This is a research paper that I prepared for my sociology, science, and human class. I evaluated it combining Artificial Intelligence and Human Resources and the theoretical approach of this article is whether artificial intelligence and automated systems can discriminate in recruitment and selection within the business enviroenment.
Articles can be found above in the list.
Why I chose artificial intelligence as a research topic is, this is a growing field and a hot subject that can attract people to do some research on because it has been affecting the lives of people and businesses, Plus, I have had my passion for technology since my vocational high school career, which truly gave me a sense that I could carry on. So, back then, I was studying web design and my passion started with that. As time went by, when I began my university journey, I wanted to shift my career from that into data science and machine learning because this field appealed to me more than web design. So, that is why I now pursue my career in this field, and these are the reasons that I wanted to do research about AI.
Artificial intelligence is an assistant that acts like a human computing, which is mainly focusing on transmitting of intelligence and thinking into machines by using certain algorithms. Historically, this term was first used in 1956 by John McCarthy (Neelam, M, 2022). AI has enhanced in many fields such as engineering, mathematics, physics, and technology all of which have influenced the current shift in these fields that we are experiencing now.
It is known that AI has been seen and utilized in numerous sectors such as healthcare, education, manufacturing, data & surveillance, and human resources (Neelam, M, 2022). It can be said AI has gotten a major development and played crucial roles in those fields. Plus, it has brought something that eased life and businesses so that it increased productivity and efficiency. In terms of healthcare, AI innovations are driving the way for better life quality and investigating how to empower people with disabilities. As an example, a person who is paralyzed needs wheelchairs, which can be supported by robot support vehicles without the use of a joystick or sensors (Neelam, M, 2022). Another example might be about education. For instance, there may be algorithms that can predict student performance or can establish programs according to the interests of students in a particular major.
It is stated that many aspects of our lives have been had the potential by AI to be the cure to human bias and to improve fairness. However, it could be wrong to consider that algorithms reveal an objective truth just because they are based on Mathematics. Indeed, there is an AI bias problem lays in the algorithms’ inadequacy to comprehend the social and historical data they use (Beneduce, G, 2020). This is profoundly difficult to succeed and causes inequality and segregation lie in prediction. Therefore, this shows that how AI used in sectors is possibly harmful.
It is known that the position of the women in the society has changed the way in which female are socially sensed (Njoto, S, 2020). This shows androcentric biases as a norm that describes both men and female and this term refers to stating both sexes in data and documentation so, this shows men are perceived more universal standard of ‘human’, whereas female are overrepresented and sensed to be the member of that group. For example, when it comes to describing ‘parenting’ (Njoto, S, 2020). Therefore, these incorrectness and generalization of data become a problem when algorithms are trained, and they might generate outcomes that are incorrect or worse by making decisions based on these data.
A hiring algorithm to convert resumes and predict the suitability of a candidate was generated by Amazon. This algorithm used the existing data from their own workforce over the past ten years. Then, they found that the algorithm was against the female candidates. Indeed, this segregation was not intentional, rather it was because of algorithmic bias. This stemmed from people who coded it in such a way because most of the Amazon employees were white male employees, and this action as a determining factor of success was captured by their algorithm. Thereby, they developed a bias against female candidates. This includes words such as all-women’s college, women’s chess club and other words that identified female applicants (Njoto, S. 2020).
Beneduce, G. (2020). Artificial intelligence in recruitment: just because it´ s biased, does it mean it´ s bad? (Doctoral dissertation).
Neelam, M. (2022). Neelam MahaLakshmi (2021) Aspects of Artificial Intelligence In Karthikeyan. J, Su-Hie Ting and Yu-Jin Ng (eds),“Learning Outcomes of Classroom Research” p: 250-256, L’Ordine Nuovo Publication, India.
Njoto, S. (2020). Gendered Bots? Bias in the Use of Artificial Intelligence in Recruitment. The Policy Lab Research Paper.