01/22/20 – Vikram Mohanty – Ghost Work

Summary

In the opening chapter of this book, the authors introduce the world of ghost workers, or gig workers, who have been thriving, almost unnoticed, in the shadows of modern-day software. Here, the authors give us an insight about how the gig economy has grown over the years, in terms of number of workers, where they come from, the different kinds of on-demand platforms and the features they offer, and the different kinds of jobs on these platforms. The highlight of these chapters is how these gig workers sit at the core of Artificial Intelligence (AI), either by providing training labels to build the AI models, or by filling when AI falls short of the job. The authors break down the hype of robots rising, and present the last-mile paradox of AI i.e. the race towards automation will never converge, nor take away all our jobs, but will result in the shifting of jobs from full-time employment towards gig work. They also draw historical analogies to factory work, piece work and outsourcing. Posting tasks for gig workers, instead of hiring people full-time, seems more appealing to companies and requesters for multiple reasons including automated hiring, evaluation and payment, low costs and overhead, and higher-quality work in a short time. The authors discuss the future of employment, which would look more like a world of ghost workers and not robots, and therefore, make the case for focusing our attention on improving this world of on-demand work.

Reflection

1. The hype about AI has been majorly propelled by our media [3]. Companies, especially the rising number of AI start-ups, are partly to blame, as they have to portray themselves as an AI start-up for getting investors onboard. As they explore complex open-ended problems, these standard AI systems are almost certainly bound to fall short, leaving room for the “last-mile” to be filled in by on-demand online workers [1].
   1. Essentially, the efforts of these ghost workers are passed off as the genius of AI, without any credit, and thus propels the hype further. This adds on to the “algorithmic cruelty” of the ghost work platform APIs. 
   2. Sometimes, companies acknowledge the contributions of the experts who are responsible for training and shaping their AI models, but fall short of acknowledging the inevitable presence of these human experts in a predominantly AI workflow [2]. This, once again, can be attributed towards a need to portray themselves as an AI company. 
2. Instead of the usual rhetoric about AIs taking over jobs, this book paints a realistic portrait about the future of employment, which involves a shift towards on-demand crowd work. This necessitates the need to redesign on-demand infrastructure with a goal of enhancing the quality of workers’ lives (illustrated in great detail in the later chapters). The chapters briefly discuss organizational hierarchy and worker collaboration, two important factors contributing towards LeadGenius’s success. These factors are also echoed by Kittur et al [4] as crucial for enhancing the value and meaning of crowd work. 
   1. It’s good to see politicians addressing issues related to gig economy [6, 7] and AI disrupting the traditional workplace [8]. 
3. The gig workers need to be hypervigilant in order to get more work, and earn more money. Employers no longer need to actively assign jobs to employees. Therefore, the cost is now borne through a worker’s time and effort to seek and eventually get the job. This is one area where algorithms could help by connecting workers to jobs on these on-demand platforms [9]. Going one step further, ML models can be used to automatically assign workers to tasks where they can contribute [4].  
4. The AI hype will inevitably blow up, and result in AI-infused systems to invariably rely on human intelligence for complementing (and completing) the shortcomings of AI. Human intelligence, employed either by on-demand labor or any other source, is an invaluable resource, and therefore, shouldn’t suffer the brunt of algorithmic cruelty. To address this, we need human-centered design of tools and platforms. Jeff Bigham, in his “The Coming AI Autumn” post, points out some areas where HCI research and practice could help in designing intelligent systems and making them useful for people. 
5. These chapters illustrate many real-world examples where humans are needed to “backfill decision-making with their broad knowledge of the world” to make up for the AI’s limitations e.g. spike in search terms during a disaster, identifying hate speech, finding a great springtime wedding venue, face verification. Partially to blame is the predominant use of artificial/synthetic datasets for training AIs, which calls for designing problems rooted in the real world. 

Questions

1. Let’s say, we have an organization with a human workforce using an AI system for solving a certain problem. The whole workflow involves a feedback channel from the human experts that is also used to train certain aspects of the AI systems, which going forward, may reduce the role of these human experts. Some of these experts may be AI critics as well. Should this organization be collecting data from these experts? If so, how should the workflow be designed? What are some of trade-offs?
2. The book chapters raises some interesting points about global labor arbitrage and localization of data. Most of the AI systems being built are almost always deficient of data coming in from places/regions/countries with lower labor costs, and therefore, may be biased towards non-US data (face recognition, speech translation, etc.). Why is that the case? How should this be addressed?
3. “The API isn’t designed to listen to Ayesha”. (How does Ghost Work Work?) Has anyone been on the receiving end of algorithmic cruelty? What kind of systems or intelligent user interfaces did you wish for, if any? 
4. How should journalists cover AI? How should AI claims be fact-checked? 

References

1. The rise of ‘pseudo-AI’: how tech firms quietly use humans to do bots’ work https://www.theguardian.com/technology/2018/jul/06/artificial-intelligence-ai-humans-bots-tech-companies
2. AI at the Speed of Real Time: Applying Deep Learning to Real-time Event Summarization https://www.dataminr.com/blog/ai-at-the-speed-of-real-time-applying-deep-learning-to-real-time-event-summarization
3. An Epidemic of AI Misinformation https://thegradient.pub/an-epidemic-of-ai-misinformation/
4. Kittur, A., Nickerson, J. V., Bernstein, M., Gerber, E., Shaw, A., Zimmerman, J., … & Horton, J. (2013, February). The future of crowd work. In Proceedings of the 2013 conference on Computer supported cooperative work (pp. 1301-1318).
5. AI and automation will disrupt our world — but only Andrew Yang is warning about it https://thehill.com/opinion/technology/469750-ai-and-automation-will-disrupt-our-world-but-only-andrew-yang-is-warning
6. Elizabeth Warren Takes On the ‘Gig Economy’ https://www.thenation.com/article/elizabeth-warren-takes-on-the-gig-economy/
7. Pete Buttigieg just called out Uber and McDonald’s for their treatment of workers — and said beefing up unions is the best way to protect them https://www.businessinsider.com/pete-buttigieg-plan-to-overhaul-the-gig-economy-2019-7
8. The Coming AI Autumn https://jeffreybigham.com/blog/2019/the-coming-ai-autumnn.html
9. Prolific. https://www.prolific.co/