Introduction:
On November 18, 2024 it was reported that Hon Hai Technology Group (Foxconn), a Taiwan based leading technology solutions provider and a key supplier for Apple, issued a directive to its hiring agents to remove age, gender and marital criteria from job description of assembly line workers. This decision followed a Reuters investigation that uncovered that Foxconn had rejected married women for iPhone assembly jobs. When questioned, both Foxconn and Apple declined to comment. Further investigation revealed that Foxconn outsourced the recruitment of assembly line employees to third party vendors1, raising concerns about discriminatory practices in hiring processes.
Challenges women face in hiring processes
The challenges women face in hiring are not limited to discriminatory job descriptions or recruitment practices. A notable example is the case of Irene Fernandes v Neo Pharma (Pvt) limited1 where the claim was that the work done by male and female were of same nature yet the pay received by her was less in comparison to male counterpart. The court held that the authority has erred in denying equal entitlement under the Equal Remuneration Act.
In another recent case, permanent commissioned officer former Lt Selina John of military nursing service (MNS) was removed from service as she got married and obtained a low grade in annual confidential report. The court said that such rule was ex-facie manifestly arbitrary, as terminating employment because the woman has got married is a coarse case of gender discrimination and inequality.2
Discrimination against women was also seen against a pregnant women who was issued an offer letter for joining in the post of nursing officer and on submission of requisite documents including medical fitness test and was further denied the position on grounds of temporarily unfit as she was pregnant. The High Court ruled that this lead to bias against women and cannot be denied employment.3
These examples of biases and practices, which have long been entrenched to disadvantage certain sections of the population, reveals a stark discrepancy in the societal norms and workplace equality. A deeper look into these issues highlights how systemic prejudice continues to impact hiring decisions and career progression for women.
A recent survey revealed that nearly one in five HR decision-makers admit they have been reluctant to hire women they thought might go on to start families4 and almost 50% of Small and Medium Enterprises (SMEs) and start-ups in India reported less female hires in 2019 as a result of Maternity Benefit Amendment Act, 2017(hereinafter referred to as Maternity Act).5
Some Personal Information collected at the time of recruitment
Biases in hiring are often reinforced by collection of personal information during recruitment, which provides insights into an individual's personal life and can inadvertently contribute to discriminatory practices. Recruitment teams commonly collects information such as:
- Name, country, e-mail, phone number, place of residence, gender
- Annual salary
- Curriculum vitae (CV), cover letter which can include work experience, education qualification, skills, language known, contact details, etc.
- Marital status-whether single or married, if married number of children, etc.
While some of this information may appear essential for hiring process, it can often become basis for bias.
Legal Remedies under Indian Law
To address such biases and protect against discrimination, Indian law provides legislative remedies. For instance, the Occupational Health, Safety and Working Conditions Code, 2020 (hereinafter referred to as OSH Code), mandates that establishment with more than 50 persons shall have crèche facilities for female employee with children. Women can be employed in factories, between 7pm to 6am with their explicit consent only and other conditions pertaining to safety, holidays and working condition need to be strictly adhered by. Employer shall further ensure compliance with rules further restricting employment of pregnant women, in manufacturing and operation exposing them to bodily injury or disease.
The Social security code, 2020 also provide that establishment with more than 50 employee shall have facility of crèche and female employee should be allowed two nursing break breaks a day, in addition to other breaks allowed to her, until child turns 15 months. Further organization shall ensure that female employees are allowed maternity leave with maternity benefit for up to 26 weeks and further prohibits to employee women for a six weeks period immediately following delivery.
Further the code provides with penalty of upto INR 50000 and imprisonment of upto 6months upon dismissal, discharge or failure to pay maternity benefits.
The Code on wages, 2019 mandates employer need to ensure wage parity between all genders and prohibit discrimination in matters related to determination and payment of wages for same work or work of similar nature. Employers are also prohibited from discriminating during recruitment and in condition of employment based on the gender for same work, unless expressly permitted by law.
