Algorithmic Transparency
Practice of making AI decision-making processes understandable and accountable to users, regulators, and affected stakeholders.
Definition
Algorithmic transparency involves documenting model architectures, training data sources, performance metrics, and decision logic to enable external audit and understanding of AI systems.
This practice encompasses technical documentation, bias testing results, and clear explanations of how AI systems make decisions that affect individuals or business outcomes.
Why It Matters
Regulatory compliance increasingly requires algorithmic transparency, with laws like GDPR and emerging AI regulations mandating explainable automated decision-making in many business contexts.
Transparent AI systems build customer trust and enable better business decisions by allowing stakeholders to understand limitations, biases, and appropriate use cases for AI tools.
Examples in Practice
Financial institutions provide algorithmic transparency in loan approval systems, documenting which factors influence credit decisions to comply with fair lending regulations and explain rejections to applicants.
Hiring platforms implement transparency measures in resume screening AI, allowing companies to understand how candidates are ranked and ensuring compliance with employment discrimination laws.
Healthcare AI systems include transparency features that show clinicians which medical factors contributed to diagnostic recommendations, enabling informed decision-making and maintaining clinical accountability.