Ethical Use of Data in AI Applications: Bias, Governance, Transparency and Agentic AI
How can organizations innovate with artificial intelligence without compromising privacy, fairness, transparency, accountability, or public trust?In this episode of RHEMINISCING with Dr. Tony Rhem: Exploring Artificial Intelligence—Past, Present, and the Possible, Dr. Tony Rhem revisits a timely panel discussion from the DGIQ AIGV 2024 Conference on the ethical use of data in AI applications.Moderated by Dr. Rhem, the panel brings together experts in ethical AI, AI governance, data science, and responsible technology to examine why responsible AI begins with the data used to train, test, fine-tune, retrieve information for, and operate AI systems.The discussion explains how poor-quality, biased, improperly governed, or inadequately documented data can produce unreliable outcomes, discriminatory decisions, privacy violations, security vulnerabilities, and significant organizational risk.The panelists also explore how the rapid adoption of generative AI, retrieval-augmented generation, copilots, chatbots, automation, and agentic AI has raised the stakes for organizations. AI systems are no longer limited to generating predictions. They can now create content, interact with customers, retrieve enterprise knowledge, call tools, influence workflows, and participate in operational decisions.Topics discussed include:• What the ethical use of data in AI means• Why ethical AI requires more than regulatory compliance• Bias, fairness, justice, and discrimination in AI systems• Data privacy, consent, provenance, and security• The difference between AI transparency and explainability• The importance of model cards and documented design decisions• Monitoring AI systems for model drift and unintended outcomes• The roles of the EU AI Act, GDPR, NIST AI RMF, and AI assurance frameworks• Risks associated with generative AI and autonomous AI agents• How retrieval-augmented generation can expose conflicting or outdated information• Why “AI hallucinations” are often data, design, governance, or tool-selection problems• The importance of multidisciplinary and culturally diverse AI teams• Developing meaningful ethical AI metrics and key performance indicators• AI’s potential impact on employment, inequality, privacy, sustainability, and society• Why organizations of every size need responsible AI controlsA central message from this conversation is that AI ethics cannot be added at the end of an AI project. It must be designed into the data lifecycle, model-development process, governance framework, deployment environment, organizational culture, and ongoing monitoring program.Organizations must be able to explain what their AI systems do, what data they use, how decisions are made, what actions the systems can take, who may be affected, what controls are in place, and who is accountable when something goes wrong.This episode is essential viewing for board members, executives, AI leaders, data professionals, governance practitioners, compliance officers, risk managers, technology leaders, researchers, and anyone responsible for building, buying, deploying, auditing, or overseeing artificial intelligence.Subscribe to RHEMINISCING with Dr. Tony Rhem for informed conversations connecting AI’s history, current developments, and possible future. Share your perspective in the comments: What is the most significant ethical risk your organization faces as it adopts generative AI or agentic AI?
