Richa Sachdev: The Whole Enterprise Plays a Role in Making ML Ethical

Richa Sachdev, Head of ML Engineering at Vanguard, discusses her
approach to ensuring ML models are developed ethically and used
responsibly

As organizations get to grips with the practical issues around
ensuring AI and ML is used ethically, a lot of effort needs to go
into helping business stakeholders understand these technologies.
In this week’s Business of Data podcast, Richa Sachdev, Head of
Machine Learning Engineering at investment firm Vanguard, shares
how she’s ensuring her team puts ethical data at the center of
its strategy.
Principles for Ethical Model Development

Sachdev’s team’s primary role includes  developing
recommendation systems for funds and using data to track customer
interactions to support Vanguard’s sales and marketing functions.
For Sachdev, doing this ethically means focusing on issues such
as privacy, explainability and bias.


“As engineers, we can be proactive about governance by redacting
unnecessary information when we’re creating a model,” she says.
“Of course, we don’t want to redact everything because the model
will lose value. But I don’t need a person’s Social Security
number, their religion or their criminal history.”


“We have to ensure that we are not introducing any known or
unknown bias in our model baseline,” she continues. “There are a
lot of statistical tests that are available in our toolkit for
training or testing models. So when we get the outputs, we can
compare results to see if something applies to a general
population or just a small sample to avoid problems downstream.”
Everyone is Responsible for Using AI Ethically

Sachdev is proud of the strides her organization is making
towards data analytics maturity. While there are still
departments that don’t understand analytics function, many are
making the most of it.


Leveraging analytics cannot be a standalone function, she says.
But at the same time, everyone who uses AI within a business has
a role to play with respect to ensuring those systems are applied
ethically.


“There isn’t a single party that can ensure that everything goes
well with ethical data,” Sachdev notes. “Achieving this should be
part of the CDAO’s strategy and part of leaders’ key
responsibilities. Everything should be connected by a common
thread.”


She concludes: “I was in an internal conference, hosted by my
department and the data and governance department, where we
discussed what ethical AI really is. A lot of deliberate work
needs to go into bringing everyone to the party.”
Key Takeaways


Consider the ethical implications of each use
case. Behaving ethically will often require data
scientists to redact unnecessary personally identifiable
information (PII) or build explainability into models


Proactively combat ML bias. Enterprises should
develop processes to search for and remediate the many kinds of
bias that can lead to unfair model outputs


Everyone is responsible for using AI
responsibly. Stakeholders much be educated on how to
get the most out of AI systems and how to do so ethically