts to implement AI in traditional recruitment include recruitment activities such as pre-selection and communication with candidates and sending out recruitment results for applicants. The main benefits of AI were seen as the speeded quality and elimination of routine tasks, while the major challenge was seen as the companies’ overall readiness towards new technologies.
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Kerey, Ayşegül Begüm, and Enrico D`Alessandro. "AI in recruitment: an exploratory study into the factors that impact its pace of adoption. : A case study to reveal the strategic implications of these factors on AI solution providers from a contingency perspective." Thesis, Uppsala universitet, Industriell teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446406.
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Over the past few years, the adoption of AI in recruitment has accellerated. However, there has been a noticeable resistance from HR managers to invest in AI tools for their deparment. With the aim of understanding the causes prompting this resistance, this thesis investigates the factors that impact the pace of adoption of AI in HR, with a focus on recruitment solutions. While designing an analytical framework inspired by the contingency perspective, the factors have been searched through a literature review and their effects have been tested in terms of magnitude and direction through a qualitative study. To do this, the authors performed a case study involving an external partner, an AI solution provider start-up company. A total of 16 semi-structured interviews have been conducted with different levels of stakeholders, including external partner`s employees, investors, competitors, and end- users. Finally, a strategic analysis of the AI recruitment market has been deployed. Our ambition is that the combination of the information over the factors together with the strategic analysis will empower the companies within the industry in taking better informed strategic decisions.
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Victorin, Karin. "AI as Gatekeepers to the Job Market : A Critical Reading of; Performance, Bias, and Coded Gaze in Recruitment Chatbots." Thesis, Linköpings universitet, Tema Genus, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177257.
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The topic of this thesis is AI recruitment chatbots, digital discrimination, and data feminism (D´Ignazio and F.Klein 2020), where I aim to critically analyze issues of bias in these types of human-machine interaction technologies. Coming from a professional background of theatre, performance art, and drama, I am curious to analyze how using AI and social robots as hiring tools entails a new type of “stage” (actor’s space), with a special emphasis on social acting. Humans are now required to adjust their performance and facial expressions in the search for, and approval of, a new job. I will use my “theatrical glasses” with an intersectional lens, and through a methodology of cultural analysis, reflect on various examples of conversational AI used in recruitment processes. The silver bullet syndrome is a term that points to a tendency to believe in a miraculous new technological tool that will “magically” solve human-related problems in a company or an organization. The captivating marketing message of the Swedish recruitment conversational AI tool – Tengai Unbiased – is the promise of a scientifically proven objective hiring tool, to solve the diversity problem for company management. But is it really free from bias? According to Karen Barad, agency is not an attribute, but the ongoing reconfiguration of the world influenced by what she terms intra-actions, a mutual constitution of entanglement between human and non-human agencies (2003:818). However, tech developers often disregard their entanglement of human-to-machine interferences which unfortunately generates unconscious bias. The thesis raises ethical questions of how algorithmic measurement of social competence risks holding unconscious biases, benefiting those already privileged or those acting within a normative spectrum.
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Lundgren, Patric, and Christofer Wiechert. "Artificiell intelligens i rekryteringsprocessen : En kvalitativ studie om rekryterares perception." Thesis, Högskolan i Skövde, Institutionen för handel och företagande, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17010.
