Data has the power to drive important decisions and make a big impact. But with this powerful resource comes some challenges. How can organizations collect, store, and use data in an ethical way? What rights should they protect? These questions are explored in the field of data ethics, which provides five guiding principles for business professionals who work with data.
What is Data Ethics?
Data ethics is all about the moral responsibilities of gathering, protecting, and using personally identifiable information and how it affects individuals.
In simple terms, it asks, “Is this the right thing to do?” and “Can we do better?” As Harvard Professor Dustin Tingley explains, it’s about making sure we handle data in a responsible and respectful manner.
Data ethics is crucial for analysts, data scientists, and IT professionals. But even if we’re not directly involved in handling data, it’s important to understand its basic principles.
For example, our company may collect and store data about customers’ interactions with our website, from the first time they submit their email address to their fifth purchase. If we’re a digital marketer, we probably work with this data regularly.
While we might not be responsible for the technical aspects of data management, knowing about data ethics can help we spot any unethical practices in data collection, storage, or use. By doing so, we can protect our customers’ privacy and keep our organization out of legal troubles.
5 Data Ethics Principles for Business Professionals
1. Ownership
The first principle is about respecting people’s ownership of their personal information. Just like taking someone’s belongings without permission is wrong, collecting personal data without consent is unethical and illegal. Always ask for permission through agreements or privacy policies to avoid ethical and legal issues.
2. Transparency
Data subjects have the right to know how we collect, store, and use their information. Be transparent when gathering data and explain our methods clearly. Let users decide if they want to accept cookies or not, and avoid deceiving them about our intentions.
3. Privacy
Handling data comes with the responsibility of protecting individuals’ privacy. Even with consent, it doesn’t mean their data should be publicly available. Safeguard sensitive information by using secure databases, password protection, and encryption. De-identify datasets when possible to maintain anonymity.
4. Intention
Intentions matter in ethics. Before collecting data, ask ourself why we need it and how it will be used. Avoid collecting unnecessary sensitive data. Strive to collect the minimum amount needed to achieve our goals, making a positive impact while respecting our subjects’ privacy.
5. Outcomes
Even with good intentions, data analysis can inadvertently harm individuals or groups, leading to disparate impacts. Disparate impacts can be unintentional biases in algorithms that disproportionately affect certain groups. Consider the potential impacts of our analysis and aim to minimize harm.
By following these data ethics principles, we can conduct responsible and ethical data analysis, respecting people’s privacy and rights while making informed decisions.
Ethical use of Algorithms
If we work with machine-learning algorithms, it’s important to consider how they might follow the five key data ethics principles.
Algorithms are created by humans, so they can unintentionally have biases. Biased algorithms can harm people. Here are some ways bias can affect our algorithms:
1. Training
If our algorithm is trained on data that doesn’t represent the whole picture, it might favor certain outcomes over others.
2. Code
Although bias in algorithms is usually unintentional, it’s essential to be aware that it could be purposely written to produce biased results.
3. Feedback
Algorithms learn from users’ feedback. If biased feedback is given, the algorithm can become biased itself. For example, a job search platform may recommend roles based on previous choices made by hiring managers. If they consistently choose white male candidates, the algorithm will learn from that and only suggest jobs to similar candidates in the future.
It’s essential to strive for the best even though no algorithm is perfect. Human evaluators, representative training data, and involving diverse stakeholders can help create better algorithms for a better future.
Using Data for Good
While using data is important, it’s crucial to prioritize the safety and rights of those involved. Ethically handling data allows we to make informed decisions and drive positive change in our organization and the world.