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Hiring with AI, fairer, faster and better

Three diversity goals you can actually track and achieve in 2022

BY Team PredictiveHire

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More money is flowing into Environmental, Social and Governance (ESG) than ever. In 2021, investors poured $649 billion into ESG-focused funds worldwide, up 90% from the $542 billion invested in 2020. In the UK, over 21% of investors plan to back funds and companies with comprehensive ESG strategies by 2025. And in Australia, more than 55% of super funds are using responsible investment approaches to inform strategic asset allocation.

All this investment has prompted a sharper focus on social issues across major companies – the S in Environmental, Social, and Governance. The great news is that investment in the big S, in turn, means more money and attention toward progress in Diversity, Equity and Inclusion (DEI).

Executives that care about diversity know that an effective strategy must start at the top – take Australian superannuation fund HESTA and its 40:40 vision as an example. But, to be truly successful, we need DEI goals at all levels, and we need to track, accurately, the degree to which we meet them.

Both boards and shareholders want measurable change in DEI, and fast. According to a Harvard Business Review study of S&P 500 earnings calls, the frequency with which CEOs talk about issues of equity, fairness and inclusion has increased by 658% since 2018. You can bet that this will only increase further in the coming years.

Diversity goals need to be measurable, today

According to another HBR article, 40% of US companies discussed DEI in their Q2 2020 earnings calls, which is a huge step up from the 4% of companies that did the year before. And with 1,600 CEOs pledging to take action on DEI, setting goals and tracking progress remain top priorities.

DEI and ESG are big challenges, and we might take myriad possible approaches in trying to solve them. Some companies may start at the executive level (HESTA, as an example), while others may invest in partnerships and outreach programs. The spectrum of options can easily become overwhelming.

“Interestingly, I’m just looking at our workforce profile and have been discussing the changes in diversity since we updated our recruitment approach last March. Not only have we hired three times more ethnic minorities and 1.5 times more women, but we now have twice as many LGBTQI+ colleagues in our business than we did three years ago! Other initiatives have played a part, but I’d imagine the game changer has been PredictiveHire as we’ve had some direct feedback from a transgender colleague that they felt more confident with our recruitment process than they did in other applications! 

David Nally, HR Manager, Woodie’s UK

So why not start with the people you bring into your company, at all levels? Why not begin with the way you attract, assess, and select talent?

With a Smart conversational Ai, you can set realistic DEI targets and measure them, at scale, with little extra effort – ensuring you access the best talent from the widest possible pool. A Smart Interviewer is different to the simple chatbots used to automate routine tasks according to a fixed set of rules. For example, our conversational Ai is able to analyse interview responses to gain deeper insights about each candidate’s personality and competencies, in a fair and objective way. 

Our Smart Interviewer helps you track and meet these three key diversity goals.

  1. Gender bias

Our proprietary interview response database is made up of more than 500,000,000 words, enabling us to conduct the most sophisticated response analysis in the recruitment industry. We can do this on a macro scale (e.g. across countries, cultures, industries, and role types); or for individual companies.

Take these findings, combining data from a range of our customers, globally:

Diversity and inclusion analytics

Figure 1: Gender stats across applicants, Ai recommendations and hired

Thanks to our machine-learning capabilities, and the size of our database, we can provide the hiring team with real-time analytics on the following parameters:

  1. Number of observed female applicants vs number of expected female applicants (and the same for male applicants)
  2. Overall hiring rate of females vs males
  3. Overall rate of female vs male recommendations made by our Ai

By employing a smart interviewing Ai at the first stage of recruitment, we can prove progress with regards to inclusivity and bias reduction. These aggregate company data show that while the expected number of female applicants exceeded the number of those that actually applied, the number of recommendations made by our Smart Interviewer also beat expectations (effectively compensating for the top-of-funnel bias). We can also see that the rate of observed female hires far exceeded the expected number. 

What does this show? With just three metrics, you can see the progress being made in your recruitment process – and if performance is below expectation, you can see the stage at which targets are not being hit.

