Guardians of AI: Jyot Singh of RTS Labs On How AI Leaders Are Keeping AI Safe, Ethical, Responsible, and True
An Interview With Yitzi Weiner
Building trust. I have worked hard to build trust with clients, friends, and my broader network. Being present — and showing up when people need you — goes a long way. People remember when you help them in a time of need. Do what you say you’re going to do. Deliver.
As AI technology rapidly advances, ensuring its responsible development and deployment has become more critical than ever. How are today’s AI leaders addressing safety, fairness, and accountability in AI systems? What practices are they implementing to maintain transparency and align AI with human values? To address these questions, we had the pleasure of interviewing Jyot Singh.
An entrepreneur, investor, and advisor to enterprise and mid-market businesses, Jyot Singh is the founder and CEO of RTS Labs. He’s driven by the pursuit of innovative solutions, leveraging the technology of tomorrow to address today’s business challenges. Throughout his journey as a technologist, entrepreneur, and mentor, Jyot has gleaned insights from numerous companies and industry pioneers navigating intricate tech evolutions. He is a Member, Board, and Tech Chair at Young Presidents Organization (YPO), and he previously served as a Board Member for the Virginia Council of CEOs. He started his career as a software engineer.
Thank you so much for your time! I know that you are a very busy person. Before we dive in, our readers would love to “get to know you” a bit better. Can you tell us a bit about your ‘backstory’ and how you got started?
Growing up, I was always interested in science and math. Physics was my favorite subject. I was on my way to becoming an electrical engineer, but then I met my wife, fell into computer science, and loved it. We met in college, and she was a computer science major, so I thought “maybe I should learn computer science…” It was a huge change — I had never coded or used a computer before that, but it reshaped the course of my life.
I am a technologist at heart, but I find that I thrive at the intersection of entrepreneurship and technology. The first company I started was a data analytics company back in 2007. That led to my love of data. I discovered that knowing your data is crucial to informed decisions. Data does a lot. I also learned team-building skills, which proved useful when I started RTS Labs in 2010.
15 years ago, we expanded to machine learning, the logical next step from data science. From there, we have done a lot of high-end generative AI work and generative AI projects, ranging from monitoring ocean health to implementing AI in enterprise manufacturing supply chains. Our approach, grounded in starting small, proving out, and then scaling quickly, fits really well with the way the world is going with AI. We’ve had 15 years of hard work in preparation of this generative AI breakthrough. It feels very much in line with my core philosophy.
None of us can achieve success without some help along the way. Is there a particular person who you are grateful for, who helped get you to where you are? Can you share a story?
I started my first data company with two other founders. We were successful, but we had a founder breakup (on good terms) because of family obligations, so I found myself working by myself on sales, writing code, everything. In the process, I met a guy who was the CIO of a company who didn’t like my product, but he liked me. I heard nothing, just crickets, after the initial pitch, but then three months later, he called me in the middle of the night and asked me to come work on a data science project at another startup.
It was a gamble, but I ended up staying there for three to four straight days, day and night. That company went from $0 to $400 million in two years and became a really good client. I had validated that CIO’s trust in me, and it ended up developing into a solid relationship. He became a mentor and is now one of my best friends, and the experience led to the formation of RTS Labs.
The lesson I learned from that was that there’s always a reason, and there’s always a way the dots connect. You might be angry at the time when you don’t get a call back — I certainly was — but sometimes even the worst thing can turn into the best opportunity if you’re open to it.
You are a successful business leader. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?
- Building trust. I have worked hard to build trust with clients, friends, and my broader network. Being present — and showing up when people need you — goes a long way. People remember when you help them in a time of need. Do what you say you’re going to do. Deliver.
- Ideas are a dime a dozen. Execution is the key. Almost everyone who is successful knows how to execute. They show action. They don’t let fear and hesitation hold them back. For their successes and failures alike, they say they’re going to do something, and they do it
- Ability to adapt. The ability to fall and to get up is something that’s essential for business leaders. You might get lucky the first time, but then when the tide turns against you or you have to start over, the ability to rinse and repeat is part of being a true business leader. As the saying goes, “When you work hard, you get lucky.”
Thank you for all that. Let’s now turn to the main focus of our discussion about how AI leaders are keeping AI safe and responsible. To begin, can you list three things that most excite you about the current state of the AI industry?
- AI’s potential to help develop innovative new solutions to persistent problems. From supply chain logistics to environmental science, certain quandaries have proven difficult to resolve, in spite of countless bright people setting their minds to it. Whether because of the scale or the complexity of the issue, these problems have limited progress and caused headaches for years. But now, with the speed and pattern recognition of generative AI, skilled practitioners can analyze information exponentially faster. And because of its widespread availability, we’re seeing people develop really interesting applications for the technology to solve these problems.
