Different types of bias, whether intentional or not, have been a concern among HR departments for decades. Artificial intelligence AI and machine learning (ML) programs have been promoted as a more objective and less partial way to select, hire and train job candidates. Unfortunately, humans can program biased information into algorithms, skewing the results. Artificial intelligence programs can also exhibit bias when sufficient and representative data is lacking. Click here to see Recruitment Agency in Karachi.
Biased AI programs can create serious problems for employers and HR departments who use these tools in their hiring processes. Indeed, a new law in New York City requires employers to conduct a “partial verification” of any automated employment decision-making tool and to notify employees or candidates if the employer has used that tool to make decisions. at work.
As a growing number of companies are using AI to streamline their operations, including hiring, it’s important to take steps to avoid bias in these algorithms. Here are some ways to reduce or eliminate bias in HR algorithms.
Understand the limitations of AI
AI is a valuable tool for streamlining HR departments, but it shouldn’t be the only solution. Consider how humans and AI can work in tandem, instead of replacing one with the other.
It may appear that this defeats the purpose of using AI in the first place. However, researchers from the National Institute of Standards and Technology (NIST) suggest a “socio-technical” approach that understands the limitations of purely technical efforts to mitigate bias.
When selecting and implementing AI tools, employers, HR departments, and IT specialists should be aware of the data sources used by these tools. Make sure the AI developers have taken steps to limit bias. Human resources departments should monitor the data that powers their AI to avoid creating or emphasizing bias.
Create definitions of bias and fairness
Defining bias and fairness, and their minimum acceptable levels is a tall order for any employer. Many industries and government bodies have struggled to develop standard and universal definitions of bias. What constitutes “fair” in one organization may not apply to another.
By defining biases for their organizations, however, employers can guide the choice of AI tools and demonstrate their commitment to limiting bias in hiring. It will also help HR staff know when their AI tools are not up to their standards.
Employers can set a single standard for fairness and prejudice, or they can have different thresholds for different groups or situations. In any case, leaders should consider a variety of parameters and standards when establishing equity definitions and goals.
“An essential practice is to ensure as much as possible that the training data is representative,” says Dr. Sanjiv M. Narayan, professor of medicine at Stanford University School of Medicine. “Representative of what? No dataset can represent the entire universe of options. Therefore, it is important to identify the target application and audience in advance, then tailor the training data to that target. “
Job postings can influence AI
Where a company shares job opportunities they can affect the data that powers an AI algorithm and thus can contribute to the bias. For example, if a company posts a job posting only on LinkedIn, that platform’s algorithms select which users see that job posting, which could unfairly skew the employer’s AI data.
Rather than solely dependent on an external algorithm, employers should include their own outreach efforts to target potential job seekers. This can provide new insights into AI hiring tools that could mitigate bias.
Regardless of whether they are disclosed in a job posting or elsewhere in the hiring process, applicants should be aware of whether an employer is helpful.
Review and update artificial intelligence tools.
As AI evolves in response to new data, employers should periodically conduct audits of their ML tools to ensure minimal bias and make necessary corrections. This may include a review of rejected applicants and whether such exclusions were justified. AI may need to be adapted or even canceled by human resources personnel.
Business leaders, decision-makers, and human resources departments should also stay up to date on AI research. This includes updates to specific software or new discoveries in the field. Look for best practices from companies like Google AI or tools recommended by IBM’s “Fairness 360” framework.
While it can save time and improve efficiency, AI is not entirely hands-free. Instead of replacing one with the other, machines and humans should work together to reduce prejudice and improve the effectiveness of artificial intelligence software. Human resources departments should keep an eye on the field of AI research and the software of their choice to make sure they are using the fairest options in hiring. See also HR consultancy Abu Dhabi.