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Fenomenet Artificiell intelligens (AI) är en högaktuell teknik som appliceras på flera olika områden inom samhället. Inom HR-arbetet kan rekryteringsprocessen baseras på AI-teknik och stora delar kan komma att automatiseras. Tidigare forskning har visat på att både urvalssökning och kandidatmatchning har varit användbara områden där företag kan automatisera för att tidseffektivisera. AI-teknik är ännu inte ett väletablerat fenomen på den svenska arbetsmarknaden och därför har rekryterares perception kring användandet undersökts. Syftet med studien är att öka förståelse om användning av AI-teknik i rekryteringsprocessen hos bemanningsföretag då det är stora volymer av arbetssökande inom bemanningsbranschen och deras huvudsysslor är att arbeta med bemanning och rekrytering. Den teoretiska referensramen baseras på två olika ansatser till rekrytering. De utgörs av den psykometriska ansatsen, som är en objektiv ansats, och den sociala ansatsen, som är en subjektiv ansats, för rekryteringsprocessens utformning. Den teoretiska referensramen baseras även på en forskningssammanställning om AI-teknik för att i analysen kunna göra en jämförelse mellan tidigare forskning och rekryterares insikter. Författarna har tagit fram en egen analysmodell för att använda den teoretiska referensramen till att analysera det empiriska materialet. För att skapa en djupare förståelse för rekryterares perception av användandet av AI i rekryteringsprocessen baseras studien på kvalitativa intervjuer med rekryterare på bemanningsföretag. För att skapa en variation bland respondenterna har studiens författare utfört intervjuer med nio olika respondenter på sju olika bemanningsföretag. Den insamlande empirin har analyserats genom författarnas analysmodell. Resultatet tyder på att rekryteringsprocessen idag inte är anpassad för att använda AI och framförallt arbetet med kravprofilen behöver utvecklas för att AI ska nå maximal utdelning. Studiens slutsats är att det kommer krävas en utveckling av de två tidigare presenterade ansatserna till rekryteringsprocessen. Författarna föreslår den automatiserade ansatsen till rekrytering som en tredje ansats, där den inledande processen objektiviseras och anpassas för AI och de mänskliga faktorerna bibehålls i subjektiva intervjuprocesser och mänskliga beslut.The phenomenon of Artificial Intelligence (AI) is a trending technology that is applied in several different areas of society. In HR work, the recruitment process can be based on AI technology and large parts can be automated. Previous research has shown that both selection and matching of candidates have been useful areas where companies can automate in order to make more efficient use of their time. AI technology is not yet a well-established phenomenon in Swedish companies and therefore, the recruiters' perception of use has been studied. The purpose of the study is to increase understanding of the use of AI technology in the recruitment process of staffing agencies, as there are large volumes of job seekers in the staffing industry and their main job is to work with staffing and recruitment. The theoretical frame is based on two different approaches to recruitment. They consist of the psychometric approach, which is an objective approach, and the social approach, which is a subjective approach to the recruitment process. The theoretical framework is also based on a research summary on AI technology in order to make a comparison between previous research and the recruiters' insights in the analysis. The authors have developed their own analysis model to use the theoretical frame to analyze the empirical material. To create a deeper understanding of recruiters perception of the use of AI in the recruitment process, the study is based on qualitative interviews with recruiters at staffing companies. In order to create a variation among the respondents, the authors the study have conducted interviews with nine different respondents at seven different staffing companies. The empirical data has been analyzed by the authors' analysis model. The result suggests that the recruitment process today is not adapted to use AI and, above all, the work with the requirement profile needs to be developed in order for AI to reach the maximum usage. The conclusion of the study is that a development of the two previously presented approaches to the recruitment process will be required. The authors propose the automated approach to recruitment as a third approach, where the initial process is objectified and adapted for AI but the human factors are maintained in subjective interview processes with human decisions.
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Fält, Felix, and Reuterstrand Adrian Torres. "AI och partiskhet vid beslutsfattande i rekryteringsprocesser : Hur artificiell intelligens kan hantera partiskhet i rekryteringsprocessen." Thesis, Linköpings universitet, Informationssystem och digitalisering, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177501.
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Dagens rekryteringsprocessen drivs oftast av en mänsklig rekryterare, men på senare tid har det blivit allt mer populärt att använda sig av AI verktyg för att effektivisera delar av processen, men också för att motverka den inneboende partiskhet som finns hos människor. Detta arbete syftar till att analysera hur rekryterare med erfarenheter kring användning av AI, och utvecklare av sådana system, upplever huruvida AI kan användas som ett verktyg för att möta partiskhet inom rekryteringsprocessen. Genom att genomföra semistrukturerade intervjuer med intressenter som är insatta i både rekryteringsprocessen, men också i utveckling av AI-system med rekrytering som huvudfokus, har vi fått insyn kring hur verksamheter jobbar och utvecklar dessa typer av system. Hur de förhåller sig till etiska frågor har varit avgörande för att kunna utvärdera om AI är lämpligt för denna uppgift. Vår slutsats visar att det finns användningsområden för AI i rekryteringen, men istället som ett komplementerande verktyg för den mänskliga rekryteraren snarare än en ersättare, som AI ofta spekuleras att vara. Fördelar som vi såg var bland annat att AI kan behandla fler kandidater än dess mänskliga motpart och i de flesta fall ta beslut som är kompetensbaserade eftersom AI:n inte påverkas av externa faktorer på samma vis som vi människor gör. Även om AI har sina brister, där den kan efterbilda negativa beteenedemönster från oss människor och att den mänskliga kontakten reduceras, så fann vi att de positiva aspekterna var övervägande, och att det finns en optimistisk inställning kring vidare studier inom området.The recruitment process of today is often driven by human recruiters and lately it has become increasingly popular to use AI-driven tools to streamline parts of this process, but also to try to counteract the inherent bias present in humans. This study aims to analyze how recruiters with experience in the use of AI, and developers of such systems, experience whether AI can be used as a tool to meet bias in the recruitment process. By performing semistructured interviews with relevant parties familiar with the recruitment process, but also with the development of AI-systems with recruitment as the main focus, we have gained insight into how companies work and how they develop these types of systems. How they relate to ethical issues has been useful in being able to evaluate whether AI is appropriate for this task. Our conclusion shows that there are uses for AI in recruitment, but instead as a complementary tool for the human recruiter rather than as a replacement, as AI is often speculated to be. Advantages that we saw included that AI can treat more candidates than its human counterpart and in most cases make decisions that are competency based because AI is not affected by external factors in the same way as we humans do. Although AI has its flaws, where it can mimic negative behavioural patterns from us humans and that human contact is reduced, we found that the positive aspects of AI were predominant, and that there is an optimistic attitude towards further studies in the field.
