Wisdom of the Crowd: Interviewing Your Network with Searchlight.ai
Searchlight.ai offers a new way to leverage references in the job application process, interviewing your network to qualify you as an applicant.
In competitive markets like the tech industry, finding qualified applicants is difficult. Closing the right people for the role and the company is even tougher. Since this pain is so widely felt, many technologists dream up software-enabled solutions to solve all of these problems. From automated resume screening to AI-powered video interviews, more advanced tools are built every day to find the right people to hire.
Searchlight.ai, an Accel-backed startup founded by twins with master’s degrees from Stanford and experience at Google and McKinsey, offers an interesting twist in the space. Instead of plugging resumes into a machine learning algorithm or analyzing emotions from during interviews, Searchlight uses your network to determine how well you’d fit into the hiring company. Where most companies would start with an initial phone screen for applicants, Searchlight allows applicants to give references up front which Searchlight then sends a proprietary questionnaire about the candidate. By collecting data from your previous colleagues, they are able to build a profile unbiased by resume writing skills or unconscious bias of a phone screener. The company can then use this profile to find potential matches for soft skills as well as technical abilities.
When I originally came across Searchlight, I was skeptical. Although I’ve had great experiences with previous employers and coworkers, there is an innate uncomfortability with not having the ability to represent oneself during the application process. Perhaps less obscure than many black-box algorithms used in hiring processes these days, the inability to control the narrative or even see what the references say about you still adds to the mounting feeling that applying to new jobs is increasingly a shot in the dark. And what if your colleagues only know the “work you” and not your “true self”? The data could be tainted by existing perceptions, making it very difficult to reinvent yourself as your career progresses.
After weighing the pros and cons, I’m convinced that despite the potential downsides this is actually a great way to leverage human-generated data in a non-biased, positive way. The most apparent benefit is that people are generally bad at demonstrating or judging soft skills in an interview setting. Resume writing, interviewing, and performing well in an interview are all skills that have to be trained and learned. This leads to otherwise stellar candidates regularly being passed over in the process. By aggregating analysis on a candidate from multiple people from their past, Searchlight diversifies the data sources used in support of the candidate and allows recruiters to make less biased decisions. The Searchlight system also helps companies get more serious applicants as it requires additional upfront work. If an applicant is excited about working for the company, it is a fairly low hurdle to collect the references but it is a big enough barrier to filter out unserious applicants. Also, by giving applicants the ability to retain references on the platform, it helps applicants continually add to their profile over time.
According to the article, Searchlight is starting with hiring but expects to leverage their datasets in the future for employee development. This could be particularly interesting if past or present colleagues identified soft skill opportunity areas that could then be focus areas for personal training and development programs. Also, the profiles could be used in the very first 1:1 a new hire has with their manager to bridge the person improvement gap so often felt when switching jobs. Off to a fast start, It will be exciting to keep up with Searchlight as they grow their dataset and add additional features to their platform.
References:
Daso, F., 2020. Accel-Backed Searchlight.Ai, A B2B Hiring Software Startup, Fixes The Broken Interviewing Process. [online] Forbes. Available at: <https://www.forbes.com/sites/frederickdaso/2020/03/31/accel-backed-searchlightai-a-b2b-hiring-software-startup-fixes-the-broken-interviewing-process/#5607aed47536> [Accessed 11 April 2020].
Really interesting, John! Thanks for sharing!
A couple of reflections from my end:
i) I agree with the efficiency argument in favor of Searchlight over reference calls. It has the potential to save HR money and time. However, I’m not sure I agree that it filters out less serious applicants. You point out that “by giving applicants the ability to retain references on the platform, it helps applicants continually add to their profile over time”. If that’s the case and I’m understanding correctly, once I put in the upfront work to get my references on the platform, wouldn’t it very little incremental effort for me to apply to other jobs using those exact same references?
ii) The second thing I wanted to touch on is the idea that the platform might help mitigate unconscious biases. On their website, Searchlight says:
“Counteract prestige bias with a more equitable hiring practice and objective reference data. Using Searchlight, 80% of our partners have hired more top performers from underrepresented backgrounds.”
There are two things that influence whether your algorithm effectively eliminates or perpetuates biases: (1) the bias in the data you input, (2) the design of the algorithm itself. Regarding (1), I can see how a well defined survey might be effective in collecting data in an objective way (e.g. it’s well studied that when recommending a female vs a male, we are more likely to use certain adjectives and the survey can be designed to mitigate that). If you manage to gather less biased data, your algorithm is less likely to produce biased results. I would like to know more about how the algorithm addresses (2).
iii) Unconscious bias is just a subset of bias, but the algorithm by design might perpetuate other biases. For example, I wonder if Searchlight weighs the references from a manager at SMB the same way it weighs those of a manager from a big tech firm. Furthermore, I worry that their results might disproportionately benefit those with larger networks. Without Searchlight, a recruiter might be willing to do a few calls, but with Searchlight it can get as many reference points as possible for the same amount of effort. Hence, the disadvantage for a candidate that has worked for a few years at a small company with respect to one that has worked for a Google or a McKinsey where they have had different teams and managers might be exacerbated.
Thanks for sharing this article! I’m a huge fan of the searchlight.ai team. They’ve been super laser-focused even before they went through YC.
The biggest concern I have about this opportunity is whether companies would be willing to pay for reference checks. I think that the hurdle for HR and team leaders is that they like to rely on in-person “reference checks” and “interviews”. Humans have a bias towards thinking that they make better people decisions than an algorithm.
Hi John – Thanks for this interesting post. Their mission to “counteract prestige bias” is similar to Eightfold (the company i blogged about) which is trying to help companies identify non-traditional candidates that are equally qualified as traditional candidates.
My problem with Searchlight’s approach is that reference checks offered up are typically biased to be very positive so i wonder how helpful the data collected is. It reminds me of the testimonials feature on linkedin, which helps add color, but doesn’t provide a balanced review of the candidate.
Overall, i like the idea of helping companies “counteract prestige bias”. I see it as a win-win for both employers and job candidates.
Very interesting, thanks for sharing!
I like the idea of building this repository of references and using this as a platform that many companies start using. It’s similar to what LinkedIn tried to do with Endorsements, which I don’t know if anyone actually uses. However, I do see how this could create some issues:
1) People may try to game the system. Putting up good references for someone and doing the same in return in this efficient platform exchange could result in undifferentiated results.
2) It may be difficult to get away from luke-warm to negative reviews once they’ve been solidified, and it could be hard to get a second change. In a more simplistic way, it’s similar to Uber passengers or drivers not getting selected on the app because of their rating and never having the chance to improve upon it.
Because of these potential pitfalls around a platform, it may be better off as a one-off tool to improve the reference checking process.
John, thanks for sharing this. Searchlight has the potential to help companies enormously. However, three potential cons came to my mind:
i) Would it reduce diversity in the workplaces by favoring same type of candidates, eventually resulting in lower creativity and more group-think?
ii) Can the algorithm be gamed by references?
iii) Can the algorithm develop some kind of bias?
Apart from these 3 questions, Searchlight would be super helpful for most companies.