Ethics Is Not “One and Done”
Implementing ethics in Machine Learning is not a one-and-done effort. Organizations must build trustworthy systems that serve their customer’s best interests fairly. Ultimately, ethically designed machine learning models align with compliance and regulations while also avoiding harmful outcomes.
Practical Business Reasons to Resist the Allure of AI
There are many traps along the journey required to leverage AI/ML to generate value for your business. Success relies on aligning AI/ML initiatives with clear business objectives and understanding their true potential.
Anti-Patterns in Data Mesh
This article explores common anti-patterns in implementing Data Mesh, a decentralized data architecture emphasizing domain-oriented data ownership. While Data Mesh aims to enhance data accessibility and usability across organizations, its success relies on understanding core principles: domain-driven data ownership, data products, and federated governance.
Your Starter Guide to Data Governance
Data governance establishes standards for data collection, storage, and analysis, ensuring accuracy and mitigating risks associated with regulatory non-compliance. Moreover, governance promotes ethical data practices, safeguarding individual privacy rights and societal norms.