It is important to note that the recommendations of our Ai are based solely on its analysis of candidate responses in the chat-based FirstInterview. Its suitability criteria is based, among other factors, on HEXACO personality modeling and accurate assessments of various job related competencies such as team work, critical thinking and communication skills.

Our data also keep biases in check at each stage of the recruitment process, depending on the role type. As you can see, for all three roles, this company’s hiring outcomes were within regulatory limits (as stipulated by the US Equal Employment Opportunity Commission (EEOC)) across the three stages of their funnel: Applications received, recommendations made, and the hiring decisions ultimately made by the hiring team. The final step, it is important to note, happens independently of our Ai: It is a human decision. Despite this, the outcome data is recorded, so that the company can compare its outcomes against inputs and recommendations to see if late-funnel biases are occurring.

Solving gender bias with data

Figure 2: Role-type-based gender bias. Mid line indicates 0 bias. Shaded areas indicate the tolerance level. Right of line favours females and left favours males.

The feedback from candidates is extremely positive: Company A’s strivings for fairness and equality in its processes has resulted in a candidate satisfaction score of 98.7% for females, and 98.1% for males. Better still, the interview dropout rate across the board is less than 10%.

  1. Ethnicity bias

As with gender, our ethnicity analytics help hiring managers to easily set and accurately track goals for ethnicity representation in recruitment. Company A (whose data were shown in Figure 2) is, again, leading the way in this regard: Its BAME (Black, Asian and Ethnic Minorities) recommendation rate is at 46.5%, exceeding expectations – meanwhile, its non-BAME recommendation rate sits at 37.1%.

Our data has also helped Company A to increase its hiring commitments for First Nations people: The rate currently sits at 4.5%, from 4,000 candidates, above the national average of 1.8% (2018-19). This number is expected to increase over the coming year.

  1. Personality biases

The data we collect helps us, as well as our customers, understand the extent to which personality determines role suitability and general workplace success. It also helps us to eliminate long-standing biases that negatively impact certain candidates, despite the fact that said candidates may be highly suitable to the roles for which they are applying. 

For example, people high in trait agreeableness (compassionate, polite, not likely to dissent or proffer controversial viewpoints) tend to underperform in the traditional face-to-face interviews. Hiring managers may assume, based on this, that they are unable to lead, or are not a ‘culture fit’. However, a face-value assessment of agreeableness is not a reliable predictor of candidate potential. Only scientific analysis of HEXACO traits can make this call with accuracy.

Take these two visualizations, showing how different personality traits affect the recommendations made by our Ai. Females (red dot) and males (blue dot) are slightly different in agreeableness, but there is virtually no difference in their conscientiousness, a strong predictor of job performance. As a result of being able to measure conscientiousness accurately, our system can effectively allow for higher levels of agreeableness – or cancel out the negative face-value judgements typically made in face-to-face interviews. Despite these personality differences, as shown in Figure 1, PredictiveHire Ai recommendations for both male and female groups remain similar (~40%). This results in a fairer chance for all, and a wider pool of candidates. In this case, this is to the benefit of females.

diversity by personality type

Figure 3: Male (blue) and Female (red) personality trait differences

Bringing it all together

The world is changing, and we can no longer continue to take a “We’ll see what happens” approach to the ‘S’ in ESG. Many investors are pushing companies for better diversity and inclusion outcomes. At PredictiveHire, our data show that fair, scientifically valid, and explainable Ai can produce better outcomes for peoples of all genders and ethnicities. The companies that have adopted our Ai approach are seeing strong improvement in their own DEI practices and results.

Over and above assisting our clients, our commitment to DEI is embodied in a guiding vision of our own: Our FAIR Framework. This embeds an approach that ensures our systems and processes are ethical and transparent. Many similar Ai systems operate in a ‘black box’, providing little knowledge about how their algorithms help make important decisions or create issues like amplifying biases. We are committed to a fairer world, free of bias – and, with every candidate interviewed, our data is bringing us closer.

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