Ethical Data Use: Act Now to Protect Privacy

As organizations collect and use data to drive decisions, the conversation around ethical data use has never been more critical. Data can potentially create incredible value, but it can lead to harm, mistrust, and unintended consequences if a modern culture is not committed to ethical practices. 

Merav Yuravlivker, Chief Learning Officer at Data Society Group, emphasizes this point: “Ethical data use isn’t just about compliance—it’s about holding ourselves to a higher standard. How we treat data today will shape trust and innovation for years to come.”

At its heart, ethical data use means balancing the power of data with responsibility, ensuring that all decisions align with values of transparency, fairness, and respect for privacy.

Ethical data use goes beyond meeting regulatory requirements like GDPR or CCPA. It’s about embedding principles into data collection, storage, and application across the organization.

As AI and data-driven systems become more prevalent, ethical considerations must be a priority for organizations. As pointed out by IBM: “AI bias, also referred to as machine learning bias or algorithm bias, refers to AI systems that produce biased results that reflect and perpetuate human biases within a society, including historical and current social inequality.” This is why proactive ethical frameworks are essential to guide innovation and promote ethical AI practices.

Yuravlivker explains: “Most people might be okay with their data being used for services, but the most important piece is transparency. Users need to know upfront how their data is being leveraged and whether it aligns with their expectations.”

Ethics in data use isn’t a barrier to innovation—it’s an enabler. Transparent and fair data practices build trust, which is essential for the long-term success of any organization.

Organizations that prioritize ethical data use are on par with responsible data practices and are preparing for a future where expectations around accountability and fairness will only increase. Here are three key steps:

Develop Clear Policies

• Create guidelines that outline acceptable data practices and ensure compliance with current regulations.

Embed Ethics into Decision-Making

• Train employees to consider ethical implications when handling data or developing new technologies.

Engage Stakeholders

• Actively involve users and customers in conversations about how their data is used to build trust and transparency.

Yuravlivker emphasizes: “It’s not enough to follow the rules—organizations need to lead by example, demonstrating responsible data practices that set a new benchmark for trust and accountability.”

The rise of generative AI, global privacy laws, and heightened user awareness means that ethical data use will only grow in importance. This will also have drastic impacts on organizations and their ability to adopt and implement AI. Gartner projects: “By 2027, 60% of organizations will fail to realize the anticipated value of their AI use cases due to incohesive data governance frameworks.” Organizations that adopt a proactive approach today will navigate these changes more effectively and build stronger relationships with their customers, stakeholders and internal colleagues.

The question isn’t whether ethical data use is necessary—it’s how quickly organizations can adapt and embed it into their core operational practices.

Need help addressing the challenges and opportunities of ethical data use? Contact Data Society today.