We are continuously refining our candidate evaluation process to maximize predictive validity and ensure compliance with applicable laws. Previously, we used a fixed weighted average to calculate the Bryq Match Score. However, through local validation studies, we have discovered that this weight may not always be optimal.
Now, with the power of AI, we let the system determine the best weighting that provides the optimal balance to maximize performance prediction and maintain compliance with adverse impact laws. This allows for more flexibility and adaptability in achieving accurate and reliable evaluations.
What current methods is Bryq using for tracking and realization?
Data Monitoring and Analysis: We implemented ongoing data monitoring and analysis processes to track the impact of bias mitigation efforts. This involves regularly internal reviewing and analyzing data to identify any emerging biases and measure the effectiveness of corrective actions taken. This ensures the accuracy and reliability of the information.
User Feedback and Surveys: We gather feedback from users and stakeholders through surveys to get valuable insights into the perceived benefits of bias audits.
Diversity Mix: EEOC-protected attributes (age, gender, ethnicity, disability) that are collected via the respective survey are used to examine the subgroup representation in the data in the Bryq Dashboard.
Comparative Analysis: We conduct comparative analysis between pre-audit and post-audit periods to assess the impact of bias audits. We can then quantify any changes and attribute them to the bias mitigation initiatives.
Transparency: We take the responsibility of providing reporting data to our customers, relieving them from the burden of managing compliance independently.
How does Bryq intend to build on/create new initiatives to further the advancement of responsible AI internally and externally?
To drive the advancement of responsible AI both internally and externally, we have outlined several strategies:
Continuous monitoring and evaluation: We will consistently monitor and evaluate our AI systems to identify any potential biases or ethical concerns.
Transparency through bias audit results: Bryq is committed to sharing the results of bias audits, ensuring transparency in the performance and fairness of our AI algorithms.
Collaboration with independent auditors: We will collaborate with independent auditors, such as Holistic AI, to ensure compliance with relevant legislation and regulations, promoting responsible AI practices.
Employee training and customer awareness: We will provide comprehensive training to our employees and raise awareness among our customers about the necessary actions employers must take to comply with relevant regulations, such as those in NYC.
Sharing best practices: Bryq will share best practices related to responsible AI on our website and social media platforms, providing valuable guidance to organizations seeking to implement ethical AI systems.
Prioritizing data governance and transparency: We will prioritize robust data governance practices to ensure the privacy, security, and responsible use of data in our AI systems. Additionally, we will maintain transparency in our data practices and communicate them effectively to stakeholders.
Ongoing review and improvement: Bryq is committed to regularly reviewing and enhancing our initiatives for responsible AI, staying at the forefront of ethical AI development, and incorporating feedback from stakeholders.
By implementing these measures, Bryq aims to foster the development and adoption of responsible AI practices, both within our organization and across the industry.