AI is offering us a huge selection of new tools to innovate and grow. Along with these opportunities come new ethical challenges that demand our attention. As AI becomes a bigger part of the workplace, we must prioritize responsible use.
Understanding AI’s Potential
AI can do amazing things. It can create content, summarize information, automate tasks, even adapt to your or the whole business’ workflows and augment them. These capabilities save time and open the door to new ideas in massive scale. However, using the tools without careful consideration can lead to unintended harm.
The way we handle AI now will shape its future impact. It’s not just about what AI can do but how it aligns with human values and societal needs.
Ethical AI is not just about avoiding harm. It’s about actively creating systems that align with societal values.
I fully recommend checking out the Ethics of AI course created by the University of Helsinki. The course suggested five basic principles for ethical AI use:
- non-maleficence
- responsibility or accountability
- transparency and explainability
- justice and fairness
- respect for various human rights, such as privacy and security
The course was created already in 2019, but the basic principles still hold up great as of the end of 2024.
Non-Maleficence: Do No Harm
Generative AI systems can unintentionally cause harm, from spreading misinformation to perpetuating biases. Proactive steps must be taken to minimize these risks.
For example:
- Use human-centric approach to map opportunities
- POC and test intensively to identify and mitigate harmful outputs
- Avoid deploying AI in applications that may endanger lives, like unverified healthcare diagnostics
- Avoid deploying AI in applications that may spread misinformation
- Regularly monitor systems to detect and address unintended consequences
By prioritizing safety, we can ensure AI systems contribute positively to society.
Responsibility and Accountability
Who is responsible when an AI system fails or causes harm? Accountability in AI is a pressing issue that demands clear answers. Ownership of the actions and outcomes of their AI systems must be taken.
Key actions include:
- Establishing clear accountability frameworks for AI deployment
- Ensuring enough training material and examples for AI apps
- Ensuring human oversight for critical decisions
- Creating audit trails to trace AI decision-making processes
Responsibility also extends to preparing employees for the ethical use of AI through training and clear guidelines, and providing a safe playground for testing and learning purposes.
Teamit’s human-centric design methods ensure transparent and efficient way of finding the right use cases.
Transparency and Explainability
Generative AI often operates as a “black box,” producing outputs without clear reasoning. This lack of transparency can erode trust and lead to misuse. It’s important to provide the users with an understanding what data is being used for decisions, how their data is being used and so on.
Also we’ve seen many examples of how users have started hacking a service not tested enough.
To build trust, we have to:
- Disclose when and how AI is being used in products or services
- Provide explanations for AI-generated decisions in clear, user-friendly terms
- Develop interfaces that allow users to understand and challenge AI outputs when needed
- Possibility to give feedback, opt out, and delete data
Transparency ensures that AI remains a tool, not an enigma.
Justice and Fairness
AI systems learn from data, and if that data is biased, the AI can perpetuate systemic inequalities. Ensuring justice and fairness requires deliberate action to identify and address these biases.
Businesses should:
- Regularly audit AI systems to detect and correct biased outputs
- Diversify training datasets to reflect varied user groups
- Incorporate diverse voices into AI design and oversight teams
Especially diversifying the data, design and training is important when building accessible applications which are helpful for all.
Fair AI ensures that no group is unfairly disadvantaged or excluded by its use. This research (among others) suggests elderly people, people belonging to language and cultural minorities, people with disabilities or other circumstances reducing their capability to use digital services, people with lower socioeconomic status and people who are not digitally literate, are often excluded from the design process.
Respect for Human Rights
Generative AI’s reliance on data raises critical concerns about privacy and security. Upholding human rights means respecting individuals’ data and ensuring it is used responsibly.
Key practices include:
- Obtaining informed consent for data collection and usage
- Implementing robust anonymization and encryption techniques
- Complying with global privacy standards like GDPR and CCPA
Moreover, businesses must consider how their AI systems impact broader human rights, such as freedom of expression and equitable access to technology.
Work Positive
By adhering to these principles, we can create a foundation for ethical AI that benefits society while minimizing risks. Practical steps to achieve this include:
- Establishing AI ethics teams to oversee implementation
- Developing internal policies that align with these principles
- Engaging with external experts and stakeholders to continuously refine ethical practices
- Sharing your experiences to help everybody
Ethical AI is not just about avoiding harm. It’s about actively creating systems that align with societal values. By embracing the principles of non-maleficence, accountability, transparency, justice, and respect for human rights, we can lead the way in building trust and fostering innovation.
The future of AI depends on the choices we make today. By putting ethics at the forefront, we can ensure that this transformative technology serves humanity’s best interests.