- AI-driven breakthroughs driving new discoveries. AI technology is taking years of data and billions of data points and using it to help come up with unique answers to some of the biggest problems facing us today. For instance, using AI to simulate protein folding has helped discover new medications and combat drug-resistant bacteria, and AI weather forecasting and disaster modeling have helped warn government officials and meteorologists about looming severe weather events and natural catastrophes.
- AI process improvement to keep our daily lives running smoothly. It’s not as glamorous as working on a cure for cancer (which AI is also helping with), but by helping reduce issues in our day-to-day processes, AI is creating better outcomes for people and businesses. It’s flagging errors and breakdowns in the systems that optimize traffic patterns and urban mobility, keep our grocery stores and pharmacies stocked, and identify potential dangers like product safety issues.
Conversely, can you tell us three things that most concern you about the industry? What must be done to alleviate those concerns?
- Unethical use of AI in service of the company bottom line. Some companies have implemented AI for uses where it’s still not capable of delivering results as good as a human doing the same job. This has a real human cost, and longer-term, it also damages the businesses doing it. Businesses should ensure that they are implementing AI to improve efficiency and boost their employees’ performance, not to supplant people in roles that still require a human touch or human oversight.
- Development of AI technologies for human rights abuses. Some authoritarian regimes are using AI to surveil and control citizens, and human rights groups have raised the alarm about AI military applications potentially leading to war crimes, particularly when allowed to operate without human oversight. To mitigate this, AI innovators and democratic governments should work hand-in-hand to prevent misuse and abuse of AI technologies. Governments should also ensure that they are being judicious in their use of AI. Long-term, policymakers may need to implement laws specific to use of AI in the military.
- Environmental impact of AI infrastructure. AI is an amazing tool, but it also comes with an environmental cost, as AI is currently water- and energy-intensive. There are ways to reduce the environmental impact, and many AI companies are currently investing both in developing clean energy sources and in streamlining and improving AI technology to reduce its energy usage.
As a CEO leading an AI-driven organization, how do you embed ethical principles into your company’s overall vision and long-term strategy? What specific executive-level decisions have you made to ensure your company stays ahead in developing safe, transparent, and responsible AI technologies?
To ensure we have ethical principles baked into our AI, we start by focusing on our team. Everyone on our team believes in our shared values and works to create AI solutions that fulfill those principles. When we connect with enterprises looking to bring in AI solutions, they want to know that they can trust us to help them implement reliable, trustworthy, safe AI. Our team works hard to deliver that. With our AI partners, we evaluate their solutions and look ahead to potential issues before we ever start building client solutions. Then, once we are ready to begin integrating those solutions, we start on a smaller scale, test, and scale up. That way, we can resolve any issues early on and refine the process.
We also have ethical and safety standards about what projects we’re willing to take on, so that has generally helped us avoid potential clients wanting us to design unsafe AI solutions.
Have you ever faced a challenging ethical dilemma related to AI development or deployment? How did you navigate the situation while balancing business goals and ethical responsibility?
On one of our projects, we were working with a services firm that wanted to develop the AI agent answers to be skewed, so they’d be more in favor of the company at the expense of their customers. That was a hard situation. We ended up fine-tuning and changing the AI agent so that it would give balanced, good answers to client questions, not just the answer that would make the firm the most money.
Many people are worried about the potential for AI to harm humans. What must be done to ensure that AI stays safe?
The number one thing is for people to educate themselves on what AI can and can’t do and the ramifications. There are a lot of headlines designed to grab your attention, and there are certainly legitimate safety concerns, but we aren’t in danger of a Matrix or Terminator situation here. The overblown quotes about AGI coming for us all can distract from the very real issues of bad actors, corporations and authoritarian governments using AI for harmful purposes.
For instance, AI can help propagate disinformation rapidly, so it’s important to understand that risk and how to evaluate claims for signs of being a disinformation campaign. That way, you may not be able to stop the claim from being made, but you’re not participating and sharing it.
Overdependence on AI algorithms in law enforcement can also lead to greater bias, incorrect identifications, and loss of trust in the community, so it’s important to recognize the limitations of AI and implement safeguards, rather than handing over full control.
Another contributing factor to harmful AI is that people simply overestimate AI’s capabilities and hand over control. AI is a tool that can help facilitate great accomplishments and solve logistics problems, but it’s just that: a tool. We can’t — and shouldn’t — replace human oversight in the process.
Despite huge advances, AIs still confidently hallucinate, giving incorrect answers. In addition, AIs will produce incorrect results if they are trained on untrue or biased information. What can be done to ensure that AI produces accurate and transparent results?
