Ride hailing platforms, such as Uber, Lyft, Ola or DiDi, have traditionally focused on the satisfaction of the passengers, or on boosting successful business transactions. However, recent studies provide a multitude of reasons to worry about the drivers in the ride hailing ecosystem. The concerns range from bad working conditions and worker manipulation to discrimination against minorities. With the sharing economy ecosystem growing, more and more drivers financially depend on online platforms and their algorithms to secure a living. It is pertinent to ask what a fair distribution of income on such platforms is and what power and means the platform has in shaping these distributions.In this paper, we analyze job assignments of a major taxi company and observe that there is significant inequality in the driver income distribution. We propose a novel framework to think about fairness in the matching mechanisms of ride hailing platforms. Specifically, our notion of fairness relies on the idea that, spread over time, all drivers should receive benefits proportional to the amount of time they are active in the platform. We postulate that by not requiring every match to be fair, but rather distributing fairness over time, we can achieve better overall benefit for the drivers and the passengers. We experiment with various optimization problems and heuristics to explore the means of achieving two-sided fairness, and investigate their caveats and side-effects. Overall, our work takes the first step towards rethinking fairness in ride hailing platforms with an additional emphasis on the well-being of drivers.
Ranked search results and recommendations have become the main mechanism by which we find content, products, places, and people online. With hiring, selecting, purchasing, and dating being increasingly mediated by algorithms, rankings may determine career and business opportunities, educational placement, access to benefits, and even social and reproductive success. It is therefore of societal and ethical importance to ask whether search results can demote, marginalize, or exclude individuals of unprivileged groups or promote products with undesired features.In this paper we present FairSearch, the first fair open source search API to provide fairness notions in ranked search results. We implement two algorithms from the fair ranking literature, namely FA*IR (Zehlike et al., 2017) and DELTR (Zehlike and Castillo, 2018) and provide them as stand-alone libraries in Python and Java. Additionally we implement interfaces to Elasticsearch for both algorithms, that use the aforementioned Java libraries and are then provided as Elasticsearch plugins. Elasticsearch is a well-known search engine API based on Apache Lucene. With our plugins we enable search engine developers who wish to ensure fair search results of different styles to easily integrate DELTR and FA*IR into their existing Elasticsearch environment.
Ranking algorithms are being widely employed in various online hiring platforms including LinkedIn, TaskRabbit, and Fiverr. Since these platforms impact the livelihood of millions of people, it is important to ensure that the underlying algorithms are not adversely affecting minority groups. However, prior research has demonstrated that ranking algorithms employed by these platforms are prone to a variety of undesirable biases. To address this problem, fair ranking algorithms (e.g., Det-Greedy) which increase exposure of underrepresented candidates have been proposed in recent literature. However, there is little to no work that explores if these proposed fair ranking algorithms actually improve real world outcomes (e.g., hiring decisions) for minority groups. Furthermore, there is no clear understanding as to how other factors (e.g., job context, inherent biases of the employers) play a role in impacting the real world outcomes of minority groups.In this work, we study how gender biases manifest in online hiring platforms and how they impact real world hiring decisions. More specifically, we analyze various sources of gender biases including the nature of the ranking algorithm, the job context, and inherent biases of employers, and establish how these factors interact and affect real world hiring decisions. To this end, we experiment with three different ranking algorithms on three different job contexts using real world data from TaskRabbit. We simulate the hiring scenarios on TaskRabbit by carrying out a large-scale user study with Amazon Mechanical Turk. We then leverage the responses from this study to understand the effect of each of the aforementioned factors. Our results demonstrate that fair ranking algorithms can be an effective tool at increasing hiring of underrepresented gender candidates but induces inconsistent outcomes across candidate features and job contexts.
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Social media platforms had for a long time successfully positioned themselves in the “sweet spot” between beneficial legislative protections and a remarkable absence of obligations (Gillespie 2010: 348), yielding little need to take direct responsibility for the content of users. Increasingly, and specifically since 2016, public and policy pressure has pushed platforms to become something different: not the allegedly neutral tech companies, but powerful intermediaries responsible for the functioning of public discourse and democracy. Platforms have struggled to develop their positions and processes for handling contested and delicate issues such as hate speech and misinformation, and the (at times unwritten) policies are still changing regularly. The panel examines four interrelated aspects of platform policies, including (1) the complexification and commodification of copyright content moderation, (2) platform verification processes and policies to classify some users, things, and places as ‘official,’ or ‘authentic’, (3) the relationship between human reviewers and AI in the enforcement of content moderation policies, and (4) the dilemma platforms face when turning to human rights as a standard for their platform policies. Taken together, the papers of this panel analyze crucial power dynamics and inequalities embedded within and extending beyond platform policies. For this, the panel convenes a productive multidisciplinary conversation and methodological exchange. This examination of the social, political, legal and economic underpinnings of recent changes in platform policies from a global perspective will allow us to better understand the ability of platforms to “re-fashion the world in their image” and to foster change.
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