Redefining Talent and Equity in the Age of AI

As Artificial Intelligence (AI) becomes increasingly integral in hiring processes and talent management, there is a pressing need to reassess how we define what makes a qualified candidate and ensure equity is embedded within these technologies. We must also critically examine its role in shaping economic opportunity. While employers use AI to efficiently navigate vast pools of talent, many of these systems are often built on data sets that are too narrow or imbalanced and not taking into account responsible AI standards, which can train the AI to reinforce existing biases.

As a result, individuals from underrepresented communities such as immigrants and refugees—or “edge cases”—are frequently overlooked, not because of their lack of professional experience and skills, but because they do not fit the traditional mold assumed by mainstream algorithms.

This session will explore how employers and entrepreneurs, workforce organizations, and AI developers can work together to embed equity into AI hiring systems, ensuring economic mobility for historically excluded talent. We will explore how we can broaden our approach to talent recognition by including a more diverse set of skills, experiences, and perspectives. By doing so, we can design AI systems can be designed to not only recognize, but also value the diversity inherent in the workforce. This session will discuss how embedding equity into AI algorithms can unlock the transformative power of technology to include those historically excluded from traditional career pathways benefitting both employers and underrepresented talent.

Key discussion points will include:
– Redefining Talent for the Modern Workforce: How can we expand our understanding of talent beyond conventional metrics to include diverse experiences, skills, and capabilities?
– Ensuring Equity in AI Design: What responsible methodologies and practices can be implemented to embed equity into AI systems from the ground up?
– Identifying and Mitigating Hidden Biases: Through examples like our partnership with HiredScore, A Workday Company, we’ll uncover how biases manifest in AI tools and discuss strategies to address them.
– Collaborative Partnerships for Equitable Solutions: What support structures and infrastructure changes are necessary to foster closer collaboration between workforce organizations and AI solution designers?
– Scaling Impact Through Inclusive AI: How can intentional AI design promote equitable outcomes at scale, benefiting both organizations and the wider society?