Amazon’s ADAPT and it’s Harm on Workers
By Alice Ye | March 2, 2022
Artificial intelligence is rapidly making its way into the workplace, being incorporated in all aspects of the business. One of the greatest drivers for this widespread adoption is the potential of AI to increase employee productivity. Some companies have received positive feedback on using AI in this way. However, some companies have been criticized for treating workers like robots. Amazon in particular has received a lot of attention for using AI to monitor warehouse worker productivity.
What is ADAPT?
Amazon created an AI system called Associate Development and Performance Tracker (ADAPT) that monitors each worker’s productivity and automatically fires workers. ADAPT operates based off of Amazon’s proprietary productivity metric for measuring the productivity of each associate (Carey 2018). The most Amazon has shared about the metric is that it’s based on customer demand and warehouse location (Lecher 2019).
Amazon warehouse facilities will hold weekly productivity performance reviews using ADAPT data. ADAPT will automatically generate any warnings or termination notices to the employee without input from managers. These notices can be overridden in certain scenarios, like mechanical issues or peak operation levels (e.g. Christmas holidays, Amazon Prime Day, etc.) (Carey 2018).
Amazon states that they are able to provide quality, fast services to customers because the system allows associates to be both detailed and efficient (Carey 2018). However at what cost? With ADAPT in place, Amazon workers feel that their workday is managed down to every second. The combination of monitoring, automated supervision, and inflexible expectations leave workers feeling like Amazon views them as robots, instead of humans (Tangermann 2019). Many previous employees have shared that mental and physical health issues are common within the facility environments (Zahn 2019).
Why is ADAPT harming Amazon workers?
The two aspects, out of many, that are at the core of Amazon’s unethical AI usage are the degree of monitoring and the imbalance between the company and employees.
ADAPT monitors workers to a level of detail that takes away autonomy and violates their privacy. Amazon tracks a worker’s Time Off Task (TOT) by capturing gaps in activity and workers are expected to explain each gap. If the explanation is deemed unreasonable or too long, then a warning is issued. If an unreasonable break is 2 hours or longer automatic termination is issued (Carey 2018). This constant detailed monitoring of employees tied with heavy penalties is a loss of autonomy. Some workers have felt that they can’t even properly use their breaks for going to the bathroom because the warehouses are so large (Kantor 2021). This level of surveillance can also be considered a harm to worker privacy (Solove 2006). Workers have lost control over their personal time because they are required to justify how they spent every minute of it. Not only is taking away autonomy an ethical problem (Belmont Report 1979) and violation of privacy, but it can also impact profits. Research shows that loss of employee autonomy leads to distrust and dissatisfaction which in the long term contributes to high employee turnover, slower business growth and lower profits (Matthews 2020).
Figure 1: Showing how one of Amazon’s warehouses, JFK8, is the size of 15 football fields.Workers are expected to walk far distances within their short, timed breaks. Chang W. Lee/The New York Times
Another unethical aspect of ADAPT is the imbalance between company profits and employee welfare. This imbalance is in the AI system and in its implementation. First, taking a look inside the ADAPT shows how it was built to focus on increasing profits. Amazon has mentioned that their proprietary productivity metric is based on customer demand and warehouse location (Lecher 2019). Both factors that align with business profits, rather than employee development. Amazon uses the same productivity metric with the same expectations for all employees. There are no adjustments made for employees who have special circumstances (e.g. medical issues) or who thrive better with different measures (Carey 2018). This further emphasizes how the main purpose of the ADAPT is to increase company profits, not help employees.
Next, looking into how Amazon uses ADAPT shows that exceptions are made to benefit the company but inflexible to employee needs. Amazon explicitly states that automated terminations can be overridden when warehouses are at peak operations levels, like Amazon Prime Day (Carey 2018). This gives allowances when Amazon needs employees the most but there is no equal opportunity given to employees to dispute the automated decisions. For example, an employee could have a medical condition consistently limiting their speed of fulfilling orders but the ADAPT system doesn’t allow the employee to take longer breaks. This inflexibility paired with intense scrutiny has been cited as the cause for prevalent mental health issues amongst workers (Zahn 2019). If Amazon balanced the ADAPT system to distribute benefits between company profits and employee welfare, then some negative consequences would be mitigated.
