Data Science Ethics: Issues and StrategiesData Science Ethics: Issues and StrategiesData Science Ethics: Issues and Strategies

Organizations worldwide are recognizing the tremendous potential of data science. When professional services firm Deloitte surveyed more than 120 CEOs in 2022, it found that 91% planned to invest in artificial intelligence (AI) during the next year. Among those surveyed, 63% viewed AI as a tool to speed intelligent insight, and 53% believed AI could offer clarity to strengthen decision-making.

As the field of data science continues to expand its reach, ethical considerations are becoming more critical. Issues arising in areas such as data privacy and bias in data analysis have demonstrated a need for data science ethics. In approaching their work, data scientists and the organizations that employ them need to adhere to ethical principles.

Aspiring data scientists who may be considering enrolling in an online Master of Science in Data Science program can benefit from learning about critical ethical considerations in the field, as well as how to promote ethics in the work they conduct.

Data scientists need to have a firm understanding of the concept of data science ethics and the importance of upholding ethical principles.

Definitions of data science ethics can vary, but they tend to focus on avoiding harm and building trust.

Data ethics involves evaluating practices that generate, collect, analyze, and disseminate data that could potentially affect people and society adversely. Ethics involves concepts of whether conduct related to data is right or wrong, transparent, and defensible. Further, data ethics attempt to maintain the trust of parties such as consumers, users, clients, patients, employees, and partners.

Working with data ethically involves adhering to certain principles. For example, the Federal Data Strategy has developed the Data Ethics Framework, which requires individuals in federal government agencies who work with data to:

In another example, the World Economic Forum has outlined ethical principles for AI systems in areas such as:

While the benefits of ethics in data science might seem obvious, certain business advantages of adhering to ethical principles also exist. For example, businesses that follow ethical principles can:

Businesses that follow ethical principles can also:

Data scientists should be aware of legislation that makes adherence to ethical principles of data science even more important.

Individuals working in data science have put forth their own approaches to infusing ethics into data science.

In practice, integrating ethics and data science involves several important considerations. Examples of those considerations are highlighted below.

Protecting data privacy is critical because it helps to ensure the dignity and safety of the people who provided that data. The National Institute of Standards and Technology (NIST) has developed a framework for ensuring data privacy that incorporates actions such as:

Transparency is a cornerstone of building customer trust. To promote transparency in using data, organizations need to be clear about the data they collect, how they secure the data, and how they’ll use the data. Transparency also extends to disclosing information about data-sharing activities, the sale of data, and the algorithms on which organizations rely.

While it may be natural to confuse privacy with confidentiality, data scientists need to know the distinction. The concept of privacy can be viewed as encompassing the rights of individuals to control how their data can be used. In contrast, confidentiality spans both the safeguarding of data and the proper classification of data in an organization’s field.

Confidentiality applies to both consumer data and an organization’s own data. For example, in addition to collecting data from customers, an organization may have data on internal processes or strategic planning that it considers confidential. Properly classifying all data is the first step in controlling confidentiality.

The importance of data accuracy and quality in data science is clear: Reductions in accuracy and quality impair an organization’s ability to make good decisions based on data science. Therefore, ethical data science practices encompass assessing data accuracy and quality.

Data scientists can monitor the quality of data by examining data attributes such as:

Data scientists are responsible for their work and how it affects the world. Ethical data scientists demonstrate their accountability in several ways. For example, they:

Examining examples of data ethics issues helps to reinforce the importance of ethics in the field. Knowing what can go wrong when ethical lapses occur can be informative, as the examples below illustrate.

Bias can emerge when errors in data are complex or even overlooked. Types of bias include the following:

Misuse of data is characterized by individuals or organizations using data in ways that differ from the intended use of that data. Of course, the theft of data and its subsequent use could be viewed as an extreme misuse of data, but misuse encompasses other situations.

Ethical data science is also critical in predictive analytics, which has particular potential to impair privacy. For example, predictive analytics has been used to:

A person’s privacy can be violated if sensitive information about that individual is predicted without their knowledge or consent.

Data scientists must also be attentive to the risks of overrepresentation or underrepresentation in the data they use. The potential for underrepresentation of ethnic groups in health data leads to concerns that:

As outlined below, organizations and data scientists can pursue numerous strategies to practice ethics in data science.

Organizations should communicate their values regarding data both internally and externally. This can include actions such as:

Having an executive in the C-suite who champions ethical data science is beneficial in reinforcing the importance of ethics. In addition, the organization’s top executives and board members must be kept in the decision-making loop on matters of ethics. Educating board members about the significance of ethics in data science allows them to monitor whether an organization adheres to its values.

Forming rules for data use is fundamental to achieving ethical data science and enforcing accountability. Data use rules specify the parties in an organization who are authorized to access various types of data, the purposes for which they can use that data, and where the data will be stored. Organizations need to place limits on the length of time data can be stored and develop specific processes for deleting data.

Embedding individuals with data ethics expertise into operational teams can help enforce an organization’s data science ethics. These individuals could be employees of the organization or external experts in the field. McKinsey & Co. cites an example in which a company asked an expert from academia to participate on an operational team to provide insight on the environmental effects of certain types of data use.

Organizations that actively review data and algorithms for bias improve their ability to conduct ethical data science. Implementing a safeguard to regularly review data used in AI models can ensure its timeliness and relevance.

The Data & Trust Alliance, an organization whose members include a diverse field of large corporations, has detailed safeguards to defend against algorithmic bias in vendors’ workforce decision-making. These safeguards include questions to ask when examining a vendor’s practices and assessing the risk of bias.

Data scientists can use ethics checklists when conducting data science projects. For example, DrivenData, which hosts data science challenges on behalf of other organizations, has devised an ethics checklist that data scientists can use to help ensure that they’ve followed ethical best practices. The checklist is a list of questions that helps determine whether a data scientist has considered various aspects of ethics.

The Institute for Ethical AI & Machine Learning has developed a set of responsible principles for machine learning that professionals in the field can elect to follow. These principles require professionals to:

Organizations such as the USDSI, DAA, and ACB have crafted formal codes of ethics for data scientists to follow. Individual businesses and organizations can also develop codes of ethics that are tailored to their activities.

Organizational codes of ethics can:

Educational programs in data science can provide a good foundation for ethical practice in the field. For example, Master of Science (MS) in Data Science degree programs offer the following:

Data science educational programs also offer students opportunities to enhance their skills in collaboration and communication. This gives students the chance to discuss ethical issues in data science with their peers and deepen their understanding of the importance of ethics.

Working on data science projects as part of an educational program represents an opportunity for students to strengthen their skills in ensuring data privacy, data justice, data accuracy, transparency, and accountability.

When based on a foundation of solid ethics, data science has much to offer the world. Professionals in the field who have knowledge of and consistently apply data science ethics can help organizations realize their objectives in a responsible manner.

Individuals who have an interest in data science can explore Maryville University’s online MS in Data Science degree program to learn how it can help them pursue their professional ambitions. Offering in-demand skills that prepare students for jobs in data science, Maryville’s program could put you on the path to a rewarding career. Take the first brave step on your career path today.

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Federal Data Strategy, Data Skills Catalog and Data Ethics Framework Now Available

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