Responsible data science is a consortium where leading Dutch research groups across disciplines join forces to address urgent and challenging problems. These issues are bound to lead to a big data winter if not addressed. That means misusing data and mistrusting data science results.
The responsible data science consortium provides experts with ideas to realize scientific breakthroughs that will lead to using data positively.
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The responsible data science consortium was formed to enable the positive use of data, regardless of type, including big data.
What Challenges Does Responsible Data Science Address?
The responsible data science consortium hopes to address four challenges, namely:
- Data science without prejudice: Responsible data science aims to avoid unfair conclusions even if they are true. Many believe that “human beings are born subjective and tend to become more and more prejudiced in life unless they become cognizant of subjective and objective context to every situation, and assumptions underneath.” As such, a prejudiced person differs from someone who is aware that he/she is. In line with this train of thought, responsible data science hopes to eliminate the prejudices that could stem from the biases a data scientist holds.
- Data science without guesswork: Responsible data science hopes to answer questions with a guaranteed level of accuracy. That means not injecting suppositions and opinions into results. All research that stems from data science should be accurate. They should only rely on the data alone, and that data should be sound and obtained from reliable sources.
- Data science that ensures confidentiality: Responsible data science works toward answering questions without revealing secrets. While data scientists should always be factual, they should respect the intellectual property and privacy rights of the owners of the information they’re processing. That is especially true for data scientists working with proprietary and sensitive data, including company secrets (e.g., a recipe, formula, etc.).
- Data science that provides transparency: Responsible data science aims to clarify answers so they will become indisputable. Despite protecting the privacy and intellectual property of data owners, data scientists should be transparent about their methodology and analysis. That means even if they can’t reveal their sources, for instance, they should still be transparent about their algorithms, especially how they arrived at their conclusions. This goal is closely tied to avoiding guesswork.
What Areas Does Responsible Data Science Wish to Address?
The responsible data science consortium envisions to develop methods to make responsible science, responsible health, responsible business, and responsible government a reality.
The National Academy of Sciences defines responsible science as “a provocative examination of the role of educational efforts; research guidelines; and the contributions of individual scientists, mentors, and institutional officials in encouraging responsible research practices.”
Responsible data science, like responsible science, should ensure the integrity of the research practice.
Responsible health is defined as healthcare that does not harm the environment and living things. That entails all pharmaceutical and other health-related companies remain not just environmentally friendly but also respective of both human and animal rights. No harm should come to anything and anyone in the pursuit of healthcare development and provision.
A responsible business benefits society and addresses the adverse effects that it may have on society, people, and the planet. Decision-makers should thus make more responsible decisions, considering social and environmental impacts are balanced against financial gain. In short, being responsible means being sustainable.
A responsible government answers to its citizens. It doesn’t serve the people in power or a monarchy but relies on democracy. As such, it requires a majority parliamentary vote to pass legislation. It is employed by countries, including the U.K., Canada, and Australia.
What Does the Responsible Data Science Consortium Plan to Do to Make Their Vision Come True?
To turn responsible data science into a reality, the consortium will:
- Enable and ensure responsible data usage without inhibiting the power of data science
- Provide the technology that will protect data science’s fairness, accuracy, confidentiality, and transparency
- Provide a platform for the top Dutch researchers in data/process mining, digital humanities, ethics, information retrieval, knowledge representation, law, machine learning (ML), natural language processing (NLP), security, statistics, and visualization to come together
- Develop a nationwide multidisciplinary platform that focuses on data science- and big data-related challenges
The people behind responsible data science hope to produce new data science concepts, novel analysis and data management techniques, powerful software tools and infrastructures, and education and training. They aim to provide the means to use data in a truly responsible manner.