#439 AI, Bias, and Leadership: Why Business Leaders Must Rethink Recruitment Technology - article by Niels Brabandt
AI, Bias, and Leadership: Why Business Leaders Must Rethink Recruitment Technology
By Niels Brabandt
Artificial intelligence (AI) is rapidly transforming recruitment. At Europe’s largest HR expo, Zukunft Personal, one message was clear: executives see AI as both an opportunity and a risk. While some view it as a silver bullet for talent acquisition, others fear lawsuits, reputational damage, and ethical pitfalls. For business leaders, the challenge lies not in whether to adopt AI, but in how to do so responsibly.
The Mirage of “Something with AI”
Across industries, executives repeat the same vague ambition: “We need something with AI.” Yet without clear objectives, investments in recruitment technology risk becoming expensive experiments. Like Alice in Wonderland’s aimless wanderings, if you don’t know where you want to go, any path will do. Companies that buy AI tools without defined goals often end up with unused systems, wasted budgets, and frustrated HR teams.
The dream of “instant selection” where AI autonomously identifies the best candidate with 100% accuracy — remains science fiction. Today’s large language models (LLMs) can generate different answers to the same question on different days. Expecting flawless judgment from such systems in high-stakes hiring decisions is unrealistic and dangerous.
Speed vs. Bias
Where AI does excel is speed. Applicant tracking systems enhanced with AI can scan hundreds of CVs in seconds, verifying work eligibility, licenses, or certifications. For roles attracting tens of thousands of applicants, such as flight attendants in India, this efficiency is indispensable.
But speed without accuracy is perilous. Bias creeps in when training data is flawed, incomplete, or skewed by past human decisions. Feeding yesterday’s discriminatory hiring patterns into today’s AI is like pouring gasoline on a fire: it doesn’t remove bias, it amplifies it. The result? Increased risk of lawsuits and reputational harm.
Human Collaboration, Not Automation
The best outcomes occur when humans and AI work together. AI can pre-screen, structure, or draft responses, while humans refine, contextualize, and validate. Leaders should resist the temptation of full automation in sensitive processes. Collaboration, not replacement, is where AI delivers real value.
Ethics, Compliance, and the Employer Brand
Leadership in the AI era is as much about governance as it is about technology. Regulators in the EU already impose strict rules on data usage, consent, and fairness. Internal compliance policies often go further, requiring human oversight for soft-skill assessments or interviews. Ignoring these frameworks can prove costly, especially for mid-sized firms that cannot absorb reputational crises the way global giants like Amazon or Microsoft can.
Ethics also shape employer branding. Companies proclaiming “people are our greatest asset” cannot credibly outsource the entire candidate experience to machines. Applicants notice when application processes feel impersonal, repetitive, or robotic. Done badly, AI damages trust. Done well, it can enhance efficiency while preserving human connection.
Building Future Skills
AI in recruitment is not just about technology, it is about people. Employees must be trained not only to use AI but to question its outputs. A €19 online course will not build the critical literacy required for responsible adoption. Future-ready firms invest in meaningful training, equipping staff to evaluate, challenge, and complement AI decisions.
The Leadership Imperative
For decision-makers, the message is clear: AI in recruitment is neither a fad nor a fix-all. It is a tool that demands strategic clarity, ethical grounding, and human oversight. Leaders who treat AI as an assistant — not a replacement, will unlock speed, efficiency, and fairness. Those who don’t risk falling into the traps of bias, legal exposure, and reputational decline.
AI will not replace leaders. But leaders who fail to adapt to AI may find themselves replaced.
Niels Brabandt
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More on this topic in this week's videocast and podcast with Niels Brabandt: Videocast / Apple Podcasts / Spotify
For the videocast’s and podcast’s transcript, read below this article.
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Niels Brabandt is an expert in sustainable leadership with more than 20 years of experience in practice and science.
Niels Brabandt: Professional Training, Speaking, Coaching, Consulting, Mentoring, Project & Interim Management. Event host, MC, moderator.
