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SOURCING — Set Clear and Effective DEI Target

Tech apprenticeships are an effective mechanism for bringing more Black, Latinx/e, and Indigenous diversity into a tech org, especially in business value generating technical roles in which representation is most lacking. Given the disadvantages inherent in most recruiting processes to underrepresented groups in tech, it is all the more important to set clear, measurable diversity, equity, inclusion, and belonging (DEIB) goals for your tech apprenticeship program and track progress over time.

Below, we share steps and guidance from DEIB champions on how to set such goals in the context of tech apprenticeship.

1) Understand the Legality of Setting DEIB Targets and Goals

We’re often asked questions about the legality of setting representation targets and goals, such as:

  • Is it illegal to make a decision to hire someone (or fire someone) based on a protected class? YES.
  • Is it illegal to focus your talent search when you’re trying to correct a diversity gap? NO.
  • Is it illegal to set goals tied to representation gaps at a tech organization? NO.

Setting goals and targets that take race into account are lawful if they:

  • Are designed to eliminate imbalances in traditionally segregated jobs
  • Do not unduly harm workers from populations that are not underrepresented
  • Serve as a temporary measure to eliminate imbalance and are not intended to maintain a new balance

Many legal teams, however, are adverse to any practices that could be perceived as discriminatory, or a form of “reverse discrimination.” This aversion to legal risk often results in demographic data collection being centralized away from the apprenticeship program and its leaders.

However, anonymized data tracking and sharing with apprenticeship leads is a must to enable the program to self-evaluate progress towards meeting representation goals. For this reason, we encourage apprenticeship leaders to work with legal counsel and HR leaders well-trained in diversity hiring practices to structure data collection and sharing in a way that helps foster inclusion legally.

2) Connect your Apprenticeship Program to Existing DEIB Goals

Does your organization have healthy org-wide racial representation targets? This can be a helpful starting point in setting program targets. As an example, by 2025, Pinterest aims to increase representation of US employees who self-identify as Black, Latinx or Hispanic, American Indian, Alaska Native, Native Hawaiian and/or Pacific Islander, to 20% by 2025. If you can demonstrate concretely how your tech apprenticeship program will support such goals over time, leaders may be more likely to buy-in and support.

3) Create Your Own Programmatic DEIB Goals

When racial representation goals do not exist at all or are not robust, the apprenticeship program can and should set its own. Without goals and data monitoring, ideally tied to the performance reviews of those responsible for the program, racial and educational inclusion will suffer. This is also an opportunity to set more ambitious goals than your broader organization, and use the apprenticeship model to demonstrate how apprenticeship can fast-track diversity goals around hiring, retention, and advancement.

A great starting point for setting racial or educational targets is also to look at baseline representation of the technical unit (engineering, etc) or teams into which the apprentice will ideally convert. What gaps exist for Black, Latinx/e, Indigenous talent and for those skilled via alternative routes than a bachelor’s degree and how can your program help to fill them?

4) Decide What Data to Collect

You should only collect DEIB data you will act on. Sharing data involves trust, and employees should see a tangible value in sharing. Carefully think through what data you need to make key decisions and tell a full story of what’s happening.

Some examples of actionable data we’ve seen collected include:

Who is progressing through each phase of the apprentice journey? (anonymized data, aggregated by race, and by highest level of education completed):

  • Who applies
  • Who is interviewed and not
  • Who is accepted into the program and not
  • Who is converted into a full time role and not

Track how this data changes from cohort to cohort over time

Make sure to align any data collection categories with legal reporting requirements, but you can also expand them, if needed, as long as they ladder up neatly.

Lastly, align with the language progressiveness of your organization’s culture in your framing and labeling.

5) Track Measures of Inclusion and Belonging

While representation data is a key and often missing component of solid DEIB work, it’s also important to measure indicators of inclusion and belonging — such as whether an individual feels like a full member of the community and can thrive. Determining this can get tricky in small cohorts of apprentices, in which even anonymized surveys can reveal someone’s identity. In such cases, we recommend creating groups that are large enough (e.g., BIPOC rather than breaking out into Black, Asian, Latinx/e, White, Indigenous, etc) to preserve a higher level of anonymity for respondents.

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Tool and best practices co-designed with Aubrey Blanche, Senior Director of Equitable Design, Product & People at Culture Amp and Founder of The Mathpath.

Have questions or comments about the Equitable Tech Apprenticeship Toolkit? Send us a note.