One of the greatest joys of growing an AI company is working closely with talented data science teams around the globe. Their projects are fascinating and complex, but equally so are their future priorities, problems and needs. Robust Intelligence has helped leaders of these teams automate difficult (and often boring!) tasks that would otherwise eat up valuable time and drive down team morale. 

Whether via model stress testing, our AI firewall or continuous testing, in 2021 Robust Intelligence gave the gift of time and freedom to teams looking to push the needle on what is possible in machine learning. 

As such, we want to share the lessons learnt last year to help make your team (and of course, your models) stronger in 2022. 

1. Remove roadblocks for inclusion hiring
   

Data science as a field, like many others in tech, lacks diversity. It is impossible to eliminate bias in the AI lifecycle when minority groups are not represented on the teams that envision, develop, monitor and deploy these models. Yet, we know D&I can seem hard to achieve, especially if the wrong precedent has been set early on. 

But it is not too late. Here are a couple things to focus on:

• Track hiring demographics and be accountable to them. Sure, you can hire a woman today, but if you hire two men tomorrow you are back to square one.
  

• Be open about the problem, both internally and with candidates. Like most things in life being silent about something doesn’t make it disappear, rather, it tends to get worse. Make sure to host opportunities for employees of every seniority level to share feelings and visions for growing your team. This fosters a culture of trust and demonstrates that you are actively working towards equitable representation.
  

• Write better job descriptions. Balanced hiring isn’t always a chicken-egg problem. Your job postings may not attract certain candidates or may be unnecessarily intimidating. Focus on impact language (eg. what the candidate will “do”, not “look like”) and carefully look at your skill requirements - are they flexible and considerate of unique life journeys?
  

•  Prepare your interviewers. If a candidate asks about diversity, it is important interviewers are sensitive to those concerns and can clearly communicate your team's goals and current cultural pulse. 
  

Lastly, remember that diversity hiring is not for optics. A diverse team is proven to reduce risks, increase creativity and drive meaningful innovation.

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2. Lengthen the tenure of your data scientists 
   

It is now commonly accepted, albeit reluctantly, that the average tenure of a data scientist is two years. This has different implications for each company, but the pain felt by all is the inability to retain employees who have gained invaluable “tribal knowledge” of your company's processes. 

In truth, the biggest enemy to employee retention is boredom. The DS & ML world is on the frontier of innovation, meaning that the grass is often greener as other companies engineer the next chapter for humans + machines.

Although this excitement and progress is certainly real, it is not possible without monumental efforts dedicated to wrangling data and crafting infrastructure that supports the next greatest steps. From data preparation to infrastructure and model maintenance, data scientists and machine learning practitioners are dedicating an oversized percentage of labor hours to seemingly low-impact or tedious tasks.

Here are some actions to keep your team interested, engaged, and loyal:

• Create an individualized roadmap for growth in the company. If your employee doesn’t see a future in your organization they will find one elsewhere. Have regular career chats, check-ins and understand individual interest areas, roadblocks, and ambitions. 
  

• Automate boring work. At Robust Intelligence we are experts at this. Our RIME platform eliminates almost 30% of labor hours wasted on manually testing and debugging models. But we are not alone. Other companies automate tasks like data labelling, data acquisition, model training and so on.
  

• The term “thank-less work” should be eliminated from your team's vocabulary. How? Give thanks! Recognitions of efforts both grand and bland can go a long way for boosting loyalty and satisfaction. 
  

The costs of hiring and onboarding are paid off in the long run, so it is in your interest to lengthen the tenure of your data scientists. In a market where data skills are highly valued everywhere, (a report found that in 2021 70% of business leaders preferred applicants with data skills), leaders must create environments that convince employees to stay and grow within an organization.

3. Get ahead of AI regulation
   

Regulation is on the horizon with the EU trailblazing ahead of most other regions. Businesses had a loud wake-up call last April when a draft proposal of the EU Artificial Intelligence Act was submitted and is slated to become fully operational. This regulation has implications on businesses in the EU and beyond, because other regions will likely follow suit and implement more stringent monitoring of the use of AI. 

Requirements for companies will vary depending on how high risk the impact of the AI can be. For example, medical and financial organizations will be immediately targeted by more rigorous regulations due to the highly consequential impact they have on large populations and human wellbeing. This means that these industries have more to lose if they are not proactively planning for compliance. 

Last minute scrambling to meet regulatory requirements will place undue pressure and stress on data science teams. Because we know this regulation is coming, it’s important to make sure that AI model systems are already being monitored for compliance, accuracy, and fairness so that nothing comes as a shock when these laws become operational. 

Journeying Together

Here at Robust Intelligence we seek to simplify the lives of data science leaders. While we recognize there is a wide range of growth areas for data science leaders in 2022, we offer an efficient and automated way to eliminate failures from AI models. 

As AI modeling continues to dominate business decision-making this year and beyond, it is more important than ever to create robust and easy ways to monitor data input and model behavior and ensure that outputs (especially high impact outputs) are free of the failures inherent to AI. 

Our team at RI wishes you all the best on your 2022 journey and please do not hesitate to reach out if solving AI failure is on your roadmap this year!