Podcast Transcript
Niels Brabandt
At the moment we have the Zukonf Personal which is Europe's largest fair, Europe's largest expo for anything regarding winning talent, developing talent, retaining talent. It's the most important event of the year. It's happening right now. So this is the evening after another day at the Expo. A long day I can tell you. And AI is all over the place in a positive or in a negative way. And of course many people wonder is there any chance that we can fulfil all the wishes I have with AI and we need to talk about recruiting AI Especially about the bias which is heavily under fire at the moment, especially after many people saw that work, they had a lawsuit, a class action lawsuit against them.
And people wonder can AI really help me in recruiting? Is there anything that I can do to help AI and how can we actually get something done which is better than what we do already?
So far so welcome. We are going to talk about recruiting AI bias and of course what do leaders need to do about it? That is the leadership bit of the whole game. So when we talk about recruiting, AI bias and leadership, the first, first thing we have to, we, we have to set the record set on one aspect. Often people say I want to do something with AI and people have a lot of wishes. It's, it's perfectly fine to have wishes. Any one of us has wishes.
However, when I say what wishes do you have? They often say yeah Neil. So you know our, our leaders say we, we have to catch up our game on AI So we, you know, something with AI and when I ask what do you mean with something with AI? They often say yeah, you know something, just something AI ish. That AI can make things happen. And that is the first problem when you don't know where to go. In the world famous novel of Alice in Wonderland when Alice comes to a junction and says I don't know where to go.
And then the grinning cat asks where do you want to go? And she says I don't know, where do you want to arrive? She doesn't know. So the gunning cat says well then it's, then it doesn't matter which path you take. When you don't know where you want to go, it doesn't matter which path you take.
That is really philosophical already. This, this aspect of something AI is straightforward, dangerous. At the moment I see way too many companies throwing money at AI when you are a large corporation you just tick it off as a box of. This is something we do for research. We are willing to put in the risk and especially in the us we have the atmosphere, we have the approach and we also have it in the UK part that people say we are willing to invest and say it wasn't worth the money, but it was worth trying it. We learned something from it, that's perfectly fine. However, when you simply say something AI and then you move on forward, you implement tools that you actually never use, that is deeply problematic because what many people of course wish is I buy an AI.
Applicants apply with the AI and they tell me, the AI tells me what is the most, the best candidate that they have. And that of course is not great. That is of course something which as a wish, especially at the moment, it might happen in some future, but not at the moment. This instant selection and then including in the instant selection, just try something, you go to an AI, let's say you go to ChatGPT or to Copilot, you ask a question and then ask the exact same questions one week, every single day of the week. So you have usually seven different answers. And you have seven different answers with different focuses, with different directions. Bit of a change in there.
And then I say, so are all of these spot on? And you probably say, well, I think these two are pretty good. They are really excellent. Maybe two or three are, yeah, okay, ish. And maybe two are not so great. But people say, I need something with AI. Then they say it needs to select all the applicants and also it needs to be 100% right.
And that is something which is simply not going to happen, not at the moment. It might happen in some future, but not right now. It can assist you in selecting the right candidates. However, this is what we need to talk about. When people say, I'm afraid that AI takes my job as long as you don't do the same thing, which can be automated very quickly. AI is not going to take any jobs anytime soon, at least not your job anytime soon, when you have a fully qualified job. The so called Assistant Intelligence, also abbreviated AI, which is, I think, why these two words make it into Press Assistant Intelligence or Assisting Intelligence, depending on how you want to have it.
The first thing that an AI can do very well, it can do excellent matching. So let's say you want to hire someone and you are, let's say London, London, uk. And you say, I'd like to hire someone. And at the moment, especially when I'm at the International Expo, I'm giving nine keynotes in three days here, some people say, yeah, quite funny German. And nine keynotes, nine awfully funny. Yeah, you know, Germans don't have humour anyway, that is a different topic here. So the first thing that I can do very well is speed.
