With Great Tech Comes Great Responsibility
How we see our role as stewards of AI
As technologists in the Artificial Intelligence space, we understand there are very real implications of the AI products and solutions we develop. The impact can be revolutionary, elevating organizations toward success. It can also have real consequences if not designed and implemented ethically. As stewards of Responsible AI, retrain.ai continues to play an active role in submitting input on the creation of new regulations. We partner with organizations that advocate for Responsible AI, such as the Responsible AI Institute and the World Economic Forum, with whom we’re working to help define and develop the standards for responsible AI.
In 2023, retrain.ai launched a first-of-its-kind Responsible HR Forum to form a community of stakeholders from the technology, advocacy, government and business sectors committed to ethical AI use in HR processes. You can learn more about the Responsible HR Forum below.
The Five Pillars of Responsible AI
As AI becomes more embedded in HR systems, enterprise leaders face increased responsibility to ensure their solutions use Responsible AI to mitigate unintended bias risk. In all, there are 5 pillars of Responsible AI:
Explainability and Interpretability
AI machine learning outcomes, as well as the methodology which produces them, are explainable in easily understandable business-speak. Platform users have visibility into the external and internal data being utilized and the platform’s data structurization and outcomes delivery.
AI machine learning models mitigate unwanted bias by focusing on role requirements, skills maps and dynamic employee profiles while masking demographic and other information that can potentially introduce bias.
Data used to test bias is expansive enough to accurately represent a large data pool while being granular enough to provide accurate, detailed results.
Data Quality and Rights
AI system complies with data privacy regulations, offering transparency to the user around proper sourcing and usage of data, and avoiding using data beyond its intended and stated use.
AI systems meet rigorous accountability standards for proper functioning, responsible methodology and outcomes, and regular compliance testing.
Transparency and Explainability in AI
How retrain.ai is a responsible, white-box solution
The ultimate aim of Responsible AI is to reduce the risk of unintended bias. To understand how this works, and to adjust for accuracy over time, it’s critical to have transparency throughout the process.
The level of transparency needed to fully explain an AI solution can only be found in what is referred to as a white box solution. With this approach, a full end-to-end view of an AI system’s functionality enables system users to see the what of the system–its data output–while also being able to ask the why–the methodology behind the results.
As a fully explainable, Responsible AI solution, retrain.ai provides interpretability to allow data scientists and analysts to test the design and internal structure of the system to authenticate the input and outflow, gauge for errors or inconsistencies, and optimize functionalities accordingly.
Are you prepared for a new Responsible AI landscape?
Enterprise solutions will be increasingly held to government-regulated compliance parameters.
Effective May 6th, 2023, New York City will become the first in the country to require bias audits for hiring using AI technologies. Independent auditors will be able to peek behind the curtain to evaluate the efficacy of your automated tools. Based on recent legislative discussions in states like California and Illinois, this law won’t be the last Responsible AI law your company is set to face.
Learn how Responsible AI can transform your hiring process
If you want to learn how Responsible AI can transform your hiring process, Book a Demo to see our Talent Intelligence Platform in action.