This element straddles two interactions that are core to platform livelihoods: the interactions, either in person or electronically, that sellers have with buyers; and the interactions, either in person or electronically, that sellers have with the system itself. In some cases, the system is a customer support representative, a trainer, or some other human being. But as is essential to the platform logic, this interaction is often between a seller and an algorithm or some code. Across both types of interactions and across all channels, researchers around the world have heard a variety of impressions and experiences from their study participants. In some cases, these interactions left sellers feeling respected and treated like a human being, while in other cases, these interactions left sellers feeling decidedly less so.

One positive sentiment represented the way the structure of the platform provided cues, standards, and guardrails that collectively serve to professionalize and routinize product and service delivery. In freelancing, for example, designers in the Philippines liked how these sites provided standards for design work by price, quality of briefs, type of work, completion time, and so forth (Crosby and Cahaya 2017). By providing rate cards, it professionalized what is often informal work among home services providers in India (Surie 2020). Clients were less likely to argue with rates or with the app companies. These rates, in turn, translated to better revenues for some of the service professionals on the platform. Just being in the middle of these informal transactions, holding payments in escrow, allowing easier payment transfers, and providing ratings can increase professionalization and put small-scale sellers at ease (Malik et al. 2020).

Against this background of routinization, some find an improvement in the work environment and a higher likelihood of being treated well. For example, in analyzing the experience of ride-hailing drivers in Colombia, Reilly and Lozano-Paredes (2019, 6) explain that 

Dignity and respect at work are difficult to evaluate. There are not currently any regulations in Colombia addressing this issue. For the most part, drivers indicated that their expectations were fulfilled by working with ride-hailing platforms (64.7% indicated they were substantially, and 35.3% indicated they were entirely fulfilled). Workers did not express complaints regarding poor treatment by clients … [and] the perception is that platforms offer a good work environment. [It may be] that workers do not expect more than a well-performing platform so that they can do their job and earn a profit. 

In Making a ‘Pro’: ‘Professionalism’ after Platforms in Beauty-work, Raval and Pal (2019, 13) explain how for women in the beauty industry in India, professionalism is a two-way street, with suggestions to work in certain ways and to dress in certain ways, and also how 

Asserting that “I am a professional” also became their diplomatic way of dealing with unsanctioned and unsolicited requests, both within the confines of a paid job but also outside in the world…. Reflecting on these enactments of professionalism and how the platform symbolically and materially gets configured in them, we want to call attention to a situated discussion of the dignity of labor where donning a uniform or claiming affiliations to app-work can, in fact, further the ‘professional project’ of workers where informality and unorganized employment are rampant. This does not necessarily have to do with the absolute economic well-being of a worker but is an instance of incidental design of work that supports and safeguards the ability to keep working in socio-cultural contexts (such as India) where working-class women encounter risk on a daily basis.

This interplay of two-way humanity and professionalism also turns up in ride hailing in South Africa. Selabe (2017, 53) describes how drivers know that there are specific things they need to do to get high ratings, both in terms of physical appearance and behaviors that make individuals feel comfortable. Even little things like bottles of water and sweets—“When you talk to them, keep it light, and most importantly, professional.” 

In all, these structures are not always dehumanizing. In some cases, workers reported feeling more valued by these anonymous online clients than by the clients they found in their locality (Crosby and Cahaya 2017). In others, even the mechanisms for feedback provided by the platforms were judged to be adequate (D’Cruz 2017).

That said, frustrating or opaque processes are one of the ways in which many workers, in many contexts, have confronted the reality of working with (or for) codes and algorithms. For example, drivers in Bangladesh find it difficult to understand how fares are calculated, untangling a mix of complex pay scales and surge pricing (Kumar, Jafarinaimi, and Bin Morshed 2018; see also Selabe 2017). Drivers and delivery staff in China found imposed segmentation into classes frustrating and unfair (Chen, Sun, and Qiu 2020), while similar issues in Indonesia on Gojek—opaque processes and policy changes—stirred up unrest (Nastiti 2017). Even the basic day-to-day of being routed around a city by an algorithm can make one feel like one has little control. Ola has not always as helpful as they want. When they are on the app, all the same problems apply—drivers complained that they don’t know how far away a fare is when accepted; cancellations impact driver ratings. If driver gets caught in traffic on his way to his fare, his rating can suffer; ratings are not tied to specific drives, so drivers have trouble acting on the feedback (Ahmed et al. 2016). It’s similar to the perception that Uber “is always on the side of the riders” (Geitung 2017).

