Upholding Digital Ethics & Responsible AI
Technology innovations offer incredible benefits, particularly those with AI-based capabilities. But they also come with greater risks if not deployed responsibly and ethically.
Our emphasis on the human-centric philosophy is founded on our strong ethical values and digital responsibility. We apply it throughout our business and employment policies, technology, R&D, design and innovation.
Our approach is that responsible and ethical AI deployments are the smartest and most secure. This enforces us to consider a range of individual and societal harms that the misuse, abuse, poor design, or unintended negative consequences of AI-powered systems may cause.
Our strong emphasis on putting people and ethics first is not just a mantra, it’s the core of our mission.
Teacha Hamilton – Founder & COO – Nebuli.
We Never Conduct Unethical Data Practices
Your data is your power, and your privacy is your human right. No ifs, no buts!
Responsible data models are significantly more intelligent and relevant to specific market needs and user personalisation. More critically, they must consider cultural differences, bias risks, behavioural influences and much more.
We do not scrape and mine people’s data relentlessly and indiscriminately with minimal regard to privacy or encryption. We see this as Lazy AI, and we are firmly against it.
Citations & Copyright Ownership
Our founders, Teacha and Tim, were involved in fighting science-related misinformation using AI, with particular emphasis on reputable citations and copyright ownership. Hence, with the rise of generative AI tools, they are particularly concerned about the risk of an exponential rise and proliferation of harmful disinformation campaigns and deepfakes throughout the web and social media.
To play our part, Nebuli joined Adobe’s Content Authenticity Initiative (CAI), to integrate their tools with our AIQ Large Language Models and data solutions. The CAI focuses on cross-industry participation, with an open, extensible approach to providing media transparency that allows for better evaluation of content provenance.