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Optimization is offered as an objective approach to resolving com- plex, real-world decisions involving uncertainty and conflicting interests. It drives business strategies as well as public policies and, increasingly, lies at the heart of sophisticated machine learning systems. A paradigm used to approach potentially high-stakes de- cisions, optimization relies on abstracting the real world to a set of decision(s), objective(s) and constraint(s). Drawing from the mod- eling process and a range of actual cases, this paper describes the normative choices and assumptions that are necessarily part of us- ing optimization. It then identifies six emergent problems that may be neglected: 1) Misspecified values can yield optimizations that omit certain imperatives altogether or incorporate them incorrectly as a constraint or as part of the objective, 2) Problematic decision boundaries can lead to faulty modularity assumptions and feedback loops, 3) Failing to account for multiple agents’ divergent goals and decisions can lead to policies that serve only certain narrow inter- ests, 4) Mislabeling and mismeasurement can introduce bias and imprecision, 5) Faulty use of relaxation and approximation methods, unaccompanied by formal characterizations and guarantees, can severely impede applicability, and 6) Treating optimization as a justification for action, without specifying the necessary contex- tual information, can lead to ethically dubious or faulty decisions. Suggestions are given to further understand and curb the harms that can arise when optimization is used wrongfully. 

Privacy technologies support the provision of online services while protecting user privacy. Cryptography lies at the heart of many such technologies, creating remarkable possibilities in terms of functionality while offering robust guarantees of data confidential- ity. The cryptography literature and discourse often represent that these technologies eliminate the need to trust service providers, i.e., they enable users to protect their privacy even against untrusted service providers. Despite their apparent promise, privacy technolo- gies have seen limited adoption in practice, and the most successful ones have been implemented by the very service providers these technologies purportedly protect users from. The adoption of privacy technologies by supposedly adversarial service providers highlights a mismatch between traditional models of trust in cryptography and the trust relationships that underlie deployed technologies in practice. Yet this mismatch, while well known to the cryptography and privacy communities, remains rela- tively poorly documented and examined in the academic literature— let alone broader media. This paper aims to fill that gap. Firstly, we review how the deployment of cryptographic tech- nologies relies on a chain of trust relationships embedded in the modern computing ecosystem, from the development of software to the provision of online services, that is not fully captured by tra- ditional models of trust in cryptography. Secondly, we turn to two case studies—web search and encrypted messaging—to illustrate how, rather than removing trust in service providers, cryptographic privacy technologies shift trust to a broader community of secu- rity and privacy experts and others, which in turn enables service providers to implicitly build and reinforce their trust relationship with users. Finally, concluding that the trust models inherent in the traditional cryptographic paradigm elide certain key trust relation- ships underlying deployed cryptographic systems, we highlight the need for organizational, policy, and legal safeguards to address that mismatch, and suggest some directions for future work. 

According to the theory of contextual integrity (CI), privacy norms prescribe information flows with reference to five parameters — sender, recipient, subject, information type, and transmission principle. Because privacy is grasped contextually (e.g., health, education, civic life, etc.), the values of these parameters range over contextually meaningful ontologies — of information types (or topics) and actors (subjects, senders, and recipients), in contextually defined capacities. As an alternative to predominant approaches to privacy, which were ineffective against novel information practices enabled by IT, CI was able both to pinpoint sources of disruption and provide grounds for either accepting or rejecting them. Mounting challenges from a burgeoning array of networked, sensor-enabled devices (IoT) and data-ravenous machine learning systems, similar in form though magnified in scope, call for renewed attention to theory. This Article introduces the metaphor of a data (food) chain to capture the nature of these challenges. With motion up the chain, where data of higher order is inferred from lower-order data, the crucial question is whether privacy norms governing lower-order data are sufficient for the inferred higher-order data. While CI has a response to this question, a greater challenge comes from data primitives, such as digital impulses of mouse clicks, motion detectors, and bare GPS coordinates, because they appear to have no meaning. Absent a semantics, they escape CI’s privacy norms entirely. 

According to the theory of contextual integrity (CI), privacy norms prescribe information flows with reference to five parameters — sender, recipient, subject, information type, and transmission principle. Because privacy is grasped contextually (e.g., health, education, civic life, etc.), the values of these parameters range over contextually meaningful ontologies — of information types (or topics) and actors (subjects, senders, and recipients), in contextually defined capacities. As an alternative to predominant approaches to privacy, which were ineffective against novel information practices enabled by IT, CI was able both to pinpoint sources of disruption and provide grounds for either accepting or rejecting them. Mounting challenges from a burgeoning array of networked, sensor-enabled devices (IoT) and data-ravenous machine learning systems, similar in form though magnified in scope, call for renewed attention to theory. This Article introduces the metaphor of a data (food) chain to capture the nature of these challenges. With motion up the chain, where data of higher order is inferred from lower-order data, the crucial question is whether privacy norms governing lower-order data are sufficient for the inferred higher-order data. While CI has a response to this question, a greater challenge comes from data primitives, such as digital impulses of mouse clicks, motion detectors, and bare GPS coordinates, because they appear to have no meaning. Absent a semantics, they escape CI’s privacy norms entirely. 

According to the theory of contextual integrity (CI), privacy norms prescribe information flows with reference to five parameters — sender, recipient, subject, information type, and transmission principle. Because privacy is grasped contextually (e.g., health, education, civic life, etc.), the values of these parameters range over contextually meaningful ontologies — of information types (or topics) and actors (subjects, senders, and recipients), in contextually defined capacities. As an alternative to predominant approaches to privacy, which were ineffective against novel information practices enabled by IT, CI was able both to pinpoint sources of disruption and provide grounds for either accepting or rejecting them. Mounting challenges from a burgeoning array of networked, sensor-enabled devices (IoT) and data-ravenous machine learning systems, similar in form though magnified in scope, call for renewed attention to theory. This Article introduces the metaphor of a data (food) chain to capture the nature of these challenges. With motion up the chain, where data of higher order is inferred from lower-order data, the crucial question is whether privacy norms governing lower-order data are sufficient for the inferred higher-order data. While CI has a response to this question, a greater challenge comes from data primitives, such as digital impulses of mouse clicks, motion detectors, and bare GPS coordinates, because they appear to have no meaning. Absent a semantics, they escape CI’s privacy norms entirely.