Ethics in Data Analytics
As the field of data analytics has grown and evolved over the last several decades, so have concerns about how data is gathered, recorded, and shared. With so much data in the world today鈥攕ome forecast that more than 180 zettabytes will have been created, stored, and consumed by 2025鈥攊t鈥檚 no small matter to wonder how ethical standards can be enforced in data collection across industries.
Consumers are right to express doubts or misgivings over who is handling their private data, what it鈥檚 exposed to, and where it鈥檚 going. While organizations and businesses rely on data analytics to optimize marketing efforts and make strategically important decisions, everyday individuals may not understand exactly how or why their data could be of benefit to any particular industry.
Even more, since the advent of the internet and resulting digital advancements in data analytics, the creation of federal laws governing the responsible use of data is still evolving.
The role of ethics in data collection and distribution has quickly become a topic of public interest and debate. This blog discusses ethics in data analytics, what they mean, why they matter, and how ethical principles can be implemented in meaningful ways. Read on to learn more.
Principles of Ethics in Data Analytics
The term 鈥渄ata analytics ethics鈥� (sometimes referred to as 鈥渄ata ethics鈥�) involves following legal and moral guidelines to ensure overall accuracy and fairness in the management of data. When organizations practice good data analytics ethics, they can protect their reputation and help safeguard consumer information.
The nature of data analytics鈥攃onverting raw data into actionable insights鈥攊s a sensitive one that often involves personal identifiable information (PII). This calls into question how to uphold ethical decision-making in the data analytics process. In response, several principles of data analytics ethics have been broadly established.
These principles include the following:
- Transparency. This refers to openness, honesty, and clarity about what type of data analytics is being performed and why.聽
- Accountability. Organizations involved in data analytics should answer for and take ownership of their own actions, whether right or wrong.
- Objectivity. Also known as 鈥渇airness鈥� or 鈥渆quity,鈥� this principle means that organizations make every attempt to minimize or remove bias during data collection, evaluation, and conversion.
- Integrity. When dealing with private information, organizations should always obtain consent from individuals before their data is collected or used. Incorporating integrity also means treating sensitive data with respect and care.
Challenges of Implementing Ethical Practices in Data Analytics
While the above ethical principles may be simple to understand, putting them into practice in various areas of data analytics can often be a challenge for many businesses and organizations. Some aspects of data analytics stand to be significantly improved by a strong adherence to an ethical code. Below are five of the most major ones.
- 1. Data Privacy and Security
For an organization to live the ethical principle of integrity, it must protect sensitive information from unauthorized access, security breaches, data leaks, and other similar dangers. These types of threats can compromise the identity and well-being of individuals and entire populations.
Organizations that engage in data analytics should also comply with data protection regulations like the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), California Consumer Privacy Act (CCPA), and more, depending on their industry and location.
- 2. Bias and Fairness
The machine-learning algorithms that data analysts use to streamline their jobs may inadvertently perpetuate racial, cultural, socioeconomic, or other forms of bias. The data scientists that develop these algorithms should be as objective as possible and perform audits regularly to minimize bias.
To further combat bias and reinforce fairness, data analysts should collaborate with other members of their organization, including sales representatives, marketing specialists, human resources managers, and executive leaders. In other words, analysts should maintain lines of communication with every relevant stakeholder and solicit their input on the matter.
- 3. Ethical Use of AI
Over the last decade, the rapid advancement of AI technology and its application in data analytics have created new, layered challenges for many organizations. For example, AI algorithms are often at higher risk of being biased鈥攅ven prejudiced鈥攃ompared to traditional analytics tools.
When organizations incorporate AI in their data analytics operations, they should create standards of responsible AI usage concurrently rather than after the fact. This way, issues related to bias or inaccurate data can be remedied more efficiently. To illustrate, think about how programmers writing code can catch and correct their mistakes more quickly as they write instead of going back through once they鈥檝e finished.
- 4. Stakeholder Trust and Engagement
A key advantage that stakeholders can bring to data-oriented organizations is diversity of thought. When more minds contribute to data analytics processes, the outcomes will tend to be more inclusive and equitable. Stakeholders may include organization members, employees, business partners, clientele, and even the general public.
Seeking diverse stakeholder perspectives and staying open-minded can help eliminate skewed data sets and biases. It can also foster more stakeholder trust and engagement, which, in turn, keeps organizations healthy in the long term.
- 5. Regulatory Compliance
Best practices in data analytics constantly shift based on technological advancement, public opinion, and other factors. This means businesses and organizations must keep current with evolving data ethics laws and regulations.
Compliance and legal departments within such organizations should prioritize research in data analytics regulations and then implement flexible, adaptable policies and procedures reflecting that research. Doing so can help ensure ongoing compliance.
Benefits of Ethics in Data Analytics
Three major benefits come about when individuals and organizations actively engage with the principles of transparency, accountability, objectivity, and integrity:
- Enhanced trust and credibility. Everyone involved in and affected by data analytics is better off when they can extend and earn more trust in one another.
- Improved decision-making outcomes. Data-driven results become clearer and more accurate when problematic bias is eliminated.
- Decreased legal and reputational risks. Organizations can enjoy a sterling reputation or brand image when they practice good data analytics ethics.
Data Analytics Ethics in Practice
Many real-world scenarios can demonstrate the importance of data analytics ethics, and why it is crucial to implement unbiased data analytics tools.
For example, in 2014, Amazon worked to perfect an that would automate their hiring process for software development positions, rating job candidates on a scale from one to five.
Unfortunately, the algorithm reserved the highest ratings for male candidates only. The reason? Amazon鈥檚 computer models were trained to favor candidates who described themselves using verbs more commonly found on male candidates鈥� r茅sum茅s as opposed to those submitted by women.
Those involved in the algorithm鈥檚 development quickly learned about the limitations of their framework, ceased working on the project, and reallocated their resources to less gender-biased endeavors.
Next Steps
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