April 18, 2025

Ep 63: Fairness at Work Through Trusted Data and Human-Centric AI with Siri Chilazi

53 minutes

Bob speaks with Siri Chilazi about human-centric solutions in the workplace, particularly in the context of fairness and equality. They discuss the need for evaluation and legislation of AI, the challenges of data maturity, and the role of buyers in shaping technology solutions. They also touch on current trends in US-based organizations regarding diversity and inclusion, and ongoing efforts to ensure fairness in hiring practices. Siri and Bob discuss the critical influence of fairness in organizational performance, the integration of human-centric AI, and the importance of addressing pay equity. They explore work redesign to enhance human and AI collaboration, flexibility in work structures, and the significance of AI literacy for future generations. The dialogue emphasizes the necessity of cognitive diversity within teams and the proactive approach organizations must take to ensure equitable AI access and adoption.

Keywords

AI, fairness, workplace equality, technology regulation, data-driven solutions, human-centric AI, accountability, hiring practices, diversity, inclusion, fairness, organizational performance, human-centric AI, pay gaps, work redesign, AI literacy, future of work, flexibility, diversity

Takeaways

Importance of focusing on human-centric solutions amidst AI advancements.

Growing need for a solution-centered approach to workplace equality.

Testing and experimentation are critical in evaluating fairness solutions in the workplace.

Analogy of clinical trials is used to stress the importance of rigorous testing for technology.

Lack of regulatory oversight in technology similar to the FDA in medicine.

Educated buyers can influence tech providers to be more transparent and accountable.

Public accountability measures can drive companies to improve fairness and DEI efforts.

Current uncertain climate in corporate America is one of caution and risk aversion.

Everyday actions can lead to significant changes in fairness and inclusion, which is essential for organizational performance.

Human-centric AI can enhance team effectiveness.

Cognitive diversity is crucial for recognizing different use cases.

AI prompts important conversations about skills and pay.

Job roles should be redesigned to leverage human and AI strengths.

AI literacy is vital for all workers, not just knowledge workers.

Engaging with AI must be a core competency for all.

Trust in AI outputs is necessary for effective use.

Sound Bites

"We still focus on what makes us human."

"We can do better than this in terms of fairness."

"The future of work is humans plus AI."

"We need cognitive diversity within teams."

"AI literacy is critical across all industries."

"No one should be left behind in AI training."

"Engaging with AI is a core competency."

"Trust is a huge factor in AI literacy."

Chapters

00:00 Introduction to Human-Centric Solutions

03:06 Exploring Fairness at Work

05:46 The Importance of Testing and Regulation

09:00 Data Maturity and Technology Challenges

12:05 Accountability in Technology Solutions

15:11 The Role of Buyers in Shaping Technology

18:09 Current Trends in Corporate America

21:13 The Future of Fairness and Inclusion

25:47 The Role of Fairness in Organizational Performance

27:11 Human-Centric AI: Enhancing Team Effectiveness

30:11 Addressing Pay Gaps and Job Valuation

33:03 Work Redesign: Human and AI Collaboration

36:43 Flexibility in Work Structures

38:25 The Importance of AI Literacy

45:55 Preparing Future Generations for AI Engagement

Siri Chilazi: https://www.linkedin.com/in/sirichilazi

“Make Work Fair”: https://makeworkfair.com/

For advisory work and marketing inquiries:

Bob Pulver: https://linkedin.com/in/bobpulver

Elevate Your AIQ: https://elevateyouraiq.com

Thanks to Warden AI (https://warden-ai.com) for their sponsorship and support of the show! Warden is an AI assurance platform for HR technology to demonstrate AI-powered solutions are fair, compliant and trustworthy.

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