Arsham Ghahramani, PhD, is the co-founder and CEO of Ribbon. Based in Toronto and originally from the UK, Ghahramani has a background in each artificial intelligence and biology. His skilled experience spans a variety of domains, including high-frequency trading, recruitment, and biomedical research.
Ghahramani began working in the sector of AI around 2014. He accomplished his PhD at The Francis Crick Institute, where he applied early types of generative AI to check cancer gene regulation—long before the term “generative AI” entered mainstream use.
He’s currently leading Ribbon, a technology company focused on dramatically accelerating the hiring process. Ribbon has raised over $8 million in funding, supported over 200,000 job seekers, and continues to grow its team. The platform goals to make hiring 100x faster by combining AI and automation to streamline recruitment workflows.
Let’s start initially — what inspired you to found Ribbon, and what was the “aha” moment that made you realize hiring was broken?
I met my co-founder Dave Vu while we were each at Ezra–he was Head of People & Talent, and I used to be Head of Machine Learning. As we rapidly scaled my team, we continuously felt the pressure to higher quickly, yet we lacked the correct tools to streamline the method. I used to be early to AI (I accomplished my PhD in 2014, long before AI became mainstream), and I had an early understanding of the impacts of AI on hiring. I saw firsthand the inefficiencies and challenges in traditional recruitment and knew there needed to be a greater way. That realization led us to create Ribbon.
You’ve worked in machine learning roles at Amazon, Ezra, and even in algorithmic trading. How did that background shape the best way you approached constructing Ribbon?
At Ezra, I worked on AI health tech, where the stakes couldn’t be higher–if an AI system is biased, it could be a matter of life or death. We spent quite a lot of time and energy ensuring that our AI was unbiased, in addition to developing methods to detect and mitigate bias. I brought over those techniques to Ribbon, where we use these techniques to observe and reduce bias in our AI interviewer, ultimately making a more equitable hiring process.
How did your experience as a candidate and hiring manager influence the product decisions you made early on?
Finding a job is a grueling process for junior candidates. I remember, not too way back, being a junior candidate applying to many roles. It’s only turn into harder since then. At Ribbon, we’ve got deep empathy for job seekers. Our Voice AI is commonly the primary point of contact between an organization and a candidate, so we work hard to make this experience positive and rewarding. One in every of the ways we try this is by ensuring candidates chat with the identical AI throughout all the hiring process. This consistency helps construct trust and luxury—unlike traditional processes where candidates are passed between multiple people, our AI provides a gradual, familiar presence that helps candidates feel more comfy as they move through interviews and assessments.
Ribbon’s AI conducts interviews that feel more human than scripted bots. Tell us more about Ribbon’s adaptive interview flow. What type of real-time understanding is going on behind the scenes?
We have now built five in-house machine learning models and combined them with 4 publicly available models to create the Ribbon interview experience. Behind the scenes, we’re continuously evaluating the conversation and mixing this with context from the corporate, careers pages, public profiles, resumes, and more. All of this information comes together to create a seamless interview experience. The rationale we mix a lot information is that we wish to present the candidate an experience as near a human recruiter as possible.
You highlight that five minutes of voice can match an hour of written input. What type of signal are you capturing in that audio data, and the way is it analyzed?
People generally speak quite fast! Most job application processes are very tedious, tasking you with filling out many various forms and multiple-choice questions. We’ve found that 5 minutes of natural conversation equates to around 25 multiple-choice questions. The data density of voice conversation is tough to beat. On top of that, we’re collecting other aspects, equivalent to language proficiency and communication skills.
Ribbon also acts as an AI-powered scribe with auto-summaries and scoring. What role does interpretability play in making this data useful—and fair—for recruiters?
Interpretability is on the core of Ribbon’s approach. Every rating and evaluation we generate is at all times tied back to its source, making our AI deeply transparent.
For instance, after we rating a candidate on their skills, we’re referencing two things:
- The unique job requirements and
- The precise moment within the interview that the candidate mentioned a skill.
We consider that the interpretability of AI systems is deeply essential because, at the tip of the day, we’re helping corporations make decisions, and firms wish to make decisions based on concrete data. Something we consider is critical for each fairness and trust in AI-driven hiring.
Bias in AI hiring systems is an enormous concern. How is Ribbon designed to attenuate or mitigate bias while still surfacing top candidates?
Bias is a critical issue in AI hiring, and we take it very seriously at Ribbon. We have built our AI interviewer to evaluate candidates based on measurable skills and competencies, reducing the subjectivity that always introduces bias. We recurrently audit our AI systems for fairness, utilize diverse and balanced datasets, and integrate human oversight to catch and proper potential biases. Our commitment is to surface the most effective candidates fairly, ensuring equitable hiring decisions.
Candidates can interview anytime, even at 2 AM. How essential is flexibility in democratizing access to jobs, especially for underserved communities?
Flexibility is essential to democratizing job access. Ribbon’s always-on interviewing allows candidates to participate at any time convenient for them, breaking down traditional barriers equivalent to conflicting schedules or limited availability, which is particularly helpful for working parents and people with non-traditional hours. In actual fact, 25% of Ribbon interviews occur between 11 pm and a pair of am local time.
This is particularly crucial for underserved communities, where job seekers often face additional constraints. By enabling round the clock access, Ribbon helps ensure everyone has a good probability to showcase their skills and secure employment opportunities.
Ribbon isn’t nearly hiring—it’s about reducing friction between people and opportunities. What does that future appear like?
At Ribbon, our vision extends beyond efficient hiring; we wish to remove friction between individuals and the opportunities they’re suited to. We foresee a future where technology seamlessly connects talent with roles that align perfectly with their abilities and ambitions, no matter their background or network. By reducing friction in profession mobility, we enable employees to grow, develop, and find fulfilling opportunities without unnecessary barriers. Faster internal mobility, lower turnover, and ultimately higher outcomes for each individuals and firms.
How do you see AI transforming the hiring process and broader job market over the following five years?
AI will profoundly reshape hiring and the broader job market in the following five years. We expect AI-driven automation to streamline repetitive tasks, freeing recruiters to concentrate on deeper candidate interactions and strategic hiring decisions. AI will even enhance the precision of matching candidates to roles, accelerating hiring timelines and improving candidate experiences. Nevertheless, to comprehend these advantages fully, the industry must prioritize transparency, fairness, and ethical considerations, ensuring that AI becomes a trusted tool that creates a more equitable employment landscape.
