Scholarship

Recent Work

AIPP meets weekly throughout the year to workshop scholarship at various stages of development. We highlight some of our recent, publically-available work below.

Making the Unaccountable Internet: The Changing Meaning of Accounting in the Design of the Early ARPANET

Contemporary concerns over the governance of technological systems often run up against compelling narratives about technical (in)feasibility of designing mechanisms for accountability. While in recent FAccT literature these concerns have been deliberated predominantly in relation to machine learning, other instances in the history of computing also presented circumstances in which computer scientists needed to un-muddle what it means to design (un)accountable systems. One such a compelling narrative can frequently be found in canonical histories of the Internet that highlight how its original designers’ commitment to the “End-to-End” architectural principle precluded other features from being implemented, resulting in the fast-growing, generative, but ultimately unaccountable network we have today. This paper offers a critique of such technologically essentialist notions of accountability and the characterization of the “unaccountable Internet” as an unintended consequence. We explore the changing meaning of accounting and its relationship to accountability in a selected corpus of requests for comments (RFCs) concerning the early Internet’s design from the 1970s and 80s. We characterize 4 phases of conceptualizing accounting: as billing, as measurement, as management, and as policy, and demonstrate how an understanding of accountability was constituted through these shifting meanings. Recovering this history is not only important for understanding the processes that shaped the Internet, but also serves as a starting point for unpacking the complicated political choices that are involved in designing accountability mechanisms for other technological systems today.

A. Feder Cooper and Gili Vidan

Preprint

Allocating Opportunities in a Dynamic Model of Intergenerational Mobility

Best CS Paper Award

Opportunities such as higher education can promote intergenerational mobility, leading individuals to achieve levels of socioeconomic status above that of their parents. We develop a dynamic model for allocating such opportunities in a society that exhibits bottlenecks in mobility; the problem of optimal allocation reflects a trade-off between the benefits conferred by the opportunities in the current generation and the potential to elevate the socioeconomic status of recipients, shaping the composition of future generations in ways that can benefit further from the opportunities. We show how optimal allocations in our model arise as solutions to continuous optimization problems over multiple generations, and we find in general that these optimal solutions can favor recipients of low socioeconomic status over slightly higher-performing individuals of high socioeconomic status — a form of socioeconomic affirmative action that the society in our model discovers in the pursuit of purely payoff-maximizing goals. We characterize how the structure of the model can lead to either temporary or persistent affirmative action, and we consider extensions of the model with more complex processes modulating the movement between different levels of socioeconomic status.

Hoda Heidari and Jon Kleinberg

Proc. 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT 2021)

Using a Cross-Task Grid of Linear Probes to Interpret CNN Model Predictions On Retinal Images

We analyze a dataset of retinal images using linear probes: linear regression models trained on some “target” task, using embeddings from a deep convolutional (CNN) model trained on some “source” task as input. We use this method across all possible pairings of 93 tasks in the UK Biobank dataset of retinal images, leading to ~164k different models. We analyze the performance of these linear probes by source and target task and by layer depth. We observe that representations from the middle layers of the network are more generalizable. We find that some target tasks are easily predicted irrespective of the source task, and that some other target tasks are more accurately predicted from correlated source tasks than from embeddings trained on the same task.

Katy Blumer, Subhashini Venugopalan, Michael P. Brenner, and Jon Kleinberg

Preprint

Optimizing the order of actions in contact tracing

Contact tracing is a key tool for managing epidemic diseases like HIV, tuberculosis, and COVID-19. Manual investigations by human contact tracers remain a dominant way in which this is carried out. This process is limited by the number of contact tracers available, who are often overburdened during an outbreak or epidemic. As a result, a crucial decision in any contact tracing strategy is, given a set of contacts, which person should a tracer trace next? In this work, we develop a formal model that articulates these questions and provides a framework for comparing contact tracing strategies. Through analyzing our model, we give provably optimal prioritization policies via a clean connection to a tool from operations research called a “branching bandit”. Examining these policies gives qualitative insight into trade-offs in contact tracing applications.

Michela Meister and Jon Kleinberg

Preprint

Algorithmic Monoculture and Social Welfare

As algorithms are increasingly applied to screen applicants for high-stakes decisions in employment, lending, and other domains, concerns have been raised about the effects of algorithmic monoculture, in which many decision-makers all rely on the same algorithm. This concern invokes analogies to agriculture, where a monocultural system runs the risk of severe harm from unexpected shocks. Here we show that the dangers of algorithmic monoculture run much deeper, in that monocultural convergence on a single algorithm by a group of decision-making agents, even when the algorithm is more accurate for any one agent in isolation, can reduce the overall quality of the decisions being made by the full collection of agents. Unexpected shocks are therefore not needed to expose the risks of monoculture; it can hurt accuracy even under “normal” operations, and even for algorithms that are more accurate when used by only a single decision-maker. Our results rely on minimal assumptions, and involve the development of a probabilistic framework for analyzing systems that use multiple noisy estimates of a set of alternatives.

