Skip to main content
HomeBlogMachine Learning

How to Ethically Use Machine Learning to Drive Decisions

Having good quality data requires strong data foundations, along with a commitment to monitoring models and removing bias.
Aug 2020  · 3 min read

Focus on solid data foundations and tooling

Having good quality data is a huge challenge in itself. We recommend companies that want to leverage machine learning, artificial intelligence, and data science to consider Monica Rogati’s AI Hierarchy of Needs, which has machine learning close to the top as one of the final pieces of the puzzle.

Source: Hackernoon

This hierarchy illustrates that before machine learning can happen, you need solid data foundations and tools for extracting, loading, and transforming data (ETL), as well as tools for cleaning and aggregating data from disparate sources.

This requires strong data engineering practices—you’ll need to leverage databases, understand how to process data correctly, schedule your workflows, and make use of cloud computing.

So before you hire your first machine learning engineer, you should first set up your data engineering, data science, and data analysis functions.

Beware of bias in your data and algorithms

Machine learning can only be as good as the data you feed it. If your data is biased, your model will be too. For example, Amazon built a ML recruiting tool to predict the success of applicants based on resumes with ten years’ worth of training data that favored males due to historic male dominance across the tech industry—which caused the ML tool to also be biased against women.

This is why data ethics has emerged as such an important topic in recent years. As more and more data is generated, the impact of how that data is used also scales dramatically. This requires principled consideration and monitoring. As Cassie Kozyrkov, Google's Chief Decision Scientist, has analogized, a teacher is only as good as the books they’re using to teach the students. If the books are biased, their lessons will be too.

Keep tabs on your model and improve it

Remember that the job of machine learning doesn’t end when your model is in production, making predictions, or performing classifications. Models that are deployed and doing work still need to be monitored and maintained.

If you have a model predicting credit card fraud based on transaction data, you get useful information every time your model makes a prediction and you act on it. On top of this, the activity you’re trying to monitor and predict—in this case, credit card fraud—may be dynamic and change over time. This process, where data that’s generated is constantly in flux, is called data drift—and it proves how essential it is to regularly update your model.

Source: DataBricks



5 More Things Business Leaders Need to Know About Machine Learning

Dive deeper into what you need to know about machine learning to sustainably grow your data function and your company at large.
Hugo Bowne-Anderson's photo

Hugo Bowne-Anderson

7 min


The Many Business Applications of Machine Learning

Learn why machine learning is so crucial to conducting better business, and how our new machine learning scientist career tracks can help you tackle your business problems.
Joyce Chiu's photo

Joyce Chiu

5 min


Understanding and Mitigating Bias in Large Language Models (LLMs)

Dive into a comprehensive walk-through on understanding bias in LLMs, the impact it causes, and how to mitigate it to ensure trust and fairness.
Nisha Arya Ahmed's photo

Nisha Arya Ahmed

12 min


The Case for Responsible AI

We recently released a report co-written by DataRobot’s VP of Trusted AI Ted Kwartler, Global AI Ethicist Haniyeh Mahmoudian, and Managing Director of AI Ethics Sara Khatry. Here’s a run-down of what to expect.

Kevin Babitz

10 min


An Introduction to Data Ethics: What is the Ethical Use of Data?

Learn everything you need to know about data ethics, including the key principles and how they’re applied to your data.

Christine Cepelak

15 min



Data Demystified: The Different Types of AI Bias

In the final part of data demystified, we outline the most common types of AI bias, and why data literacy helps avoid harmful impacts from AI.
Richie Cotton's photo

Richie Cotton

8 min

See MoreSee More