In the age of artificial intelligence, data is the fuel on which algorithms run. But as powerful as the technology is - the outcomes of AI systems are only as reliable as the data underlying them. Poor-quality data not only leads to inaccurate analyses and biases, but can also pose legal and ethical risks. In this topic file "Data Quality & Compliance," we explore why high-quality data is crucial for reliable, transparent and lawful AI applications.

We cover such topics as:

-What is meant by data quality: accuracy, completeness, timeliness, consistency and provenance.

-The relationship between data quality, bias and explainability of algorithms.

-The importance of data cleansing, governance and auditing in AI projects.

-Relevant laws and regulations, including the General Data Protection Regulation (AVG) and the AI Act (emerging).

-The responsibilities of data controllers, data stewards and compliance officers in ensuring data quality.

-Practical examples of organizations that ran into legal or reputational risks due to poor data quality.

We also cover current discussions, such as the use of synthetic data, validating training data and the role of external data parties.