Jon Kleinberg and Manish Raghavan

Proc. National Academy of Sciences (PNAS 2021)

On Modeling Human Perceptions of Allocation Policies with Uncertain Outcomes

Best Paper Award

Many policies allocate harms or benefits that are uncertain in nature: they produce distributions over the population in which individuals have different probabilities of incurring harm or benefit. Comparing different policies thus involves a comparison of their corresponding probability distributions, and we observe that in many instances the policies selected in practice are hard to explain by preferences based only on the expected value of the total harm or benefit they produce. In cases where the expected value analysis is not a sufficient explanatory framework, what would be a reasonable model for societal preferences over these distributions? Here we investigate explanations based on the framework of probability weighting from the behavioral sciences, which over several decades has identified systematic biases in how people perceive probabilities. We show that probability weighting can be used to make predictions about preferences over probabilistic distributions of harm and benefit that function quite differently from expected-value analysis, and in a number of cases provide potential explanations for policy preferences that appear hard to motivate by other means. In particular, we identify optimal policies for minimizing perceived total harm and maximizing perceived total benefit that take the distorting effects of probability weighting into account, and we discuss a number of real-world policies that resemble such allocational strategies. Our analysis does not provide specific recommendations for policy choices, but is instead fundamentally interpretive in nature, seeking to describe observed phenomena in policy choices.

Hoda Heidari, Solon Barocas, Jon Kleinberg, and Karen Levy

Proc. 2021 ACM Conference on Economics and Computation (EC 2021)

 

Complete List

2021


Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs

Solon Barocas, Anhong Guo, Ece Kamar, Jacquelyn Krones, Meredith Ringel Morris, Jennifer Wortman Vaughan, Duncan Wadsworth, and Hanna Wallach
Proc. the 2021 AAAI/ACM Conference on AI, Ethics, and Society (AIES 2021)


Representativeness in Statistics, Politics, and Machine Learning

Kyla Chasalow and Karen Levy
Proc. 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT 2021)


Hyperparameter Optimization Is Deceiving Us, and How to Stop It

A. Feder Cooper, Yucheng Lu, Jessica Zosa Forde, and Chris De Sa
Advances in Neural Information Processing Systems 34 (NeurIPS 2021)


Accuracy-Efficiency Trade-Offs and Accountability in Distributed ML Systems

Oral Presentation

A. Feder Cooper, Karen Levy, and Chris De Sa
Proc. 2021 ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO 2021)


Emergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Research

Oral Presentation

A. Feder Cooper and Ellen Abrams
Proc. 2021 AAAI/ACM Conference on AI, Ethics, and Society (AIES 2021)


Model Selection’s Disparate Impact in Real-World Deep Learning Applications

Contributed Talk

Jessica Zosa Forde*, A. Feder Cooper*, Kweku Kwegyir-Aggrey, Christopher De Sa, and Michael Littman
Workshop on the Science and Engineering of Deep Learning at ICLR 2021 (SEDL@ICLR 2021)


Making the Unaccountable Internet: The Changing Meaning of Accounting in the Design of the Early ARPANET

A. Feder Cooper and Gili Vidan
Preprint


Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning

A. Feder Cooper, Benjamin Laufer, Emanuel Moss, and Helen Nissenbaum
Preprint


A National Program for Building Artificial Intelligence within Communities

Fernando A. Delgado
Federation of American Scientists (2021)


Sociotechnical Design in Legal Algorithmic Decision-Making

Fernando A. Delgado
Companion Publication of the ACM 2020 Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2020 Companion)


Articulating a Community-Centered Research Agenda for AI Innovation Policy

Fernando A. Delgado and Karen Levy
Cornell Policy Review (2021)


Optimality and Stability in Federated Learning: A Game-theoretic Approach

Kate Donahue and Jon Kleinberg
Advances in Neural Information Processing Systems 34 (NeurIPS 2021)


Better Together?: How Externalities of Size Complicate Notions of Solidarity and Actuarial Fairness

Kate Donahue and Solon Barocas
Proc. 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT 2021)


Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation

Kate Donahue and Jon Kleinberg
Proc. 35th AAAI Conference on Artificial Intelligence (AAAI 2021)


Computer Vision and Conflicting Values: Describing People with Automated Alt Text

Margot Hanley, Solon Barocas, Karen Levy, Shiri Azenkot, and Helen Nissenbaum
Proc. 2021 AAAI/ACM Conference on AI, Ethics, and Society (AIES 2021)


Allocating Opportunities in a Dynamic Model of Intergenerational Mobility

Best CS Paper Award

Hoda Heidari and Jon Kleinberg
Proc. 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT 2021)


On Modeling Human Perceptions of Allocation Policies with Uncertain Outcomes

Best Paper Award

Hoda Heidari, Solon Barocas, Jon Kleinberg, and Karen Levy
Proc. 2021 ACM Conference on Economics and Computation (EC 2021)


Was “science” on the ballot?

Stephen Hilgartner, J. Benjamin Hurlbut, and Sheila Jasanoff
Science, Vol. 371, Issue 6532 (2021)


Worlds Apart: Technology, Remote Work, and Equity

Aspen Russell and Eitan Frachtenberg
Computer (2021)


Random Graphs with Prescribed K-Core Sequences: A New Null Model for Network Analysis

Katherine Van Koevering, Austin R. Benson, and Jon Kleinberg
Proc. Web Conference 2021 (WWW 2021)


Algorithmic Auditing and Social Justice: Lessons from the History of Audit Studies

Oral Presentation

Briana Vecchione, Solon Barocas, and Karen Levy
Proc. 2021 ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO 2021)


Privacy Dependencies

Solon Barocas and Karen Levy
95 Wash. L. Rev. 555 (2020)


Americans’ perceptions of privacy and surveillance in the COVID-19 pandemic

Baobao Zhang, Sarah Kreps, Nina McMurry, and R. Miles McCain
PLOS ONE 15(12) (2020)


2020


Privacy Dependencies

Solon Barocas and Karen Levy
95 Wash. L. Rev. 555 (2020)


Americans’ perceptions of privacy and surveillance in the COVID-19 pandemic

Baobao Zhang, Sarah Kreps, Nina McMurry, and R. Miles McCain
PLOS ONE 15(12) (2020)