Greenhouse ethical principles: A framework for evaluating key business decisions

Confident team leader at work

2 mins, 37 secs read time

The promise of process automation, machine learning (ML) and artificial intelligence (AI) in recruiting technology has created excitement and trepidation among recruiting teams everywhere. These powerful tools can be used to reduce administrative burden and drive efficiencies across workflows. However, these tools also carry risk: from automating illegal discrimination to the creation of black-box decision frameworks that recruiting teams may struggle to understand. The decision to hire or not hire someone has a huge impact on real human beings, and we take our role in supporting those decisions for thousands of companies worldwide seriously.

At the first meeting of our Data, Product and Privacy Ethics Committee, we set out to create a set of principles that could be leveraged across our company and industry to promote practices that produce more fairness, equitability and consistency in the hiring process. Our principles serve as our North Star.

Below we’re sharing a high-level overview, but for those who want to dive deeper, the complete list of our Greenhouse ethical principles can be found here.


We always consider how a system can harm people within the hiring process.

Privacy & security

We secure candidate data as well as customer data.

We do not share candidate evaluations across customers.


We use consistency to create fairness and accountability.

We encourage people to make the consequential decisions.

Data science, machine learning and AI

We prioritize the explainability of machine learning models.

We do not create composite quality scores to evaluate people.

We actively seek out and mitigate existing biases in our ML technology.

Putting principles into action

It is crucial to us that these principles don’t just become words hanging on our walls and posted on our website, but that they are instilled in and actioned by every Greenhouse team member.

Commit to continuous product reviews.

We commit to actively applying the framework we’ve set forth here to product decisions that have the potential to impact individuals and society at large. Specifically, we will evaluate all product changes that may carry the risk of producing biased decisions and unfair outcomes, as well as any risk to the maintenance of privacy for sensitive customer and candidate data.

Hold ourselves accountable for decisions we made in the past.

As part of our regular planning processes, we will commit time and effort to reevaluating past decisions to drive alignment with the principles we’ve set forth.

Increase transparency with our third-party partners.

Greenhouse endeavors to clearly spell out, in plain language, how integration partnerships work so that both customers and candidates are aware of the extent to which their data is being shared beyond Greenhouse.

Our driving purpose

Our hope is that our work helps to spark a conversation across our industry and among businesses as a whole to ensure that fairness and ethics are always at the forefront of hiring practices. Our commitment to the Greenhouse community and its members remains steadfast and true: we support and enable human potential and the same level of fair and ethical consideration for all.

Interested in learning more? Read our complete list of Greenhouse ethical principles here.

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Mona Khalil

Mona Khalil

is a Data Scientist at Greenhouse with over 5 years of experience designing experiments, analyzing data and presenting results to academic and non-academic audiences. They have a strong interest in using predictive modeling for the public benefit, and improving equity and accessibility in STEM fields.

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