Quite recently, colleges and universities that offer courses on data science have made an addition of a module called ethics education in the curriculum. When it comes to technical subjects like Data Science, ethics education has an important role to play and ensure cent percent transparency in the data published by data analysts.
So what is included under Ethics Education under the modules of Data Science?
With it comes to ethics education, some of the topics that professors cover are deontology, consequentialism, utilitarianism, virtue ethics, moral responsibility, cultural relativism, social contract, feminist ethics, justice consequentialism, the distinction between ethics and law, and the relationship between principles, standards, and rules.
Based on the above frameworks, professors make it a point to cover different facets like:
- data privacy
- algorithmic bias
- misinformation and faulty processes
- protection of intellectual property
- surveillance and inequality in data
- data collection
- AI governance
- Transparency
- Security and anonymity of data source
- systemic risk
- codes of ethics
- predictive policing
- ethics in industry
WHY IS ETHICS EDUCATION SO IMPORTANT IN DATA SCIENCE?
How many times have you believed in the information that is backed by enough stats and figures? I know that I have! But what if I told you that half the times the data we use are inaccurate. This is what you call the misuse of data analytics. With or without our knowledge, many important decisions of our lives depend on the facts and figures that we come across.
This is precisely what makes it imperative that data analysts should maintain the code of ethics while creating effective models based on the analysis at Myassignmenthelp. The consequences of taking decisions using wrong and published data analysis can be quite detrimental to businesses and individuals.
At the same time, the financial data of many industries are usually meant to be kept as a trade secret and is hidden from view of the public. Ethical education is essential so that data scientists understand that it is their responsibility to keep the figures undercover and never reveal them intentionally or through ignorance.
Another reason why ethics education is important is that it teaches budding analysts how to avoid faulty processes. I’ll give you an example of how flawed data can have an adverse effect on decision making. Suppose, a survey about the use of a product is limited to only women between the age of 30 – 50, but it is published as generalized data for women of all age groups.
Now, this may seem like a trivial issue, but it can affect decision making severely. Such data models are potent drivers of preferences and innovation, and thus it becomes extremely crucial for data scientists to maintain ethics.
Mathematician Cathy O’Neil works for Assignmenthelp who says that universities all around the world are all set to take an active role in providing data scientists with ethics education so that they are better equipped to fight against the ethical dilemmas in their field. With ethics education introduced into the curriculum, we can expect a remarkable decrease in statistically flawed algorithms being published for the public.
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So how are colleges and universities preparing to incorporate Ethics Education into the curriculum of Data Science?
Universities that provide courses on Data Science have started to show considerable interest in providing students with ethical education. But since the entire process is in its nascent stages, it has become quite a challenge for professors to teach ethics and a debate has been hatched within the educational board as to how this particular branch of the curriculum will be imparted.
Ethics isn’t something that can be learned from textbooks or through practice on paper. When it comes to educating students on ethical grounds, the best option is to throw questions at them and see how they solve it.
“Rather than studying from books and answering textual questions, making them face philosophical challenges should be a better option,” says Barry Goldman, data analyst of All Essay Writer and a fellow Data Science Professor.
Another great way to train students with sound ethics would be a social-good project through which the students can engage in live sessions where they would face ethical questions and would have to work as a community to build principled and human-centered insights.
It is only through practical application that students pursuing data science can learn ethics regarding data privacy and data security directly. The data analysts should be put to think critically and ethically while being aware of how can correct data drive decision making is.
Now the debate has come down to the topic of the best and the most effective forms of assignments that can provide appropriate study help to engage students with the proper set of ethics that they should mention.
So, if you are wondering how to teach ethics to your data science students, then you can use these following effective assignment methods in your next class:
Criticism of Existing Policies
Since ethics education is more of a practical subject, you can give your students a company’s data policy and tell them to provide you with a critical analysis of it. It does not always have to be a company. You can provide them with the collection of data of the University that they are studying under, or you can even ask them to take an organization’s code of ethics and give critical feedback.
This will help the students to have a better understanding of the minutest details of the policy that they are studying, and they will realize how it affects the decision making of people. In the assignment, you can ask students to suggest changes to the data algorithms of the company or university policy that they have studied.
Debates
A debate is an excellent option through which you can engage your students and teach them data science ethics through the application. You can split your classroom into two or more groups, and you can give them a different company’s data. You can give them time to analyze the data of the company and then take a stance – either for or against it – and argue with the other groups.
You have no idea how much can each student actually learn through a debate. As this will give the students a chance to justify themselves, this will make each student learn something new, and at the same time, they will learn how to act or react in a given situation.
Adversarial Mindset
One of the best ways to teach ethics to students is by making them develop an adversarial mindset. You can give your students assignments for which they have to design a fake news campaign for a particular brand. While creating the same, they will realize how data scientists misuse data and publish faulty algorithms to manipulate users.
This way they will get a better understanding of how malicious users think, and they will learn how to counter such attacks practically. But a word of warning – while framing such assignments, make sure they are educative enough so that within all excitement, students can actually learn ethics education and the applications properly.
Peer Audit
You can ask your class to take up a case study and review its ethics. You can give them the topic, or they might have the prerogative to choose too. If this does not seem engaging enough, then you can ask your students to review each other’s work. You can also link technical assignments along with peer audits where you can ask your students to assess and evaluate the data presentation method adopted by another student and pose questions against the methods.
If you are looking for some recent case studies to give as assignments to your students, then here is a list of them.
- Cambridge Analytica’s use of Facebook data,
- The fatal crash of a self-driving Uber car
- Facebook’s emotional contagion study
- Chinese criminal facial tracking
- Uber’s tracking of one night stands
- NYPD Stop and Frisk Data
Although ethics education is a new discipline in Data Science, sincere efforts are being made by universities and colleges to ensure that students are adequately trained in ethical standards. This is imperative in helping them learn how to apply data ethically and accurately. Since private companies impact our lives on a daily basis, it is vital that the data analysts of the future learn how to maintain ethics so that the algorithms they determine are real and beneficial to users.
Since ethics is a dynamic subject, textbooks cannot be of much use. It is only through technical training and real-world examples that students can be made aware of the rapidly changing ethics. So I would suggest that you use other methods like the above to impart data science ethics education. You can even share blog posts and other research papers with your students.
Author Bio: Nathan William is an engineer by profession and an assignment proofreading blogger by calling, who is associated with MyAssignmenthelp.com, and extends help to students in writing their CDR Australia. Besides being a gizmo freak, he loves to keep herself busy with community work.
