Transparency and Control in Platforms for Networked Markets

In this work, we analyze the worst case efficiency loss of online platform designs under a networked Cournot competition model. Inspired by some of the largest platforms today, the platform designs considered tradeoffs between transparency and control, namely, (i) open access, (ii) controlled allocation and (iii) discriminatory access. Our results show that open access designs incentivize increased production towards perfectly competitive levels and limit efficiency loss, while controlled allocation designs lead to producer-platform incentive misalignment, resulting in low participation and unbounded efficiency loss. We also show that discriminatory access designs seek a balance between transparency and control, and achieve the best of both worlds, maintaining high participation rates while limiting efficiency loss. We also study a model of consumer search cost which further distinguishes between the three designs

In and of the body

This practice traces information as it moves through physical and digital spaces, asking questions surrounding how technology alters meaning as it makes interpretations. Led by my own personal interests and the memetic bodies of popular culture I was embedded, questions arise surrounding how individuals communicate with and experience the networks they are embedded within. Furthermore, this practice expands to investigate what happens when the body becomes a technological interface, and how issues of ownership affect our interactions

Cooperative AI via Decentralized Commitment Devices

Credible commitment devices have been a popular approach for robust multi-agent coordination. However, existing commitment mechanisms face limitations like privacy, integrity, and susceptibility to mediator or user strategic behavior. It is unclear if the cooperative AI techniques we study are robust to real-world incentives and attack vectors. However, decentralized commitment devices that utilize cryptography have been deployed in the wild, and numerous studies have shown their ability to coordinate algorithmic agents facing adversarial opponents with significant economic incentives, currently in the order of several million to billions of dollars. In this paper, we use examples in the decentralization and, in particular, Maximal Extractable Value (MEV) (arXiv:1904.05234) literature to illustrate the potential security issues in cooperative AI. We call for expanded research into decentralized commitments to advance cooperative AI capabilities for secure coordination in open environments and empirical testing frameworks to evaluate multi-agent coordination ability given real-world commitment constraints.Comment: NeurIPS 2023- Multi-Agent Security Worksho

For What It's Worth: Humans Overwrite Their Economic Self-interest to Avoid Bargaining With AI Systems

As algorithms are increasingly augmenting and substituting human decision-making, understanding how the introduction of computational agents changes the fundamentals of human behavior becomes vital. This pertains to not only users, but also those parties who face the consequences of an algorithmic decision. In a controlled experiment with 480 participants, we exploit an extended version of two-player ultimatum bargaining where responders choose to bargain with either another human, another human with an AI decision aid or an autonomous AI-system acting on behalf of a passive human proposer. Our results show strong responder preferences against the algorithm, as most responders opt for a human opponent and demand higher compensation to reach a contract with autonomous agents. To map these preferences to economic expectations, we elicit incentivized subject beliefs about their opponent's behavior. The majority of responders maximize their expected value when this is line with approaching the human proposer. In contrast, responders predicting income maximization for the autonomous AI-system overwhelmingly override economic self-interest to avoid the algorithm

Transparency and Control in Platforms for Networked Markets

In this work, we analyze the worst case efficiency loss of online platform designs under a networked Cournot competition model. Inspired by some of the largest platforms today, the platform designs considered tradeoffs between transparency and control, namely, (i) open access, (ii) controlled allocation and (iii) discriminatory access. Our results show that open access designs incentivize increased production towards perfectly competitive levels and limit efficiency loss, while controlled allocation designs lead to producer-platform incentive misalignment, resulting in low participation and unbounded efficiency loss. We also show that discriminatory access designs seek a balance between transparency and control, and achieve the best of both worlds, maintaining high participation rates while limiting efficiency loss. We also study a model of consumer search cost which further distinguishes between the three designs

Replication-Robust Payoff-Allocation with Applications in Machine Learning Marketplaces

The ever-increasing take-up of machine learning techniques requires ever-more application-specific training data. Manually collecting such training data is a tedious and time-consuming process. Data marketplaces represent a compelling alternative, providing an easy way for acquiring data from potential data providers. A key component of such marketplaces is the compensation mechanism for data providers. Classic payoff-allocation methods such as the Shapley value can be vulnerable to data-replication attacks, and are infeasible to compute in the absence of efficient approximation algorithms. To address these challenges, we present an extensive theoretical study on the vulnerabilities of game theoretic payoff-allocation schemes to replication attacks. Our insights apply to a wide range of payoff-allocation schemes, and enable the design of customised replication-robust payoff-allocations. Furthermore, we present a novel efficient sampling algorithm for approximating payoff-allocation schemes based on marginal contributions. In our experiments, we validate the replication-robustness of classic payoff-allocation schemes and new payoff-allocation schemes derived from our theoretical insights. We also demonstrate the efficiency of our proposed sampling algorithm on a wide range of machine learning tasks

News devices : how digital objects participate in news work and research

News work is increasingly taking place in and through a variety of intersecting digital devices, from websites, to search engines, online platforms, apps, bots, web analytics, data analysis and visualisation tools. These devices are also increasingly used as resources in digital research, and their implications are yet to be fully understood. This thesis examines how digital objects participate in news work and research. To this end, I propose an orientation towards the news device as a research topic and approach. The news device approach calls attention to the ways in which practices and relations are co-produced with digital objects involved in news work. It also attends to how such digital devices may afford modes of studying these practices. To make the case for this approach, I examine the participation of three types of devices in three aspects of news work: (1) the role of the network graph in journalistic storytelling, (2) the role of the online platform in journalism coding, and (3) the role of the web tracker in news audience commodification. In all, the thesis contributes to understanding the digital transformations of news in two ways. First, it develops a rich, nuanced, multidisciplinary, collaborative and reflexive approach to news research with digital methods. Secondly, it provides novel insights into how digital devices shape both news processes and relations with the online advertising and marketing industries, commercial online platforms, digital visual culture, and other digital content producers

The social turn of artificial intelligence

Social machines are systems formed by material and human elements interacting in a structured way. The use of digital platforms as mediators allows large numbers of humans to participate in such machines, which have interconnected AI and human components operating as a single system capable of highly sophisticated behavior. Under certain conditions, such systems can be understood as autonomous goal-driven agents. Many popular online platforms can be regarded as instances of this class of agent. We argue that autonomous social machines provide a new paradigm for the design of intelligent systems, marking a new phase in AI. After describing the characteristics of goal-driven social machines, we discuss the consequences of their adoption, for the practice of artificial intelligence as well as for its regulation