Iva Kojic: How Applying the Counterfactual Test to Organizational Systems Prevents Ethical Failures in AI Implementation

As a part of this series, we had the pleasure to interview Iva Kojic.

Iva Kojic is an AI innovation consultant who helps founders and leaders integrate AI into their organizations, from strategy and system design to practical implementation. She holds an MSc in Emotional AI with a thesis on facial emotion recognition, has taught IT and software engineering to more than 200 students, and built an accelerator program from the ground up within Vienna’s startup ecosystem. She spoke on the human side of AI at the WomenTech Global Conference 2026 and has studied the ethics of AI at LSE.

Thank you so much for joining us in this interview series! Before we dive in, our readers would like to learn a bit about your origin story. Can you share with us a bit about your childhood and how you grew up?

From the age of two, I spent hours with encyclopedias, studying every species of bird I could find. That early fascination with classification and detail shaped the way I think to this day. I grew up in Serbia, where parents often had to be remarkably creative to give their children the best possible education, and mine did an exceptional job.

I was multidimensional from the start. I was drawn to science, I trained in several sports, and I attended music school, where I learned to play the piano. Looking back, the common thread was observation. I was always watching people and noticing patterns in how they behave. Today I apply that same instinct to AI, which at its core is also about recognizing patterns.

Can you share with us the “backstory” of how you decided to pursue a career path in AI?

My work has always lived at the intersection of people, organizations and technology. That intersection is where I do my best thinking.

I trained in software engineering, and my first encounter with AI was in 2019, through machine learning. At the time, the field felt vast and abstract, but it opened a question I have been pursuing ever since.

As a teaching associate, I began to study why some concepts consistently failed to land with my students. That led me deep into cognitive psychology: how we process information, how we learn and how the brain makes decisions. From there, I became fascinated by neuroscience and by the way artificial neural networks are modeled as a synthetic version of the brain.

Those early questions defined my path: how do we use AI properly, and how do we use it ethically? That question remains at the center of my work today.

Can you tell our readers about the most interesting projects you are working on now?

I work as a systems architect for founders. Every engagement starts with diagnosis: defining precisely what the founder wants to achieve, and then how they want it achieved, because the problem a founder describes is frequently a symptom of something deeper. In one recent case, an agency founder was frustrated that her team kept missing deadlines. Our analysis showed that the underlying issue was delegation and task tracking. Of roughly 4,500 tasks, 161 had no owner and 179 had no assigned month. We addressed it in three stages: first, a single dashboard giving the team full visibility; second, an integration with ClickUp; and third, which is now underway, moving the team’s daily work directly into AI. At every stage, I ask founders the same question: does this create measurable value for the business?

I apply the same architecture to my own work. My operations are organized into four domains, health, career, finance and personal, all structured in files, and I define exactly how deep AI may go in each one. In some domains it receives only basic information; in my professional work it has full context. I run scheduled agents that monitor AI developments daily, and I have built custom skills, including one that produces content in my own voice. Alongside this, I studied the ethics of AI at LSE to deepen my understanding of where the boundaries of responsible AI use lie. Ultimately, my work is about making AI accessible and genuinely useful to the people who rely on it.

None of us are able to 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 about that?

In AI, I would name Mo Gawdat, whose thinking on the human implications of AI has influenced me deeply. In innovation, it is Clemens, my former CEO in Vienna, who mentored me in how to evaluate and support startups at an early stage with rigor.

In leadership, it is my father. He has held leadership positions for more than twenty years, and he leads with remarkable skill, patience and genuine understanding of the people around him.

What has always impressed me is his ability to meet every person at their level. He speaks with a janitor and with a professor with exactly the same respect and attention, and both leave the conversation feeling seen. I consider that the most important leadership skill anyone can develop.

As with any career path, the AI industry comes with its own set of challenges. Could you elaborate on some of the significant challenges you faced in your AI career and how you managed to overcome them?

The most significant challenge is ethics, and it is genuinely difficult. There is no checklist that defines where the line is. While studying the ethics of AI at LSE, I analyzed cases such as an AI system that ranks employees by speed and activity. On the surface it appears efficient. In practice it measures the wrong attributes, so the people handling the most complex work fall to the bottom and receive warnings. No one intended that outcome. This is precisely why ethics matters: the harm rarely announces itself. It emerges quietly, from good intentions applied at scale.

For that reason, every AI implementation I lead follows three phases. The first is evaluation: understanding what the organization truly needs and confirming that we are solving the right problem. The second is boundaries: defining what data AI must never access, who is accountable for each decision, and where human judgment remains essential. With my clients, for example, we never work with internal company files; all training and exercises use synthetic data. The third is adaptation: testing, measuring what works for the team and optimizing continuously.

One framework from my ethics studies at LSE that I apply regularly is the counterfactual test. You keep the technology constant and change only the people, rules and incentives around it, then observe how the outcome shifts. More often than not, the root of the problem lies in the institution, and AI simply amplifies it.

The barrier I encounter most often is a lack of curiosity. I was raised to be curious about people, machines and systems, and about how the world works. Moving beyond one’s comfort zone with AI can feel overwhelming, but the return on that effort is substantial.

Ok, let’s now move to the main part of our interview about AI. What are the 3 things that most excite you about the AI industry now? Why?

Speed, even if that sounds counterintuitive. We are in the middle of a technological revolution, and the choice is to engage with it or to be left behind.

The three developments that excite me most are:

1. AI as a thinking partner. Used well, AI helps people break complex problems into components, see the core issue faster and make better informed decisions.
2. Innovation powered by AI. AI enables businesses and individuals to experiment with many ideas at once, at a fraction of the previous cost, and to build new capabilities quickly.
3. Lower barriers for founders. In the startup ecosystem, the friction of testing an idea has dropped dramatically. A concept that once required months and a team can now be validated in days.

