Policies significantly shape computation’s societal impact, a crucial HCI concern. However, challenges persist when HCI professionals attempt to integrate policy into their work or affect policy outcomes. Prior research considered these challenges at the “border” of HCI and policy. This paper asks: What if HCI considers policy integral to its intellectual concerns, placing system-people-policy interaction not at the border but nearer the center of HCI research, practice, and education? What if HCI fosters a mosaic of methods and knowledge contributions that blend system, human, and policy expertise in various ways, just like HCI has done with blending system and human expertise? We present this re-imagined HCI-policy relationship as a provocation and highlight its usefulness: It spotlights previously overlooked system-people-policy interaction work in HCI. It unveils new opportunities for HCI’s futuring, empirical, and design projects. It allows HCI to coordinate its diverse policy engagements, enhancing its collective impact on policy outcomes.

Building systems that are good for society in the face of complex societal effects requires a dynamic approach. Recent approaches to machine learning (ML) documentation have demonstrated the promise of discursive frameworks for deliberation about these complexities. However, these developments have been grounded in a static ML paradigm, leaving the role of feedback and post-deployment performance unexamined. Meanwhile, recent work in reinforcement learning has shown that the effects of feedback and optimization objectives on system behavior can be wide-ranging and unpredictable. In this paper we sketch a framework for documenting deployed and iteratively updated learning systems, which we call Reward Reports. Taking inspiration from technical concepts in reinforcement learning, we outline Reward Reports as living documents that track updates to design choices and assumptions behind what a particular automated system is optimizing for. They are intended to track dynamic phenomena arising from system deployment, rather than merely static properties of models or data. After presenting the elements of a Reward Report, we discuss a concrete example: Meta’s BlenderBot 3 chatbot. Several others for game-playing (DeepMind’s MuZero), content recommendation (MovieLens), and traffic control (Project Flow) are included in the appendix.

The implementation of AI systems has led to new forms of harm in various sensitive social domains. We analyze these as problems How to address these harms remains at the center of controversial debate. In this paper, we discuss the inherent normative uncertainty and political debates surrounding the safety of AI systems.of vagueness to illustrate the shortcomings of current technical approaches in the AI Safety literature, crystallized in three dilemmas that remain in the design, training and deployment of AI systems. We argue that resolving normative uncertainty to render a system 'safe' requires a sociotechnical orientation that combines quantitative and qualitative methods and that assigns design and decision power across affected stakeholders to navigate these dilemmas through distinct channels for dissent. We propose a set of sociotechnical commitments and related virtues to set a bar for declaring an AI system 'human-compatible', implicating broader interdisciplinary design approaches.

Despite interest in communicating ethical problems and social contexts within the undergraduate curriculum to advance Public Interest Technology (PIT) goals, interventions at the graduate level remain largely unexplored. This may be due to the conflicting ways through which distinct Artificial Intelligence (AI) research tracks conceive of their interface with social contexts. In this paper we track the historical emergence of sociotechnical inquiry in three distinct subfields of AI research: AI Safety, Fair Machine Learning (Fair ML) and Human-Inthe-Loop (HIL) Autonomy. We show that for each subfield, perceptions of PIT stem from the particular dangers faced by past integration of technical systems within a normative social order. We further interrogate how these histories dictate the response of each subfield to conceptual traps, as defined in the Science and Technology Studies literature. Finally, through a comparative analysis of these currently siloed fields, we present a roadmap for a unified approach to sociotechnical graduate pedogogy in AI.

While formal definitions of fairness in machine learning (ML) have been proposed, its place within a broader institutional model of fair decision-making remains ambiguous. In this paper we interpret ML as a tool for revealing when and how measures fail to capture purported constructs of interest, augmenting a given institution's understanding of its own interventions and priorities. Rather than codifying "fair" principles into ML models directly, the use of ML can thus be understood as a form of quality assurance for existing institutions, exposing the epistemic fault lines of their own measurement practices. Drawing from Friedler et al's [2016] recent discussion of representational mappings and previous discussions on the ontology of measurement, we propose a social measurement assurance program (sMAP) in which ML encourages expert deliberation on a given decision-making procedure by examining unanticipated or previously unexamined covariates. As an example, we apply Rawlsian principles of fairness to sMAP and produce a provisional just theory of measurement that would guide the use of ML for achieving fairness in the case of child abuse in Allegheny County.

Despite recent interest in both the critical and machine learning literature on "bias" in artificial intelligence (AI) systems, the nature of specific biases stemming from the interaction of machines, humans, and data remains ambiguous. Influenced by Gendler's work on human cognitive biases, we introduce the concept of alief-discordant belief, the tension between the intuitive moral dispositions of designers and the explicit representations generated by algorithms. Our discussion of alief-discordant belief diagnoses the ethical concerns that arise when designing AI systems atop human biases. We furthermore codify the relationship between data, algorithms, and engineers as components of this cognitive discordance, comprising a novel epistemic framework for ethics in AI.