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Approachable, Ethical, Human-Centered AI

 

From Pigeon Navigation to Pandata: A Chat with Hannah Arnson, Director of Data Science

When Hannah Arnson was just a little girl, someone gave her the perfect gift. Her Fisher-Price® Micro Explorer Set™ featured a 30-power microscope, a specimen bottle, and two clear slides for Hannah to use to discover her world. Hannah has been exploring ever since. We sat down with Hannah over a coffee to learn about her journey from child scientist to Director of Data Science at Pandata.

What influenced you to pursue neuroscience?

From an early age I knew I wanted to be a scientist. I have always been interested in how things work. In high school, I was exposed to laboratory work and basic science research. I was excited to uncover what was going on at the cellular level—beyond what you can see. I channeled my lifelong sense of curiosity and problem solving to pursue a career in science.

Tell us about your academic path

I began with undergraduate training in neuroscience and mathematics. I then pursued a Ph.D. in neuroscience. Then I moved onto additional postdoctoral research. As a neuroscientist, I studied topics ranging from how the brain perceives smells to how pigeons navigate.

What led you to transition from neuroscience to Pandata?

I came to a crossroads in my career. I had to decide if I wanted to stay in academia or have a stable job doing what I love to do. I chose to pursue my passion. I attended a Big Data Mega Meetup in Cleveland, where I came upon Pandata’s booth. I met Cal Al-Dhubaib, Pandata’s CEO, and Nicole Ponstingle McCaffrey, Pandata’s COO. We really hit it off. They later contacted me for a job. I started at Pandata as a junior data scientist, moved up to data scientist, and today I am Director of Data Science.

What’s the difference between your two worlds—academia and business?

Some things are the same. In both academia and in business, you must understand what’s going on by identifying patterns in data, and by applying those patterns to something impactful or tangible. But the big difference is in expectations and timelines. They are totally different. In academia, things just kind of go on. But in business, there is pressure to avoid rabbit holes with your research. You have to be mindful of client budgets and timelines when doing data science projects.

What does a typical week as the Director of Data Science look like?

A typical week for me is a mix of project work, meeting with clients, meeting with my team, narrowing down modeling approaches, brainstorming, and keeping on top of what’s going on in the world of data science. Throughout the week, I am constantly looking for ways to bring innovation to the company, and to leverage new and existing tools at Pandata.

Do you have a favorite project or solution you’ve worked on recently?

 

My favorite project is actually one I have been working on for more than three years. Our client is FirstEnergy, the electric utility headquartered in Akron, Ohio. The project is insider threat detection. Cybersecurity threats can usually be stopped by rules, but when an employee is doing something within the company, it’s much more difficult to detect. We are using AI to detect long-scale patterns for normal, abnormal, and malicious behavior so that the FirstEnergy security team can better identify insider threats.

What unique advantages do you bring to the team with your neuroscience background?

 

My time at grad school and the work required to earn my PhD taught me how to think through problems. It developed in me strategic problem-solving skills that I bring to the team every day.

What are the most challenging aspects of working in the data science and AI industry?

 

One challenge for me is keeping on top of everything. The industry and data science itself are so rapidly changing. That pace of change is exciting, but it’s also overwhelming. Another challenge is keeping up with developments in new tools and resources. If you can relate, consider signing up for Pandata’s Voices of Trusted AI monthly digest for industry articles and insights on Trusted AI.

What is one misperception you often hear about AI or data scientists?

 

AI has a reputation for being a malevolent robot operating behind the scenes. People also think AI is so distanced from humans, when really it’s not. The truth is that Trusted AI is helping organizations get value from their data. AI is helping organizations understand what’s going on. That makes their lives easier, and makes it easier to make decisions. Here at Pandata, we firmly believe that AI should be ethical, unbiased, and made for humans—and we hope that others feel the same way.

What are your primary goals as a data scientist?

 

On the professional side, my primary goal is delivering value to our clients. Personally, as the Director of Data Science, my primary goal is to cultivate an innovation mindset, so that everyone meets their goals. As a consultant, my goal is to ensure that AI is approachable.

How do you make AI approachable to someone who isn’t familiar with it?

 

I would tell them that AI is all about looking for patterns in data. AI is about trying to understand what’s going on, and then using that information to make decisions.

 What are some emerging trends in data science and AI that you look ahead to?

 

One top trend is the move to making AI explainable. I am looking forward to that, so that people understand AI, and understand the decisions that are being made in the AI model.

A related trend I am glad to see is the move away from Black Box AI. Black Box AI uses operations that are not visible to data scientists. It can be extremely problematic. It’s led to racist, biased, and discriminatory AI solutions. Today and in the years to come, data scientists and organizations must understand how their AI solutions are working and what type of data is being used to power their solutions.

What advice do you have for business leaders looking to implement AI in their company?

 

The first piece of advice I have is to adopt the mindset that AI can help, while understanding that AI is not a magic solution. Correct and effective AI solutions take time and money to create. The next piece of advice I have is to understand when and how to use AI. For example, you can use AI to augment decisions, and to replace highly repeatable tasks that take up time and energy.

Gain more expert insight

 

Hannah Arnson’s journey is just one of the many unique backgrounds that make up the Pandata team of expert data scientists. If your organization feels ready to work with the Pandata team to design and implement the right AI solution, schedule an AI Exploration Session with our team.

In this session, we will discuss your current needs, and dive deeper into ways your organization can implement human-centered AI and machine learning solutions to accelerate your business goals. 

 

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Hannah Arnson, Director of Data Science at Pandata