AI Guiding Principles – Achievers

At Achievers, we are committed to our mission to Change the Way the World Works. We are always striving to help our employees engage in meaningful work and to find efficiencies to serve our mission better. As part of this at Achievers, we believe in the importance of aligning our AI initiatives with our strategic objectives to create sustainable and scalable value in our business operations, harnessing new technologies while maintaining trust by using AI ethically and responsibly.  To that end, we have identified the following principles that we believe should guide the development and use of AI within Achievers.

1.           Ethical and Responsible Use: AI systems should be developed, deployed, and used in an ethical and responsible manner, respecting privacy, security, and legal requirements.  At Achievers, we strive to foster collaboration between humans and AI systems, recognizing that AI is a tool to augment human capabilities rather than replace them.

2.           Transparency: We strive for transparency in AI systems by providing clear explanations of how the AI algorithms work, the data used, how customers will experience AI within our applications, and provide context for how AI is used in decision-making processes.

3.           Social Responsibility, Fairness and Inclusion: We take measures to identify and mitigate biases in AI systems to ensure fair treatment for all users, regardless of their demographic characteristics or background.

4.           Privacy and Data Protection: At Achievers, data protection is critical to our mission, therefore we are committed to implementing privacy and security measures to keep data secure and ensure compliance with applicable regulations and prevailing industry standards.

5.           Accountability: We strive to establish clear lines of accountability for AI systems within Achievers, ensuring people are accountable for AI systems and acting with integrity in the use of AI in accordance with industry standards. Part of this goal includes regularly monitoring and evaluating the performance of AI systems to identify and address any issues or biases that may arise.