Addressing ethical considerations has never been more important as our lives become increasingly intertwined with advanced technology. Converging artificial intelligence (AI) and the Internet of Behaviors (IoB) in our daily lives raises ethical questions about behavioral monitoring and using personal data.

While AI and IoB solutions are important tools for enhancing data analytics across various applications and industries, we must acknowledge and understand the ethical issues. Let's explore some common concerns and discuss ways to use these technologies while maintaining integrity and prioritizing privacy and data security.


AI-powered IoB technology collects and uses behavioral data to understand and influence someone's decisions. In this context, behavioral data can include: 

  • Online browsing history

  • Device usage

  • Location data and travel behavior

  • Purchase history

  • Health and fitness tracking, including biometric data

  • Communication patterns

Handling this highly personal data requires stringent safeguards, including practices like informed consent and data anonymization. Informed consent ensures users understand how their data is collected, processed, and used. Data anonymization techniques remove personally identifiable information from datasets while preserving behavioral data at an aggregate level.

Protecting individuals' rights and privacy online is a growing area of importance and demand for business leaders across industries. For working professionals looking to expand their knowledge in this area, the Virginia Tech Master of Information Technology program is a valuable option for upskilling and career advancement. The VT-MIT program also offers 10 graduate certificates in specialized areas like Cybersecurity Management, Cybersecurity Policy, and Information Technology Management. Graduate certificates can be earned as part of a degree program or as stand-alone credentials. 


Algorithmic fairness ensures that AI and IoB systems are not discriminating against individuals or groups based on personal attributes like race, gender, age, and socioeconomic status. Potential biases can arise from training data and algorithm design. 

For example, a healthcare AI system trained on data that excludes certain demographics could lead to incorrect or inadequate diagnoses for certain patients. Similarly, a facial recognition AI solution trained on a dataset of individuals from a certain race or ethnic background might misidentify or discriminate against individuals with different skin tones. 

Achieving fairness in AI and IoB involves scrutiny of training data and algorithm design decisions. Those involved in creating and training AI algorithms should firmly commit to equity and inclusion when immersed in Big Data and have a deep knowledge of business data analytics and related topics. 


Both individuals and organizations share the responsibility of navigating the ethical challenges of AI and IoB. Privacy policies and data consent forms are often lengthy and complex, but users should do their due diligence to review them thoroughly before consenting. 

Transparent data practices build trust and empower users to make informed decisions about who they share their personal data with and its usage, so explanations about data collection and use must be accessible. Data policies detailing collection and usage must be easily accessible and should be written in plain language for maximum transparency and accountability so users can fully understand them. 

Businesses should make privacy policies and consent forms easy to scan and understand, and privacy-by-design principles should drive every data-related initiative and system. Transparency and data protection are fundamental requirements and are especially important when data is sensitive and personal, like in financial settings and healthcare environments

“Importantly, organizations should make sure they are not only being transparent about their data practices but also must follow them. Organizations who do not follow their own stated privacy and security practices can lose credibility with customers and investors and even gain unwanted attention from regulators,” explains Elise Elam, Cyber Law and Policy for Information Technology adjunct professor at Virginia Tech.


Ethical frameworks and guidelines act as a compass for those implementing and maintaining AI and IoB applications. By focusing on principles like fairness, accountability, transparency, and data privacy, engineers and business leaders can harness the power of these technologies without compromising individuals' rights. 

Though AI and IoB are not static solutions. Ongoing monitoring and evaluation are important in addressing emerging ethical concerns and maintaining integrity. Business leaders and policymakers should frame all their AI and IoB decisions around the pillars of privacy protection, fairness in algorithms, and transparency in data handling, updating, and revising policies as new challenges enter into consideration.


Learn more about the opportunities and benefits of AI and the Internet of Behaviors in data management.


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