Are inclusive AI hiring tools not as inclusive as previously thought?

Are inclusive AI hiring tools not as inclusive as previously thought

Organizations are increasingly adopting Artificial Intelligence tools to boost cost efficiency, productivity, reduce resource burnout, change the conventional workflows and even to hire invaluable talent

The pandemic has resulted in organizations shifting to remote work environment overnight. Artificial intelligence tools are being increasingly adopted by organizations to ease the pressure on employees, boost cost-efficiency, and accelerate the complex hiring process. AI algorithms are considered as tools to eliminate human subjectivity, such as bias from the employees’ hiring process.

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IT leaders have warmed up to the prospect of hiring remote employees during the pandemic. Remote hiring has the advantage of enabling organizations to not stay limited to candidates from a given geographical location, now competent candidates can apply from across the world. Hiring managers state that filtering through the sheer volume of applications can be daunting.

CIOs are looking to automate the hiring process by eliminating the bias and reducing the time. Mutale Nkonde, CEO of AI For the People while talking to stated that AI algorithms are good for efficiency but not a good instrument for equity. Current use cases and evidence state that these platforms offer multilayer workings that deter the inclusivity efforts since the algorithm will produce a biased output if it is being fed biased data. IT leaders state that ML models are created by humans, AI is good for well-defined, specific tasks but falters when dealing with ambiguous data which results in subpar results.

AI optimization metrics with flaws can lead to biased results

Amazon had created a team of engineers to develop models that can filter talent and rank them based on capabilities. However, the technology was identified to exhibit biased choices and hence was discontinued.

IT leaders state that when drafting a hiring algorithm, organizations would want to implement the in-house measures to distinguish talents. The algorithm can be fed historical data gathered from the current pool of top-working employees’ capabilities as a part of the filtering process. If the organization has a history of the biased hiring process, the data being fed to the ML model will also be biased which results in boosting a lack of inclusivity.

Imperfect emotional recognition technology will hamper the inclusivity efforts

Many CIOs feel that organizations have implemented emotion recognition software during
interviews to analyze applicants. In the current facial recognition, technologies have been heavily criticized for biased results. Top organizations like IBM and Amazon have bowed out of the facial recognition market after pledging to improve inclusivity measures in the employees’ pool. The technology has been accused of giving false-positive errors especially when fed data related to minority communities.

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IT leaders acknowledge that AI tech is capable of consuming a large volume of datasets and identifying patterns, but is inadequate when it comes to identifying context and human reasoning. Building a hiring platform technology on pre-existing standards will only return biased results based on a particular type of employee. C-suite executives have to scrutinize the ML models before approving the implementation

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AI does reduce the time required for hiring, but at what cost? Technology is incapable of rectifying a broken process or broken culture. Leaders and HR professionals need to take the step to ensure a fair and diverse resource pool while understanding the added value of doing so and keep an eye out for areas to constantly improve upon.