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Nordström, Rebecca A., and Hannah Björnlinger. "Artificiell intelligens- mer än bara en stödfunktion? : En kvalitativ undersökning hur artificiell intelligens kan medvetandegöra bias i en rekryteringsprocess." Thesis, Högskolan Dalarna, Institutionen för kultur och samhälle, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:du-37486.
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Syftet med denna studie är att bidra med en djupare förståelse för hur rekryterare använder Artificiell Intelligens (AI) i en rekryteringsprocess för att medvetandegöra bias. Tidigare forskning visar att arbetssökandens chanser till arbete påverkas av rekryterarens bias, detta gör att arbetssökanden inte bedöms utefter kompetens. Tidigare studier visar att arbetssökanden missgynnas baserat på olika egenskaper, kopplat till etnicitet, ålder och kön. Rekryteringsprocessen är i ett behov av verktyg som minskar denna bias, där forskning visar att AI-system kan vara ett sådant verktyg. I denna studie har vi inkluderat respondenter som besitter erfarenhet av AI-system i en urvalsprocess. Studien genomförs med en kvalitativ forskningsansats där åtta respondenter har inkluderats. Empirin har analyserats genom en tematisk analys där sex teman identifierats. Resultatet presenterar olika faktorer som jämförs mot tidigare forskning där diskussionen behandlar de mest centrala från studien. Resultatet visar att alla respondenter är överens om att alla människor innehar bias som påverkar urvalsprocessen. AI-system tar bort fokus från etnicitet, ålder och kön, därmed upplever respondenterna att AI-systemet kan medvetandegöra bias eftersom systemet baserar rangordning av arbetssökande utifrån kompetens. Studien lyfter vad som anses behövas av rekryterare för att möjliggöra för AI att kunna medvetandegöra bias. Avslutningsvis visar resultatet att AI-system kräver kontinuerlig utveckling. Med rätt förutsättningar kan AI medvetandegöra bias, bortse från synliga attribut och bedöma arbetssökande efter kompetens.The purpose of this study is to contribute with a deeper understanding of how recruiters use Artificial Intelligence (AI) in a recruitment process to raise awareness of bias. Previous research shows that applicant chances of getting a job are affected by the recruiter's bias, this means that applicants are not assessed on competence. Previous studies show that applicants are disadvantaged based on different characteristics, linked to ethnicity, age and gender. The recruitment process has a need of tools that reduce this bias, where research shows that AI systems can be such a tool. In this study, we have included participants who have experience of AI systems in a selection process. The study is carried out with a qualitative research approach where eight participants have been included. The empirics have been analysed through a thematic analysis where six themes have been identified. The results present various factors that are compared to previous research where the discussion deals with the most central from the study. The results show that all participants agree that all people have biases that affect the selection process. AI systems remove focus from ethnicity, age and gender, participants believe that the AI system can raise awareness of bias because the ranking is based on applicant’s competence. The study highlights what is considered needed by recruiters to enable AI to be able to raise awareness of bias. In conclusion, the results show that AI systems require continuous development. With the right conditions, AI can raise awareness of bias, ignore visible attributes and assess jobseekers according to competence.
Book chapters on the topic "AI recruitment"
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Amigoni, Francesco, and Mauro Villa. "An Algorithm for Recruitment of Agents in Agency Design." In AI*IA 99: Advances in Artificial Intelligence, 321–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-46238-4_28.
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Ochmann, Jessica, and Sven Laumer. "AI Recruitment: Explaining job seekers’ acceptance of automation in human resource management." In WI2020 Zentrale Tracks, 1633–48. GITO Verlag, 2020. http://dx.doi.org/10.30844/wi_2020_q1-ochmann.
