Dr. Arsham Ghahramani, Co-founder and CEO of Ribbon – Interview Series
Dr. Arsham Ghahramamani is the co-founder and CEO of Ribbon. Headquartered in Toronto, Ghahramani was originally from the UK and has a background in both artificial intelligence and biology. His professional experience covers a range of areas including high frequency trading, recruitment and biomedical research.
Ghahramani started working in the AI field around 2014. He completed his PhD at the Francis Crick Institute, where he used early forms of generative AI to study cancer gene regulation, long before the term “general AI” entered mainstream use.
He is currently leading Ribbon, a technology company focused on a dramatically accelerating the hiring process. Ribbon has raised more than $8 million in funding, supporting more than 200,000 job seekers and continuing to grow its team. The purpose of the platform is to hiring 100 times faster by combining AI and automation to simplify the hiring workflow.
Let’s start from the beginning – what motivated you to find the ribbon, what made you realize what the “ahha” moment when recruiting was broken?
When we were both in Ezra, I met my co-founder Dave Vu – who is the person in charge of talent and I am the person in charge of machine learning. As we rapidly expand our team, we constantly feel the pressure to improve quickly, but we lack the right tools to streamline the process. I had AI very early (I completed my PhD in 2014 before AI became mainstream) and I had an early understanding of the impact of AI on recruitment. I have witnessed first-hand the inefficiency and challenges in traditional recruitment and know there must be a better approach. This awareness has led us to create ribbons.
You’ve already worked in machine learning roles in Amazon, Ezra and even algorithmic trading. How does this background shape the way you approach architectural ribbons?
At Ezra, I work at AI Health Tech, where the bets can’t be higher – if the AI system is biased, it can be a matter of life or death. We have spent a lot of time and effort to ensure our AI is impartial and have developed methods to detect and mitigate bias. I brought these technologies to the ribbon, where we used them to monitor and reduce AI interviewer bias, ultimately creating a more equitable hiring process.
How does your experience as a candidate and hiring manager affect product decisions you made early?
Finding a job is a arduous process for junior candidates. I remember not long ago, I was a junior candidate to apply for many jobs. It only became more difficult since then. At Siben, we have deep sympathy for job seekers. Our voice AI is often the first point of contact between the company and candidates, so we strive to make this experience positive and meaningful. One way we do this is to make sure candidates chat with the same AI throughout the recruitment process. This consistency helps build trust and comfort – a traditional process with candidates in the traditional process, our AI provides a stable, familiar presence that can help candidates feel more relaxed when interviewed and evaluated.
Ribbon’s AI conducted interviews more humane than scripting robots. Tell us more about the Ribbon adaptive interview process. What kind of real-time understanding is happening behind the scenes?
We have built five internal machine learning models and combined them with four public models to create a ribbon interview experience. Behind the scenes, we constantly evaluate the conversation and combine it with the background of our company, career pages, public profiles, resumes and more. All this information comes together to create a seamless interview experience. The reason we combine so much information is that we want to provide candidates with the experience as close as possible to human recruiters.
You stress that five minutes of voice can match an hour of written input. What kind of signal do you capture in your audio data and how do you analyze it?
People usually say it’s fast! Most job application processes are very tedious, tasks you fill out many different forms and multiple choice questions. We found that 5 minutes of natural conversations are equivalent to about 25 multiple choice questions. The information density of voice conversations is hard to beat. Most importantly, we are collecting other factors such as language proficiency and communication skills.
The ribbon also acts as an AI-powered scribe with automatic summer and ratings. What role does interpretability play in making these data useful and fair for recruiters?
Interpretability is at the heart of the ribbon approach. Every score and analysis we produce is always closely related to where it comes from, making our AI deeply transparent.
For example, when we rate candidates on their skills, we refer to two things:
- Initial job requirements and
- The candidate mentioned the exact moment in the interview with a skill.
We believe that the interpretability of AI systems is very important because at the end of the day, we are helping companies make decisions and companies like to make decisions based on specific data. We believe this is crucial for both fairness and AI-driven hiring.
Bias in AI recruitment systems is a big problem. How to minimize or mitigate biased ribbons while still surfaced?
Bias is a key issue in AI recruitment, and we attach great importance to it in the functional area. We have established our AI interviewers to assess candidates based on measurable skills and abilities, thereby reducing the subjectivity of often introduced bias. We regularly review our AI systems equity, leverage diverse and balanced datasets, and integrate human supervision to capture and correct potential biases. Our commitment is to express the best candidates fairly to ensure fair recruitment decisions.
Candidates can be interviewed at any time even at 2 a.m. How important is flexibility in democratizing work capacities, especially for underserved communities?
Flexibility is the key to democratizing job opportunities. Ribbon’s consistent interview allows candidates to participate in their participation anytime, anywhere, breaking down traditional barriers such as conflicting schedules or limited availability, which is especially beneficial for working parents and parents with non-traditional time. In fact, between 11:00 and 2:00 a.m. local time, 25% of ribbon interviews occurred.
This is especially important for underserved communities, where job seekers often face other restrictions. By enabling 24/7 access, the ribbon helps ensure that everyone has the opportunity to demonstrate their skills and secure job opportunities.
Ribbons are not just about recruiting, but about reducing friction between people and opportunities. So what is the future like?
In the functional area, our eyesight is beyond the scope of effective recruitment. We want to remove friction between individuals and opportunities that fit them. We foresee a future where technology seamlessly connects talent with characters that fully match their abilities and ambitions, regardless of their background or network. By reducing friction in career mobility, we enable employees to grow, develop and find fulfilling opportunities without unnecessary obstacles. Faster internal mobility, lower turnover, ultimately brings better results for individuals and companies.
How do you think about AI changing the hiring process and the broader job market over the next five years?
AI will profoundly reshape the recruitment and broader job market over the next five years. We hope that AI-driven automation will simplify repetitive tasks and allow recruiters to focus on deeper candidate interactions and strategic recruitment decisions. AI will also improve matching candidates and roles, speed up hiring schedules and improve the accuracy of candidate experience. But to fully realize these benefits, the industry must prioritize transparency, equity and ethical considerations to ensure that AI becomes a trusted tool that creates a more equitable employment environment.
Thank you for your excellent interview, and readers who hope to learn more should visit Ribbons.
