Pay and Performance Management: Are they two sides of the same dysfunctional coin?

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This webinar discusses the need for a transformation in performance management and reward systems, emphasizing the importance of motivation beyond money and the shift towards more team-oriented and purpose-driven approaches.

Key Insights

Responsible AI in HR involves ensuring that AI tools are valid, reliable, accountable, and transparent to make trustworthy and fair decisions. It is essential to evaluate the effectiveness and bias of AI tools, and not solely rely on the presence of AI in the tool. HR professionals should ask for technical reports or manuals from AI vendors to understand how the tool was developed, its validity, reliability, and potential biases.

Additionally, requesting evidence of test-retest reliability and validity can help in evaluating the tool’s effectiveness. AI tools used in HR, whether for hiring, performance management, or other decisions, are considered tests if they have evaluative properties that impact business decisions.

Ensuring job relevance and fair treatment of individuals is crucial in utilizing AI tools effectively. The integration of AI in HR requires a focus on job relevance, effectiveness, and fairness simultaneously. Avoiding bias, particularly related to protected classes, and emphasizing the importance of job characteristics in decision-making are key considerations when using AI tools.

Evaluation of AI tools in HR should prioritize evidence of job relevance, predictive validity, and fairness, rather than solely focusing on the absence of bias. Balancing effectiveness and fairness in AI-based decision-making is essential. HR professionals should approach the adoption of AI tools with caution, ensuring that the tools they choose have been thoroughly evaluated for validity, reliability, and fairness.

Seeking expert advice and conducting in-depth assessments of AI tools can help mitigate risks and ensure ethical decision-making. Continuous monitoring, data collection, and analysis are essential for assessing the performance and impact of AI tools in HR. Ongoing evaluation of test-retest reliability, validity, and job relevance can help organizations make informed decisions and improve the effectiveness of AI-based assessments in HR practices.

Timestamped highlights:

06:45 Eric emphasized the importance of evaluating AI tools based on their actual performance and not just their AI capabilities.

10:20 Jay highlights the need for technical reports or data to support the validity and reliability of AI tools, especially in HR decision-making.

15:30 Fred discusses the significance of continuous monitoring and evidence gathering to ensure the effectiveness and fairness of AI tools in HR practices.

23:10 Eric underscores the importance of AI tools being both unbiased and effective in predicting job-related outcomes for HR decision-making.

31:55 Fred explains the concept of bias in assessments and the need to focus on job relevance rather than demographic characteristics in HR evaluations.

37:40 Jay emphasizes the importance of ensuring that AI tools measure job-relevant skills and effectiveness beyond just being unbiased in HR decision-making.


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