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Authority MagazineDear Friends, Authority Magazine is starting a new series about Guardians of AI: How AI Leaders Are Keeping AI Safe, Ethical, Responsible, and True. As AI technology rapidly advances, ensuring its responsible development and deployment has become more critical than ever. How are today’s AI leaders addressing safety, fairness, and accountability in AI systems? What practices are they implementing to maintain transparency and align AI with human values? In this interview series, we would like to talk to leaders of AI companies about how they are working to keep AI safe, ethical, responsible, and true. Most interviews will not require phone interviews and can be done by email. Requirements: We are seeking leaders of AI companies to discuss how they are working to keep AI safe, ethical, responsible, and true.

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SumatoSoftI'm looking to hear from CTOs, CIOs, Heads of AI/Data, or VPs of Operations who led an AI rollout. 1) Tell us about your company: industry and size. 2) Share one AI tool you rolled out successfully in the last 18 months: what changed, and by how much? Numbers welcome. 3) What's one KPI that improved after adopting AI? Please include the old and new numbers. 4) What was the hardest part of getting adoption to stick, and how did you solve it? All responses included in the research will receive a credit with a dofollow backlink.

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BUNCHI’m working on an article about why computer vision models still struggle with edge cases in real-world production environments, even as image recognition technology rapidly improves. I’m looking to speak with AI infrastructure leaders, computer vision teams, ML engineers, and product leaders at companies actively using vision AI systems in production—especially in industries where accuracy and reliability are critical. Questions to help guide responses: - How are teams identifying annotation gaps or dataset weaknesses? - What labeling inconsistencies are hardest to detect? - How are companies validating difficult image categories or ambiguous scenarios? - What QA workflows improved production accuracy most? - How has image annotation changed as models have become more advanced? I’m especially interested in perspectives from companies running customer-facing or operationally critical vision AI systems rather than experimental research projects. Ideal respondents include Heads of AI or Computer Vision, ML Infrastructure leaders, AI Product Managers, Robotics or Autonomous Systems teams, and Engineering leaders overseeing production AI deployments.

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