Explaining how your model thinks (chain-of-thought reasoning, for example), and citing authoritative sources, enhances transparency and builds trust.
AI can be misused for purposes like surveillance or profiling, which infringe on an individual’s privacy rights.
It’s imperative that organizations appoint supervisors to cambodia rcs data ensure that any AI apps used within a company align with certain ethical standards to prevent malpractice.
Algorithmic bias can lead to discrimination
LLMs often learn and pass on biases found in their training data, leading to unfair or discriminatory practices in the hiring of new employees or the lending of money, for example – potentially violating individual privacy rights.
Using diverse and representative datasets, implementing fairness-aware algorithms, and regularly auditing LLMs can reduce such bias.
Addressing the privacy issues listed above requires a comprehensive approach to AI data governance, combining technical, legal, and ethical strategies to ensure the responsible and secure use of this emerging technology.
AI Data Quality Challenges
Ensuring AI data quality isn’t easy. In the same survey mentioned above, we found that data quality is one of the top concerns associated with building GenAI apps. That is because data quality plays a critical role in building trust for AI apps inside organizations.