What are the 3 things that concern you about the AI industry? Why? What should be done to address and alleviate those concerns?

The three concerns I take most seriously are:

1. Speed. The same speed that excites me can also backfire. Our technology is advancing exponentially, while the human brain evolved over hundreds of thousands of years and has not changed. That mismatch is my deepest concern.
2. Deepfakes. Many people lack the critical thinking skills and the awareness needed to recognize synthetic content. We will need robust detection systems, and in the future we may be required to verify that we are human every time we submit a form online.
3. AI agents with unrestricted access. Today an agent can read your email, your entire cloud storage and your calendar. That capability is powerful, and it also concentrates risk: a single flawed instruction or a single malicious actor can reach everything. I deliberately define how far AI can go in each area of my work, and I believe every organization will need clear safeguards of this kind.

What should be done? From my ethics studies at LSE, I took the concept of the regulatory gap. Companies operate globally while laws remain local, so people in different countries are protected unequally, or not at all. Clear accountability must exist for every decision made with AI. At the individual level, my advice is consistent: develop critical thinking. Seek evidence, stay curious and question assumptions, including your own. Our brain operates in two modes, one fast and impulsive and one slow and deliberate. Awareness of that distinction is the first step toward choosing the second.

For a young person who would like to eventually make a career in AI, which skills and subjects do they need to learn?

I believe the future belongs to multidimensional professionals. That means learning to articulate ideas clearly, to delegate, to define what you want and to explain it, whether to people or to machines. Logic and philosophy are essential, because they teach you to understand concepts and see the whole system. Always step back and look at the bigger picture of both life and AI, and stay curious.

In terms of subjects, I would recommend management and organizational design, technical foundations such as engineering and physics, and a broad base of knowledge beyond that. The wider your perspective, the more valuable your contribution.

As you know, there are not that many women in the AI industry. Can you advise what is needed to engage more women in the AI industry?

We need systemic changes that make the AI industry more accessible to women. I believe this is an especially promising moment for women in AI, and I expect a shift, because many of the women I meet bring a strong ability to see the bigger picture.

That ability to see the whole system is exactly what the AI industry needs, and it is one reason many women are effective leaders in this era. Representation is also critical. When women see other women in AI, they recognize that the path is open to them as well. Each of us can contribute simply through our own example.

After my talks, the question I hear most often from women is how to become more visible. That tells me how much visible role models in AI matter.

Ethical AI development is a pressing concern in the industry. How do you approach the ethical implications of AI, and what steps do you believe individuals and organizations should take to ensure responsible and fair AI practices?

Transparency comes first. Beyond that, it is important to recognize that responsibility and fairness are distinct, and that legal compliance and ethical conduct are distinct as well. Ethical practice requires both fairness and responsibility.

Organizations should be transparent about how they use AI and accountable for implementing it at a pace that works for the team and for each individual within it.

Ultimately, it returns to critical thinking: understanding how your own mind works, examining your reasoning and continually asking yourself whether what you are doing is the right approach.

Can you please share the “Five Things You Need To Create A Highly Successful Career In The AI Industry”? If you can, please share a story or an example for each.

1. Openness. Be willing to explore new fields and acquire new skills. After two years in academia, I chose to move into entrepreneurship and joined a startup program, a decision that significantly broadened my perspective.

2. Critical thinking. Challenge ideas, including your own. While reviewing startup pitch decks, I encountered one that seemed exceptional at first glance. A deeper analysis revealed fundamental weaknesses that the presentation had concealed.

3. The ability to articulate ideas. Human conversation remains essential. Every time you explain an idea to colleagues or peers, it becomes sharper. When I began rehearsing my WomenTech Global Conference talk, I was nervous and relied on my slides. By the fifth rehearsal, I was delivering it with confidence.

4. Presence. Whatever you are doing, give it your full attention, even if only for ten minutes. Something as simple as putting your phone away during a call changes the quality of your thinking and your conversations.

5. Relationships. Be curious about people and what you can learn from them. Listen actively, make people feel seen and offer help where you can. That instinct to help is fundamentally human and something AI cannot replicate. My father exemplifies this: he can speak with a janitor and a professor, and both feel equally respected.

Continuous learning and upskilling are vital in a dynamic field like AI. How do you approach ongoing education and stay up-to-date with the latest advancements in the AI industry? What advice do you have for those looking to grow their careers in AI?

Continuous learning is central to how I work. Both of my parents work in education, so learning has been part of my life from the beginning. Whenever I come across a demanding learning opportunity, my instinct is to take it on, precisely because it will challenge me.

That mindset is what keeps me current. Learning genuinely energizes me, and that enthusiasm is what sustains it over time. My advice to anyone building a career in AI is to treat learning as a permanent part of the job.

What is your favorite “Life Lesson Quote”? Can you share a story of how that had relevance to your own life?

My favorite life lesson, in my own words, is: “You are kind to yourself when you do the things you say you will do, because there is nothing kinder than keeping a promise you made to yourself.”

For a long time, I believed that being kind to myself meant allowing myself to rest and do nothing. I learned that real kindness is keeping the commitments I make to myself. When I overcommit, I treat it as feedback and adjust. Today I am far more aware of how much mental load I can carry in a day, a week or a season, and I plan accordingly.

You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger. :-)

An AI academy for every team: practical AI education available to people everywhere. In the same way Apple helped older generations become comfortable with their devices, we need to help people understand AI, use it well and recognize its risks, such as deepfakes.

How can our readers further follow your work online?

You can follow my work on LinkedIn at https://www.linkedin.com/in/iva-kojic and on my website at https://ivakojic.com.

This was very inspiring. Thank you so much for joining us!