In the Chaos, We’re Forgetting the Ethics of AI Technology
Not only are generative AI models hallucinating, but increasingly, organizations seem to be rushing headlong into implementing technology. Everything needs to be done as quickly as possible—preferably yesterday—to achieve results fast. But in all this chaos, we seem to be neglecting ethics. That’s why in this article, I’ll highlight some key considerations about ethical practices. Ethics involves reflecting on your actions and determining whether they’re the right thing to do in a given situation. It’s about principles that dictate how we should behave—not just for ourselves but also toward others. When it comes to ethics in technology, it’s about how AI impacts our lives and ensuring it does so positively.
Growing Challenges
We need to critically examine how we create and use technology, ensuring it benefits humanity without causing unnecessary harm or inequality. However, challenges at the intersection of ethics and technology are becoming increasingly apparent.
- Lack of transparency: Companies often obscure how they collect and use data. For example, Uber was fined €10 million in the Netherlands for failing to clarify how it gathered and processed driver data.
- Privacy concerns: Technologies like facial recognition and tracking apps frequently intrude on personal privacy without explicit consent. In the UK, the use of facial recognition by police has sparked significant criticism over constant surveillance.
- Bias and discrimination: The rise of generative AI has reignited debates about bias. For instance, Amazon discontinued its AI recruitment tool after discovering it discriminated against female candidates.
- Lagging regulation: Technologies often outpace legislation, creating gaps in ethical oversight. In the Netherlands, debates on regulating drones and autonomous vehicles highlight this disparity.
- Accountability: Who is responsible for managing and protecting data? Who takes the blame for a chatbot’s incorrect answers or accidents involving self-driving cars?
Businesses Falling Short
Ethical lapses are often associated with major tech companies, but even SMEs are increasingly struggling to navigate AI responsibly. Over the years, I’ve worked with organizations of all sizes to integrate digital transformations. While enthusiasm for new technologies like NFTs, AI, the metaverse, and AR is high, the rush to adopt these innovations often leads to oversight of potential side effects.
The rapid pace of technological development can exacerbate this issue. For example, it took seven years to establish regulations for the sharing economy and over a decade to create cryptocurrency laws. However, these regulations often fail to address newer developments like tokenization, DAOs, and DeFi.
Using Your Moral Compass
Organizations can choose to operate in regulatory “gray areas” until laws catch up, but I prefer to rely on a moral compass. When making decisions about new technologies, I consider whether they are morally justifiable, even in the absence of clear rules.
Practical Tips for Ethical AI Use
1. Rethink Data Practices
While GDPR has improved awareness about data use, many companies still collect more data than necessary, increasing the risk of privacy violations.
What you can do:
- Implement a “Data Minimization Protocol.”
- Regularly evaluate data collection activities and ensure they align with core business needs.
- Remove redundant data to minimize risks.
2. Prioritize Transparency
Many businesses are hesitant to disclose their use of AI for fear of negative reactions. However, a lack of openness can erode trust when these practices inevitably come to light.
What you can do:
- Develop an “AI Transparency Strategy.”
- Clearly communicate how and where AI is used within your organization. For example: “We use AI to categorize emails so we can respond faster.”
3. Address Bias
AI often inherits biases from its training data, which can lead to discriminatory outcomes. For instance, Google’s Gemini faced backlash for producing historically inaccurate representations.
What you can do:
- Experiment with tools like IBM’s AI Fairness 360 to identify biases.
- Set clear thresholds for acceptable bias levels in your systems.
4. Avoid Blind Trust
Automation bias—the tendency to trust AI outputs uncritically—can lead to flawed decisions. With AI tools sometimes “hallucinating,” human oversight remains essential.
What you can do:
- Identify areas where human evaluation is critical and establish structured review processes.
- Log and address inconsistencies or errors in AI outputs.
5. Create Clear Guidelines
Many organizations provide only vague instructions about AI usage. Employees often need concrete, actionable rules.
What you can do:
- Develop a concise “Ethical AI Code” with clear guidelines tailored to your organization’s needs.
- Pair this code with a checklist to ensure thoughtful, consistent decision-making when using AI tools.
6. Appoint Chief Ethical Officers
Ethical considerations should be a continuous priority. Drawing inspiration from financial institutions, organizations could benefit from dedicated roles like Chief Ethical Officers to oversee AI implementation.
What you can do:
- Establish an ethics committee or working group to evaluate AI practices regularly.
- Test tools for biases, adherence to internal guidelines, and alignment with moral principles.
Responsible Innovation
Ethical AI use isn’t a luxury; it’s a necessity. Businesses have a responsibility not only to innovate but to do so in a way that’s responsible and sustainable. Every decision made in designing, implementing, and using AI systems can have far-reaching consequences for customers and society.
As Isaac Asimov once said: “The tragedy of the world is that the intelligent are full of doubt, while the foolish are full of confidence.” Let’s strive for a balance of intelligence and responsibility in our approach to AI.
Don’t wait until tomorrow to embed ethics into your AI strategy. Start today by implementing these practical tips. Every small step brings us closer to a future where AI is not just powerful, but fair and trustworthy.
Jan Scheele werkt dertien jaar op het snijvlak van deep tech, strategie en leiderschap. Als keynote spreker en dagvoorzitter maakt hij technologie tastbaar voor boardrooms, directieteams en grote podia, zonder de complexiteit te versimpelen of te verbergen achter buzzwords.
Zijn achtergrond ligt in het bouwen. Als CEO van een technologie scale-up, oprichter van meerdere techbedrijven en organisator van meer dan vijftig TED-events wereldwijd zag hij van dichtbij hoe technologische keuzes doorwerken in strategie, governance en cultuur. Vanuit zijn betrokkenheid bij het World Economic Forum en de BCNL Foundation kijkt hij daarbij niet alleen naar wat technisch mogelijk is, maar ook naar wat bestuurlijk houdbaar en maatschappelijk wenselijk is.
Hij publiceerde vijf boeken, waarvan twee Amazon-bestsellers, en schrijft wekelijks over AI, blockchain en de organisatorische gevolgen van deep tech. Zijn blogs bereikten inmiddels meer dan twee miljoen lezers.
