Data & Data Ethics
Data is the new oil.
Data is one of the most important assets for any organisation. Using data can enable us to innovate, create more efficient and effective services and products, and fuel economic growth and productivity.
What is data?
The word data is derived from the Latin meaning “given.” Rob Kitchin, a social scientist in Ireland and the author of The Data Revolution (2014), has argued that instead of considering data as given, it would be more appropriate to think of it as taken, for which the Latin would be CAPTA.
Data is facts or things that make the basis of reasoning, arguments, decision-making, or evidence.
- Data is never raw
- Data is never neutral
- Data has context
Why do we need data ethics?
We live in a world of big data, massive data streams being created, collected, combined and shared.
The traditional governance frameworks and risk-mitigation strategies are insufficient.
What is Data ethics?
Data ethics relates to good practice around how data is collected, used and shared. It is trustworthy and ethical, especially when data activities can potentially impact people and society, directly or indirectly.
To understand the ethical questions that can arise from data analysis, we will look into a piece of recent news. John Hancock, one of the largest life insurance companies in the United States, has added fitness tracking with wearable devices to its policies. By return, consumers can benefit from discounts and cheaper premiums if they are willing to share their Fitbit data. The company then changed its policy recently to switch from voluntary to mandatory enrolment.
We are going to look at privacy, transparent and consent.
Privacy
Privacy is a topic on ethical issues surrounding big data. The analysis of extensive datasets may include personal data, and the more personal information included in the analytics, the more it might interfere with the privacy of the individuals concerned.
Data privacy is about access, use, and data collection and the data subject’s legal right to the data. This refers to freedom from unauthorised access to private data, inappropriate use of data, ownership.
No doubt, the widespread adoption of personal fitness tracking devices raises potential opportunities for innovation to improve business decisions. However as individuals, we tend to react strongly and emotionally about how our data is used. Everyone has the right to protect personal data concerning themselves and the right to have it rectified.
The consumers have no idea how the insurance company handles their data, e.g.
- How their data is collected?
- What kind of their personal data will be used?
- Will the data be used appropriately?
- Will their data be sold to the third party?
Transparency
Transparency comes down to can we identify what sources of information are being used? Have we established the right properties, the right principles in place when we train the systems to use data that is representative of who we are, and the information that we are using?”
Some customers will be happy for their data to be used if they receive specific benefits in return, but others will not. Transparency is, therefore, essential to ensuring that each customer understands their choices about the use of their data. Customer will be more likely to share their fitness data if they have control.
Today, transparency is a core principle personal data must be processed lawfully, fairly and in a transparent manner, illustrating the close connection between transparency, lawfulness, and fairness.
Consent
The concept of consent refers to data must be processed fairly for specified purposes and lawful. This means that any personal data processing should be based on individuals’ consent. Individual also have the right to withdraw their consent.
Collecting individuals’ consent does not mean organisations obtain free consent to process the data as they wish. They are still accountable and have to meet privacy standards.
It is also worth noting that if individuals have given their consent for a particular personal data processing activity, they also have the right to withdraw their consent.
The insurance company should inform participants about the purpose of the data collection, what will happen to their data contribution — including archiving and data sharing, and outline the right to withdraw from the program, for example, by stating that the data will only be used by the insurance company and the data won’t be shared or sold to the third party, participants have the right to withdraw.
Consent should be obtained before the intervention, and that the intervention should be based on a sound understanding of its implications and possible consequences. However, it is questionable how much this is relevant to or practicable in the modern data world. The data typically already exists in databases so the research or program are mostly non-interventional.
In general, to be meaningful, informed consent to data use requires an understanding of what the data might be used for in the future and of how the data are to be used.
Consumers are concerned about how insurance company will access, use, and store their fitness data. Therefore giving customers control, e .g.
- What part of their device data they want to share?
- Who they want to share their data with?
- How they can withdraw from the program?
In that way, people could be more willing to participate in fitness tracker-based insurance policies.
Conclusion
Ethics should be one of the core components of the business’s decision-making process, right alongside legal, contractual and business concerns. Implementing ethical standards around data handling will win the customer’s trust and make the business stand out from its competitors.
On the other hand, if data is misused or in a non-compliant way, trust can very quickly be eroded, resulting in reputational damage and financial loss. Regulation only goes so far in keeping the balance right. Data ethics is the other key player.
We therefore need to ensure that data is collected and used trustworthy and ethical.
Reference
https://www.thenewatlantis.com/publications/why-data-is-never-raw
https://www.bbc.com/news/technology-55998588
