Tech vs. Touch: The Evolution of Cosmetic Grading

How does balancing human judgment with robotic precision revolutionize the device grading process in the secondary market? 

In this episode, host Allyson Mitchell, VP of Sustainability at Apkudo, and Asa Gismervik, Apkudo’s Director Of Quality Assurance, discuss the challenges associated with cosmetic grading. The reality is that humans don't agree, which means humans and robots won't agree either. Apkudo is deploying a new alignment process to overcome the inherent limitations of subjective grading techniques to achieve greater accuracy. 
 
The conversation takes a deeper dive into this fascinating world of human-machine collaboration, exploring the pivotal role of blind assessments in ensuring unbiased evaluations. Asa shares his insights into the challenges of aligning human and machine interpretations and the dangers of overfitting algorithms to specific datasets. You’ll hear how large sample runs are crucial in fine-tuning algorithms to bridge the gap between manual and automated grading techniques. Ultimately, Apkudo seeks to establish a unified approach to cosmetic grading that harmonizes the efforts of humans, machines, and supply chain partners, paving the way for a more consistent and reliable secondary market for connected devices. 
 
In this episode: 

• (00:00) Allyson and Asa discuss the dual aspects of device grading: functional and cosmetic states. Asa explains the impact of grading on resale value and customer satisfaction, highlighting the potential consequences of overgrading and undergrading.  
• (09:00) The collaboration between human insight and robotic precision in cosmetic grading. The importance of blind assessments to ensure unbiased results, and the risk of overfitting algorithms and the need for large sample runs to refine these algorithms, with the aim of achieving a standardized approach to grading across the industry. 
• (14:00) Tools and guidelines used by human graders and the precision capabilities of machines in assessing device conditions. There is a complementary nature of human judgment and machine accuracy -  humans provide intent and context that machines cannot inherently discern. 
• (16:30) Asa outlines the risk of overfitting algorithms to specific datasets, how large sample runs are used to refine grading algorithms. 
• (19:30) Establishing a standardized cosmetic grading approach, human intent teaching robots in grading process. 
• (0:23:00) Future of human-machine collaboration in device grading. Asa is excited about transitioning from customer relationships to partnerships, emphasizing collaborative efforts to improve grading processes. The industry-wide impact and the ongoing efforts to refine grading standards, ultimately aiming for greater alignment and efficiency between humans, machines, and supply chain partners. 

Resources: 

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