In freelancing, respondents around the globe report opaque procedures (Malik, Nicholson, and Heeks 2018), confusing taxation (Graham et al. 2017), and sudden rule changes (Wood and Lehdonvirta 2019). It’s the same in local labor, too (Aneja and Sridhar 2019).

But perhaps in no sector are these frustrations better documented than in microtasking. Workers report a lack of transparency about how jobs are distributed, how qualifications are determined, and what constitutes grounds for being blocked from the system (Martin et al. 2016; Gray and Suri 2019). Others are nervous about how checks are cut, and how they can guarantee that they will be paid (Martin et al. 2016). Genuine workers are sometimes penalized as scammers without an explanation (N. Gupta et al. 2014), just part of what some call a “black box” of rejected work (Rani and Furrer 2019).

While there is a palpable possibility of being penalized at the “whim” of the client (Anwar and Graham 2020), the avenues for feedback/redress often seemed inadequate, with “no mechanisms in place to address worker concerns on unfair ratings and deactivation…structural inequalities between workers and organization policies prevent workers from demanding rectifications or explanations” (S. Gupta 2020, 7) see also Graham et al. 2017).   

Indeed, in microwork, “most microtasks are simple and repetitive and do not coincide with high level of education of crowdworkers” (Berg et al. 2018, 4). Human beings are marketed as inexpensive, anonymous (Gray and Suri 2019) replacements for things robots can’t yet do. But is it surprising that people don’t want to be treated as robots (Panteli, Rapti, and Scholarios 2020)? 

Even outside of anonymous microwork, where there is a name and a face to be attached to the platform task, there are several ways in which the experience might leave the worker feeling disrespected lesser. When work is mostly determined by clients, and workers have to meet client deadlines, and when communication with clients takes place only in ways that suit them, there is a power imbalance and workers suffer as a result (Wood et al. 2019). Platform systems around the world thrive on rating systems, which come from feedback provided by buyers to sellers for other buyers to consume. Studies suggest that perceived gender and race bias are reflected in the ratings (S. Gupta 2020).

Class, too matters. Some workers reflected on experiences where lower and middle class people were serving higher status individuals who treated them with a “servant” mentality (S. Gupta 2020; see also Hunt et al. 2019; Aneja and Sridhar 2019). Abuses abound, from condescension and disrespect of time zones (Zhao 2020), to xenophobia in the South African ride-hailing sector (Geitung 2017; Mare, Chiumbu, and Mpofu 2020). There’s probably an interaction between the aloofness of the algorithm and the willingness of some customers to look down on service providers. For example, drivers can struggle with imposed daily targets (Krishna 2020), or with customers who keep moving or cannot give the exact location. “Or, when we get there, they don’t pick up. Also, sometimes they don’t have much respect for us delivery men or just people lower than them. You know what I’m saying. They think: I’m buying, I’m king, you follow me” (Jack, Chen, and Jackson 2017). 

There is perhaps no better or comprehensive writing on this element of the platform livelihoods experience than Gray and Suri’s account of microworkers, although in this case, we will apply the frames more broadly. They describe “weaponized ignorance” on the part of the platforms, “on-demand ghost work platforms see themselves as neutral parties, arguing that they are the software serving as the middlemen managing what economists call two-sided market” (Gray and Suri 2019, 35), and later “algorithmic cruelty” “when the design of an algorithm, platform, or API lacks thought and is unleashed on unsuspecting consumers, people … suffer the unintended consequences. When ‘thoughtless processes’ are introduced into the workplace, especially one with low-income earners who have little bargaining power and with a lot to lose, the unintended economic and social consequences are severe” (Gray and Suri 2019, 68). The touch on many of the themes enumerated above and elsewhere, from risks of nonpayment to the need for hypervigilance, and return to assert that “the worst expression of algorithmic cruelty is disenfranchisement. Under the guise of safety, systems designers make it easy to block or remove an account in case a bad actor tries to cheat the system. This adversarial stance means that good workers are sometimes misinterpreted as shady players” (Gray and Suri 2019, 86).

That is a lot to process. There is a litany of harms that often accompanies the apparent positive structural “professionalizing” benefits of the transaction platform. To close this section, it’s worth noting an interesting skew or interaction identified by Zollmann and Wanjala (2020). They discern a strong interaction between dignity and professionalism. For low-status individuals, the professionalism of the uniform and a role might be a step up, and platforms provide transparency and protection from nonpayment. And yet, for high-status individuals, the platform role can be demeaning and perceived as a step down. As in other parts of this experience, professionalism, and whether one experiences this work as a whole person or something less, may be determined partly by one’s experiences and self-image prior to beginning the work.