So when you say, hey, we have 500 people applying for this job, because we are a reasonably large organisation, I cannot scan through 500 applications. The speed is the number one aspect where an AI can be brilliant. So you, for example, can say, we are London based company. We need people who have the right to work all over Europe because we're international company. And also we need to be able to actually check if they have licence abc, whatever licence ABC might be, they need to have licence ABC. That can be done. 500 CVs checked by an automated system driven by AI within seconds instead of days when humans do it.
And when you now say, hey, we have one of these applicants tracking systems and they already check for certain words and everything. Yeah, brilliant. Absolutely brilliant. So the speed that you can do, the speed that you can enhance, that is something where AI can really, really work well. And when you have data matching, then of course AI can help as well. So when you say this is the data we look for when people fulfil this, this profile, Brilliant. However, here's the first problem.
Bias comes from data which isn't great. So when you say the data we have in our AI is our former recruiting, most likely the human bias is already in there. And when you have human bias in AI, then putting an AI on it is just a turbo button. It's basically gasoline on a fire. It's basically making everything worse from there. When you have bias in a set of data and you put AI on it, that's the turbo button for making any kind of bias worse. And of course that means lawsuit incoming.
Sooner or later someone will sue you based on that because they can prove that. You exclude group XYZ from your, from your recruiting. And the data matching is something which is always as bad as it can be when the data is, let's say, not great. So when you say, and I just had one company yesterday telling me, oh yeah, we have our data in there, we have more than 500 applications this year and we took this data. So first, data protection laws in the European Union, on Europe, you have to stick to them. When you are in the European Union or working somewhere, they have a location in there. And then you can't work that way.
You cannot just take people's data without their consent. And in addition to that 500 application just is not enough.
Even 1000 wouldn't be enough. You need more data, you need the experience of that, of Decades, proper data, selected data, clean data, which is as close as possible to aspects such as. I don't want to use the word neutral, but as neutral as possible on the data side, especially when it comes to recruiting. And this data matching, when you have great data, perfect. However, when you don't have great data, you're making anything worse from there. What is really helping when we look at where do we get the best results? The best results we do never get when a human does something on their own.
We also don't get the best results when we make an AI do something on their own. When, for example, the AI does something and then the human take that result from the AI to their side and then optimise it from there.
You probably did that. You probably took ChatGPT or let's say open AI and they wrote an answer to an email which you received. And when you said, yeah, the answer is good, we just have to tweak it here and there so it looks more natural, looks a bit more like me. That is the best result, always working together. And that's the human collaboration. The human collaboration always means AI prepares something. AI prepares something and then the human takes it and optimise it also can be done vice versa, that you say, hey, this is my piece of writing.
Could you please, dear, I optimise the whole thing. And then the AI says, here it is optimised. And then you might take it from there again and say, okay, now I optimise it again and now it's really good. But the collaboration aspect is the best outcome that you can ever have. And of course, now some people say, look, I'm a managing director. And as a managing director, I hold a lot of risk in my hand. I have liability, my name stands next to this.
So when I any kind of AI, I need to be sure how to do this from a leadership point of view. So what should I do? And that's exactly what we're going to talk about right now, the leadership aspect of AI. And of course, number one, and I know that people do not like when I talk about this, but we have to regulatory and compliance and of course, ethics. I give you a very simple aspect of that when you want to say you have. Let's take the case of, for example, an Indian airline. An Indian airline which recently during an expo, said when they have open positions for flight attendants, they receive 40,000 applications within 24 hours in a country of the size of India.
Not too surprising. However, you cannot manually, at reasonable costs, go through 40,000 applications and when you then want to say, I want to check, is their English good enough?
Are they fluent? Can they hold a conversation? That is something an AI can do. Rhetorio from Germany, for example, offers a product for that, that you can have an AI based conversation. Because simply saying, do you have the language certificate? That is of course an indication. But it doesn't mean that they can apply the language, it only means they applied it to the test.
