How many times have you heard that data is the new oil, or perhaps the new gold of the 21st century? If that is the case, then using data ethically should be the gold standard. When it comes to ethics, it can be a daunting task to strike the balance between regulations, benefits for your business and not losing sight of your moral compass.
Data science with an ethical edge
To get the most out of your data, all businesses should be aware of some data usage fundamentals. The next step is spotting potential ethical and legal vulnerabilities. Once trained in identifying these liabilities, you can apply ethical guidance to eliminate or reduce ethical risks. Based on a meta-analysis from 39 unique authors of guiding principles, we identified that the commonly accepted ethical guidelines revolve around: Safety, Fairness, Transparency and Privacy.
IG&H Ethical Risk Quick Scan
We then distilled these guidelines into an Ethical Risk Quick Scan that highlights areas in need of particular attention and can help you assess the risks that may arise from a particular use case. The scan consists of a series of questions concerning things such as who will be impacted by the decisions made, how will they be impacted, and to what extent?
For example: will you be using the data to make decisions that may have an impact on the financial well-being of a population that is considered vulnerable? If so, how might their personal behavior be impacted? And how long will it take before you can measure whether the algorithm may be causing an undesirable effect?
IG&H Ethical Guidance framework.
Compared to the Quick Scan, the Guidance Framework is best used continuously throughout the project life cycle, while more characteristics of the model and implementation become known. This framework provides evaluation dimensions, including more detailed check questions and possible solutions.
For instance, you may ask yourself: Does the model change how people are treated, and is this intentional? Can (and do) we explain to the user why a certain outcome is reached? Is learning autonomous? Has all data involved been secure at all moments, and will it be in the future? To arrive at an answer, you first need to ask the right questions.
Are you ready to update and upgrade your approach to data ethics? Discover how these tools are applied in a fictional ethical use case about a taxi company. Read our whitepaper to brush up on your theory and see data ethics made practical.