Figure 2: Amazon worker expressing his opinion that worker health needs to be taken more seriously by Amazon. Spencer Platt/Getty Images
How could Amazon improve their usage of AI on productivity?
There are a couple ways Amazon could improve the way they’re using AI on worker productivity. First, change the role of AI. Instead of using AI to measure how many tasks employees are completing, AI can be used to make tasks easier to complete. For example, Amazon currently uses ADAPT to track how quickly workers can count and verify the number of items in an order. Instead, AI can count the number of items and the worker can investigate packages where there are discrepancies. Then worker time would be spent on more stimulating, valuable tasks. They would be less bored and more satisfied with their work which has been shown to increase productivity and reduce turnover rates (How 2021). Thus, focusing AI in a different way could still result in higher productivity but not cause harm to employees.
Another way is to allow employees to customize the metrics they are evaluated against. In practice, Amazon could provide a range of performance metrics and have employees select metrics that suit their working style. Employees and managers could set what the thresholds and expectations should be based on personal circumstances. This would address the two unethical aspects of ADAPT that were discussed earlier. Employees would gain some control over how ADAPT is tracking their activity and provide a bit more balance in power between Amazon and the workers.
Final Thoughts
Many companies look to Amazon for how to be a successful business. Thus, it’s vital that Amazon ethically uses AI to drive worker productivity. If Amazon is allowed to continue, other companies will do the same, resulting in a new norm. In fact, other companies have already started following in Amazon’s footsteps, like Cognito monitoring their customer service reps talking speed (Roose 2019) and Walmart eavesdropping on employee conversations (Woollacott 2020). As Amazon’s ADAPT system gets more awareness, I hope legislation is created to protect all types of workers, even contract workers.
References
* Carey, Crystal S. (September 4, 2018). “Case No. 05-CA-224856.” Philadelphia, PA: Morgan Lewis. Retrieved February 13, 2022, from https://cdn.vox-cdn.com/uploads/chorus_asset/file/16190209/amazon_terminations_documents.pdf
* Lecher, C. (2019, April 25). How Amazon automatically tracks and fires warehouse workers for ‘productivity.’ The Verge. Retrieved February 13, 2022, from https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations
* Tangermann, V. (2019, April 26). Amazon Used An AI to Automatically Fire Low-Productivity Workers. Futurism. Retrieved February 13, 2022, from https://futurism.com/amazon-ai-fire-workers
* Zahn, M., & Paget, S. (2019, May 9). ‘Colony of Hell’: 911 Calls From Inside Amazon Warehouses. The Daily Beast. Retrieved February 13, 2022, from https://www.thedailybeast.com/amazon-the-shocking-911-calls-from-inside-its-warehouses?ref=scroll
* Kantor, J., Weise, K., & Ashford, G. (2021, December 15). Inside Amazon’s Employment Machine. The New York Times. Retrieved February 13, 2022, from https://www.nytimes.com/interactive/2021/06/15/us/amazon-workers.html
* Solove, Daniel J. (2006). A Taxonomy of Privacy. University of Pennsylvania Law Review, 154:3 (January 2006), p. 477. https://ssrn.com/abstract=667622
* The National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research. April 18, 1979. https://www.hhs.gov/ohrp/sites/default/files/the-belmont-report-508c_FINAL.pdf
* Matthews, V. (2020, April 9). Productivity secrets: better leaders and working smarter. Raconteur. Retrieved February 13, 2022, from https://www.raconteur.net/business-strategy/productivity/productivity-secrets/
* Roose, K. (2019, June 24). A Machine May Not Take Your Job, but One Could Become Your Boss. The New York Times. Retrieved February 13, 2022, from https://www.nytimes.com/2019/06/23/technology/artificial-intelligence-ai-workplace.html
* Woollacott, E. (2020, February 10). Should you be monitoring your staff with AI? Raconteur. Retrieved February 13, 2022, from https://www.raconteur.net/technology/artificial-intelligence/ai-workplace-surveillance/
* How AI is increasing employee productivity. (2021, September 24). Memory. Retrieved February 13, 2022, from https://memory.ai/timely-blog/ai-increase-employee-productivity