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Ethics is arguably the hottest product in Silicon Valley's hype cycle today, even as headlines decrying a lack of ethics in technology companies accumulate. After years of largely fruitless outside pressure to consider the consequences of digital technology products, the very recent past has seen a spike in the assignment of corporate resources in Silicon Valley to ethics, including hiring staff for roles we identify here as "ethics owners." In corporate parlance, "owning" a portfolio or project means holding responsibility for it, often across multiple divisions or hierarchies within the organization. Typically, the "owner" of a project does not bear sole responsibility for it, but rather oversees integration of that project across the organization. 

Excerpt in lieu of abstract: Ethics is arguably the hottest product in Silicon Valley's hype cycle today, even as headlines decrying a lack of ethics in technology companies accumulate. After years of largely fruitless outside pressure to consider the consequences of digital technology products, the very recent past has seen a spike in the assignment of corporate resources in Silicon Valley to ethics, including hiring staff for roles we identify here as "ethics owners." In corporate parlance, "owning" a portfolio or project means holding responsibility for it, often across multiple divisions or hierarchies within the organization. Typically, the "owner" of a project does not bear sole responsibility for it, but rather oversees integration of that project across the organization. A remarkable range of internal and external challenges and responses tends to fall under a single analytic framework called "ethics." This strains an already broad term that in some contexts means an open-ended philosophical investigation into moral conditions of human experience and, in other contexts, means the bureaucratized expectations of professional behavior. Likewise, it places strain on corporate structures because it is bureaucratically challenging to disambiguate whether these problems belong in the domain of legal review, human resources, engineering practices, and/or business models and strategy. 

A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outcomes such as fairness, justice, and due process. Bedrock concepts in computer science---such as abstraction and modular design---are used to define notions of fairness and discrimination, to produce fairness-aware learning algorithms, and to intervene at different stages of a decision-making pipeline to produce "fair" outcomes. In this paper, however, we contend that these concepts render technical interventions ineffective, inaccurate, and sometimes dangerously misguided when they enter the societal context that surrounds decision-making systems. We outline this mismatch with five "traps" that fair-ML work can fall into even as it attempts to be more context-aware in comparison to traditional data science. We draw on studies of sociotechnical systems in Science and Technology Studies to explain why such traps occur and how to avoid them. Finally, we suggest ways in which technical designers can mitigate the traps through a refocusing of design in terms of process rather than solutions, and by drawing abstraction boundaries to include social actors rather than purely technical ones. 

The study of influence maximization in social networks has largely ignored disparate effects these algorithms might have on the individuals contained in the social network. Individuals may place a high value on receiving information, e.g. job openings or advertisements for loans. While well-connected individuals at the center of the network are likely to receive the information that is being distributed through the network, poorly connected individuals are systematically less likely to receive the information, producing a gap in access to the information between individuals. In this work, we study how best to spread information in a social network while minimizing this access gap. We propose to use the maximin social welfare function as an objective function, where we maximize the minimum probability of receiving the information under an intervention. We prove that in this setting this welfare function constrains the access gap whereas maximizing the expected number of nodes reached does not. We also investigate the difficulties of using the maximin, and present hardness results and analysis for standard greedy strategies. Finally, we investigate practical ways of optimizing for the maximin, and give empirical evidence that a simple greedy-based strategy works well in practice.