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Roy, Mrinmoy. "AI-Powered Workforce Management and Its Future in India." In Artificial Intelligence. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.97817.
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Day in and day out, the Workforce Department faces new problems and operational demands. It is very important for the department to respond quickly and understand the best possible action to be taken in each single case. It is unknown in a compromised setting of near-constant shifts in forecast and scheduling, increased customer demands, and changing recruitment and retention of employees. Workforce management around the world has begun to use artificial intelligence (AI)-based workforce management (WFM) software to solve the above problems and reach goals. These tools transform workforce management by helping to anticipate and plan short- and long-term planning. These tools improve Workforce Management by helping to predict short- and long-term scheduling and recruiting requirements, communicate with staff, and at the right time bring customers in contact with the right agent. This chapter addresses AI workforce management intervention and WFM instruments with industry-specific case studies and its experience with the product Workforce Dimensions. Present status and future expectations are also critically reviewed. Techniques of AI and machine learning (ML) are transforming industries, as are goods from thermostats to cars. The global enterprise value generated from AI continues to grow, according to Gartner, and is projected to reach up to $ 3.9 trillion by 2022. But what do these approaches mean for workforce management in the field? The current chapter examines the growing use of artificial intelligence (AI) in various HRM functions, as well as the ongoing debate about the expected decline in the usability of human resources in organizations. In the presence of AI in the workplace, HR practitioners are constantly afraid of being replaced by computers/robots/smart business machines. The study aims to recognize AI’s important contribution to enhancing organizational decision-making processes, as well as to enhance awareness of AI’s acceptability and inclusion in the HRM department. Despite the fact that the combination of AI and HRM is attracting a large number of researchers, many aspects of the field remain unexplored. The current research proposes a collaborative approach by stressing the complementary role of HRM in the successful use of AI, and it contributes to the existing literature. Since AI and HR are so intertwined, organizations should concentrate on incorporating AI as a supporting tool for HR rather than attempting to take over HR’s function. Business systems and smart business machines should be designed in such a way that they cannot produce results without the help of HR.
Conference papers on the topic "AI recruitment"
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Mujtaba, Dena F., and Nihar R. Mahapatra. "Ethical Considerations in AI-Based Recruitment." In 2019 IEEE International Symposium on Technology and Society (ISTAS). IEEE, 2019. http://dx.doi.org/10.1109/istas48451.2019.8937920.
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Pena, Alejandro, Ignacio Serna, Aythami Morales, and Julian Fierrez. "Bias in Multimodal AI: Testbed for Fair Automatic Recruitment." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2020. http://dx.doi.org/10.1109/cvprw50498.2020.00022.
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Lee, Jung Hee, Ju Hyung Kim, Yong Hwan Kim, and Yong Min Song. "A Study on Priorities for Utilization of AI Recruitment System." In 2021 21st ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter). IEEE, 2021. http://dx.doi.org/10.1109/snpdwinter52325.2021.00072.
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Fernández Martínez, María del Carmen, and Alberto Fernández. "AI in Recruiting. Multi-agent Systems Architecture for Ethical and Legal Auditing." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/903.
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Artificial Intelligence (AI) domain-specific applications may have different ethical and legal implications. One of the current questions of AI is the challenges behind the analysis of job video-interviews. There are pros and cons to using AI in recruitment processes, and potential consequences for candidates, companies and states. Furthermore, the deficit of regulation of these systems reinforces the need for external and neutral auditing of the types of analysis made in interviews. We, therefore, propose a Multi-agent system architecture for neutral auditing to guarantee an inclusive and accurate AI and to reduce the potential discrimination in the job market.
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Ortega, Alfonso, Julian Fierrez, Aythami Morales, Zilong Wang, and Tony Ribeiro. "Symbolic AI for XAI: Evaluating LFIT Inductive Programming for Fair and Explainable Automatic Recruitment." In 2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW). IEEE, 2021. http://dx.doi.org/10.1109/wacvw52041.2021.00013.
Full textReports on the topic "AI recruitment"
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Cary, Dakota. Academics, AI, and APTs. Center for Security and Emerging Technology, March 2021. http://dx.doi.org/10.51593/2020ca010.
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Six Chinese universities have relationships with Advanced Persistent Threat (APT) hacking teams. Their activities range from recruitment to running cyber operations. These partnerships, themselves a case study in military-civil fusion, allow state-sponsored hackers to quickly move research from the lab to the field. This report examines these universities’ relationships with known APTs and analyzes the schools’ AI/ML research that may translate to future operational capabilities.