In simple words, data science ethics allows us to learn about all the ethical problems that appear during our use of data. Data collection is a vital part of nearly every aspect of our lives, from the phones in our pockets to the cars we drive. Almost every human behaviour and operation we do with a tool like a computer, can be collected as data. Advanced technologies related to data science like machine learning and artificial intelligence, have brought a lot of benefits to our lives. 

However, as humans begin to step away from hands-on analysis and let the automated machines do most of the tasks for us, different issues such as fairness, privacy, and representation emerge. There are several programs running across the world that stresses upon producing graduates who not only have deep technical expertise, but who also know how to responsibly collect and manage data and use it to take informed decisions and advance innovation to benefit the rapidly evolving world they’re graduating into. This makes it imperative for every data science professional to have in-depth understanding of data ethics. As data scientists often deal with big sets of data-driven technologies that also calls for an understanding of the underlying human and social structures.

A future-oriented, privacy-protecting data science practice comes down to how data professionals behave. With the adoption of GDPR, there’s a rising awareness for the need to protect and responsibly deal with personal information amongst both corporations and consumers. The Oxford-Munich code is an initiative to define a code of conduct for professionals working within corporate data science teams. They aim at developing a set of practices, covering a wide range of issues, that is designed to avoid dangerous consequences and to protect data scientists, companies employing them and the data owners. A code of conduct must provide practical guidelines for managing professional data scientists as well as clear standards against which RandD and operational groups’ practices and activities may be audited. 

Companies would benefit from the suggested Code in several ways:

• By having their data professionals aware of and adhering to standardized set of practices
• By reducing the risk of any adverse data related event as well as mitigating the potential impact
• By improving brand image and reputation as proven compliance fosters greater consumer trust

This brings us to the question;

WHY DATA SCIENCE ETHICS ARE IMPORTANT?

One of the biggest mistakes a data scientist could make is to assume that legal compliance with privacy and data protection laws mitigates all risks associated with the use of personal data. Simply maximizing the power of a predictive model using all data does not necessarily lead to optimal business outcomes in the long run. the foremost thing to appreciate is that legal does not equal ethical. It’s true that the law seeks to define behaviours which society deems to be right or wrong (ethical or unethical). Dealing with customers in an ethical way is worthwhile as in the long term. It improves the bottom line. Negative public sentiment about how you are using data can devalue a brand immensely. For instance, if it comes to light that your model puts women, ethnic minorities and the poor at the back of the queue for medical treatment, then be ready to be challenged on that. Alternately, the ethical considerations should drive a number of constraints within decision making systems, which need to be given appropriate consideration when the system is designed.

Secondly, nature of ethics is very subjective, very much a personal thing. Two people may hold very different, yet equally valid opinions as to what constitutes acceptable behaviour. Personal data is there to be harvested and used to maximise organizational goals. If there’s a problem using a specific data or an unacceptable bias is present against a specific group, then legislation is enacted to address that particular concern.

Every business manager and marketing pro understands the importance and power of understanding what drives their customers and prospects, seeing big data as the key that opens the treasure chest. They’re faced with ethical issues on a daily basis. When business interests prevail over customer needs, all the big data manipulation in the world won’t sway those valued customers in rolling out more funds or private information. 

People recognize that businesses need to turn profits, which occur through public interactions. Customers want the business they invest in to make profits and vice-verse customer is valued as they deliver profits to the company. Since, every consumer wants to be appreciated by the businesses they patronize, this creates an ideal win-win scenario in which both businesses and their patrons thrive. 

Here are the several ways that you can incorporate data ethics into your business:

1. Inform and consent 
2. Privacy and protection
3. Two-way transparency
4. Respect the rules
5. Privacy by design
6. Algorithm evaluation and auditing

Critical of all is the integration of the concept of data ethics within the minds of business owners and executives. Once viewed as the ideal method for obtaining and maximizing the big data handling benefits, your team will be on board with the concept. And that’s absolutely the first important step to satisfying the growing demand for data ethics.

Towards the close, having talked in-depth about the data ethics and their relevance in today’s business world, it becomes imperative to spare some time for investing in a credible data science certification, that actually assists a data science professional to further the nuances of data ethics, thereby flourishing with the company growth. There are numerous data science certifications to choose from in today’s times like- USDSI, MIT, Coursera, Stanford, Google, etc, as most of them are online and can be accessed to from the comfort of your location. These certifications are conducted by industry experts who have relevant experience in the field of data science and leverage their expertise first-hand to the participants and assures guided learning with paced-out learning plan that could be accommodated as per your choice and availability. And yes, never forgo the benefits that you can reap out of a great data science certification from USDSI, MIT, Stanford, Coursera, etc to amplify your career growth.