The role of AI in Amplifying Gender Bias
With the advancement of technology Artificial Intelligence (hereinafter referred to as AI) has become part of hiring process, a report published by Society for Human Resource Management (SHRM), showcased that 79% of organizations use automation or AI in recruitment7 while a leading professional firm revealed that 40% of its new hires were recruited using AI tools. These systems have streamlined the hiring process, often making it touchless until the interview stage.8
However, the reliance on AI in hiring has also amplified biases embedded in the data used to train these models. AI hiring platforms are often built on years of historical hiring data and preferences, which may inherently reflect discriminatory practices, for instance, a study evaluating Alpaca, a white-box model i.e. an AI model that provides explanation for the results showcased biases in classification task. It rejected candidates based on explanation that "She is pregnant" or "Because of her pregnancy."9
These AI is being used for creating job advertisement, shortlisting candidates, providing recruitment updates, etc. Since technology is still evolving it poses various risk, such as-
- The use of AI in hiring will eventually remove the human factor and will lead to lack of trust and bond which could be developed between humans, beneficial in hiring top talent.10
- AI uses complex machine learning methods which makes it difficult for recruiter to understand the process behind outcome therefore limiting the explainability leading to distrust in the organization and potential harm to the reputation.
- AI may collect vast amount of candidate's data which can involve personal identifiable information and sensitive personal data or information. Therefore, the more data collected the more protection is required from breach or unethical use.
- If data set provided to AI is biased it can lead to biases in recruitment as AI learn from data provided to it. Amazon AI model for shortlisting candidates for technical roles was found to favor male candidates as it rejected the female candidates CV. This was result of historical male dominance in the respective industry.11
UK based makeup artist after being temporarily suspended during pandemic was asked to re-apply and was evaluated on past performance and AI screening program. She was rejected as AI scored her less on body language.12
- The non-involvement of women in designing of AI data can contribute in women needs being overlooked affecting the women to men ratio in recruitment.
Regulations protecting from AI Biases
Further with the incorporation of AI in hiring, laws governing the data of individuals has been put in place by various jurisdiction. General Data Protection Regulations (hereinafter referred to as GDPR) mentions about human oversight in automated processing, including profiling. Also, various jurisdiction have come up with ethical guidelines to incorporate in the AI model tin order to mitigate biases in AI.
Further, the European Union AI Act classifies AI based hiring, workers management and access to self-employment platform are considered high risk AI. The data set being used for AI shall be complete, relevant and free from bias in order to ensure fairness and prevent discrimination in recruitment.13 It shall be under the supervision of human all the while in use.14 Further, system which continue to learn after being placed in market shall be so developed that risk of possible biased outputs shall be reduced or eliminated.15 The act also provides for penalties in case of breach of the various provisions of the act.16
To know more about AI in hiring refer to our article https://ssrana.in/articles/genai-bots-get-a-big-say-in-hiring/
Conclusion:
A woman is entitled for maternity leave which has now been held as social and fundamental right by the Apex Court time and again, to deny joining on the ground of pregnancy, would be highly discriminatory to a woman. It is certainly in violation of Article 14, 16 and 21 of the Constitution of India. After joining females are entitled for maternity leave. Therefore denying them position because they are pregnant at the time of hiring is unfair and needed to be looked with a new angle.17Further, AI is a useful tool in hiring as it makes a burdensome task easy and convenient, HR had to not go through various resume before shortlisting few as it can be done by the help of AI, recruitment team shall ensure that the efficiency shall not overshadow the key ethical principles. Also, Bias in non-algorithmic can be reduced through complying with the AI Principles and relevant laws. To protect the personal identifiable information of candidate, it can be stored in encrypted form till the process of hiring is going on and can further be deleted on fulfillment of purpose. Although AI can be a useful tool, human oversight is essential for several other factors like to remove bias or hiring candidate based on interactive skill and not just qualification.
https://ssrana.in/articles/role-of-ai-in-legal-systems-a-detailed-analysis/
Abhishekta Sharma, Assessment Intern at S.S. Rana & Co. has assisted in the research of this article.
Footnotes
2 1997(99(1))BOMLR 633
7 https://info.recruitics.com/blog/legal-and-ethical-risks-of-using-ai-in-hiring
9 https://engineering.nyu.edu/news/women-may-pay-mom-penalty-when-ai-used-hiring-new-research-suggests
12 https://www.bbc.com/worklife/article/20240214-ai-recruiting-hiring-software-bias-discrimination
13 https://artificialintelligenceact.eu/article/10/
14 https://artificialintelligenceact.eu/article/14/
15 https://artificialintelligenceact.eu/article/15/
16 https://artificialintelligenceact.eu/article/99/
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