First, if you’re building your own AI, it’s important to understand what data it’s being trained on. Make sure your training data is high-quality and up-to-date, and depending on its purpose, you may need to ensure that it has sufficient data to fulfill its tasks. Otherwise, it may make its “best guess“ based on limited information, which can lead to inaccurate responses. If your AI will be interacting with other AI, placing guardrails on those interactions can help limit the damage if the other AI model starts hallucinating. In addition, data poisoning is a growing threat, so implementing cybersecurity measures is crucial.
If you’re using AI, there are a few methods to ensure your data is accurate. First, use detailed prompts that explain exactly what you are looking for. When you get a response, ask the AI to explain its work or cite its sources. Then, try to confirm that information from a third party. When using AI, opt for solutions that preserve the original data as well as the AI analysis. For instance, if you have an AI-generated transcript of a meeting, retain the original video recording of the meeting in case you need to cross-reference something later.
Lastly, whether you’re building an AI model or using someone else’s, remember that today’s AI has its limitations and make sure that you’re using it appropriately. If incorrect information could have a severely negative outcome, such as in a hospital setting, then you may want to limit use of AI.
Here is the primary question of our discussion. Based on your experience and success, what are your “Five Things Needed to Keep AI Safe, Ethical, Responsible, and True”? Please share a story or an example for each.
- Transparency. Implement clear documentation and open communication about how AI models are built and how decisions are made. For example, we had a logistics client that wanted to optimize delivery routes. To get trust and adoption from delivery drivers, we had to explain the AI’s decision-making process. Being transparent was not only the right thing to do, but it led to better outcomes, maintaining morale and helping drivers understand what the AI was doing and why. This created an effective human-AI collaboration.
- Accountability. Establish protocols to track AI decisions and assign responsibility when errors occur. For instance, we created human escalation protocols for our clients to ensure timely intervention when anomalies were detected. This enabled rapid resolution of issues, prevented potential negative impacts, and ensured that there was a clear analysis of what went wrong.
- Fairness and Bias Mitigation. Regularly test AI models for biases and adjust data sets to represent diverse populations. In fields where bias has led to historical disparities, it’s important to check your data sources and ensure that you’re not training AI to recreate those patterns. When we were working on a healthcare analytics project, we implemented a bias-checking framework, which helped us ensure accurate outcomes across demographic groups.
- Privacy and Data Security. Use encryption, anonymization, and strict data governance policies. As AI grows more pervasive, this will be an increasingly important component of keeping AI ethical and safe across industries. AI can also help implement better safety practices. For example, we built a privacy-first recommendation engine for a retail partner in order to protect customer identities.
- Continuous Monitoring and Improvement. Deploy monitoring tools to track AI performance and retrain models as needed. For example, a transportation client faced rapidly evolving conditions, but real-time performance checks helped ensure model accuracy.
Looking ahead, what changes do you hope to see in industry-wide AI governance over the next decade?
We anticipate stricter regulations requiring model explainability and fairness testing. Increasingly, especially in the wake of major incidents and inappropriate use of AI models, AI has come under scrutiny for nebulous reasoning, especially where the technology has been applied overzealously.
While regional regulatory bodies currently are handling AI cases individually, we believe there will eventually be global collaboration to establish standardized ethical AI guidelines
In addition, from the industry side, we expect to see more industry-led initiatives promoting best practices and certifications.
What do you think will be the biggest challenge for AI over the next decade, and how should the industry prepare?
The biggest challenge for AI will be managing the growing complexity of AI models while maintaining transparency and interpretability — which will continue to be crucial for ethical AI. The industry should prepare for this by prioritizing the development of tools that simplify model explanations for non-technical stakeholders and foster collaboration across disciplines. Educating more people about AI and how it works will also help promote understanding of the technology.
You are a person of great influence. If you could inspire a movement that would bring the most good to the most people, what would that be? You never know what your idea can trigger. 🙂
AI has the potential to revolutionize industries, improve access to healthcare, optimize supply chains, and create more equitable opportunities. But for it to truly benefit humanity, we need to prioritize fairness, transparency, and accessibility in its development and deployment. This movement would bring together businesses, policymakers, and technologists to ensure AI is used to amplify human potential — whether by making life-saving medical diagnostics more available, streamlining logistics to reduce waste and emissions, or empowering underrepresented communities with smarter education and job opportunities.
How can our readers follow your work online?
You can follow me on LinkedIn (https://www.linkedin.com/in/jyotsingh/) and visit https://rtslabs.com/. We also post company updates on the RTS Labs LinkedIn.
Thank you so much for joining us. This was very inspirational.
Guardians of AI: Jyot Singh of RTS Labs On How AI Leaders Are Keeping AI Safe, Ethical… was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.