And maybe they just were good at testing or they had very good exam preparation material. An AI can hold a conversation and then this simply means that you can really test. Are there skills up to the level where they should be? And that is something where many people suddenly say, oh yeah, this is way quicker done with an AI. Exactly. And by the way, no one has an issue with that, talking with an AI, when you say it's only about checking the skills. But let's say you have on your website, oh, our team members are the most important asset and the greatest people that we have are the ones that work for us and we are all in for the human being. And suddenly people apply for a job with you and they only speak to AI from the beginning to the very end, Only the very last step maybe is done by a human being.
That is an ethical question and there is no right, no wrong. It is simply a question you have to answer. Is that something that you want to have out there? Because it immediately will be in connection with your employer brand. People will say on your website it says you're a human focused organisation and during the application process everything is just automated, not even in a good way. They asked me questions which I already mentioned in my cv, so why did they ask me again? And the AI didn't really get the gist of what I sent them, they just answered randomly when I sent emails.
It's all not very good. It looks very formal, looks very stiff, looks very fake, looks very standardised, doesn't really tailor to what I really asked. So the employer brand is out there and this can have massive, this may receive massive promotions or massive damages if you do it well or not so well. And by the way, compliance of course is not only regulated by laws which are obviously out there, it's also dominated by internal regulations that you have. So for example, when you have a, have a, have a rule where, where it simply says in compliance. Let's just give you one rule that quite a number of companies set up where they say standardised testing for tasks, for simple skill checks can be done by AI, but anything soft skill Related needs to be asked by a human being and evaluated by a human being. When you then say no, not for me, then the organisation will say, look, the compliance internally is also legally binding for you, so please stick to it.
So ethics compliance in your employer brand needs to be considered from the very beginning. If you do not consider that, you will be one of these companies which suffer due to public outrage. However, when you now say, well, I saw Amazon had issues and Microsoft had and now workday has and they are all doing pretty well from here, well, when you are workday, Amazon or whichever else international corporation with billions of dollars on your bank account and the willingness to spend millions of dollars on lawsuits, go for it. However, when you are not that business and you're maybe a mid sized company where people say, not really too great to work for you, thank you very much, no thank you, and your clients run away and your whole customer base is gone within a week, then you might not survive this. So very important is ethics compliance. Employer brand needs to be on board. When you now say it's all about future skills and the human element of it that you say, people need to be aware of AI.
People need to use AI. We want to be one step further than others. We are always a bit more adventurous, we are a bit more daring, we are willing to take risks. So we are not risk averse. We are able to take risk. Then of course you can say let's go for it. However, it means you need to train people properly and the amount of companies I see that at the moment say, yeah, we have this European Union guideline to teach our people AI and train them on it.
So where do I get the 19 pound 19 online class to just tick the box and then I can pretend they all know AI because we trained them. And also when you say that your company has something like human focus, appreciation, you appreciate people's work, you are thankful for their work, you're rewarding, etc. Etc. The $19 online class is not going to make the cut. There's nothing bad with online training, not at all. I even offer it. However, it is something which adds on to other means of communication and also of training and qualification.
It is not the only solution. You have future skills and human elements always means humans need to be able to use AI. However, they also need to be able to evaluate the results that AI gives them. So when you for example say use AI and they do something wrong and you say how could you, they will say I used AI, you told me so. I didn't know how to question an AI on the results. I thought they are always right. No one told me.
And there you are again. The best result is always delivered when you say the human being is assisted by an AI and together these two entities achieve the best. And when you do it the way that we discussed right here today, then you will have massive benefits in your organisation and I wish you all the best implementing that in your organisation. And when you now say I think I have about five to 48 questions now, very very happy to answer all of them. Lots of questions came to me during my talks and speeches during the present. I gave in in in Cologne this this week and the upcoming ones there. There are more more of these coming tomorrow.
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