Artificial Intelligence Bias and Discrimination: Will We Pull the Arc of the Moral Universe Towards Justice?

Curated on Posted on by Stefaan Verhulst

Paper by Emile Loza de Siles: “In 1968, the Reverend Martin Luther King Jr. foresaw the inevitability of society’s eventual triumph over the deep racism of his time and the stain that continues to cast its destructive oppressive pall today. From the pulpit of the nation’s church, Dr King said, “We shall overcome because the arc of the moral universe is long but it bends toward justice”. More than 40 years later, Eric Holder, the first African American United States Attorney General, agreed, but only if people acting with conviction exert to pull that arc towards justice.

With artificial intelligence (AI) bias and discrimination rampant, the need to pull the moral arc towards algorithmic justice is urgent. This article offers empowering clarity by conceptually bifurcating AI bias problems into AI bias engineering and organisational AI governance problems, revealing proven legal development pathways to protect against the corrosive harms of AI bias and discrimination…(More)”.

Crowdsourcing research questions in science

Curated on Posted on by Stefaan Verhulst

Paper by Susanne Beck, Tiare-Maria Brasseur, Marion Poetz and Henry Sauermann: “Scientists are increasingly crossing the boundaries of the professional system by involving the general public (the crowd) directly in their research. However, this crowd involvement tends to be confined to empirical work and it is not clear whether and how crowds can also be involved in conceptual stages such as formulating the questions that research is trying to address. Drawing on five different “paradigms” of crowdsourcing and related mechanisms, we first discuss potential merits of involving crowds in the formulation of research questions (RQs). We then analyze data from two crowdsourcing projects in the medical sciences to describe key features of RQs generated by crowd members and compare the quality of crowd contributions to that of RQs generated in the conventional scientific process. We find that the majority of crowd contributions are problem restatements that can be useful to assess problem importance but provide little guidance regarding potential causes or solutions. At the same time, crowd-generated research questions frequently cross disciplinary boundaries by combining elements from different fields within and especially outside medicine. Using evaluations by professional scientists, we find that the average crowd contribution has lower novelty and potential scientific impact than professional research questions, but comparable practical impact. Crowd contributions outperform professional RQs once we apply selection mechanisms at the level of individual contributors or across contributors. Our findings advance research on crowd and citizen science, crowdsourcing and distributed knowledge production, as well as the organization of science. We also inform ongoing policy debates around the involvement of citizens in research in general, and agenda setting in particular.Author links open overlay panel…(More)”.

Citizen science and the right to research: building local knowledge of climate change impacts

Curated on Posted on by Stefaan Verhulst

Paper by Sarita Albagli & Allan Yu Iwama: “The article presents results of a research project aiming to develop theoretical and empirical contributions on participatory approaches and methods of citizen science for risk mapping and adaptation to climate change. In the first part, the paper presents a review of the literature on key concepts and perspectives related to participatory citizen science, introducing the concept of the “right to research”. It highlights the mutual fertilization with participatory mapping methods to deal with disaster situations associated to climate change. In the second part, the paper describes and presents the results and conclusions of an action-research developed on the coastline between the states of São Paulo and Rio de Janeiro, Brazil in 2017–2018. It involved affected communities as protagonists in mapping and managing risks of natural disasters caused by extreme climate events, by combining citizen science approaches and methods with Participatory Geographic Information Systems (PGIS) and social cartography. The article concludes by pointing out the contributions and limits of the “right to research” as a relevant Social Science approach to reframe citizen science from a democratic view….(More)”.

When Do Informational Interventions Work? Experimental Evidence from New York City High School Choice

Curated on Posted on by Stefaan Verhulst

Paper by Sarah Cohodes, Sean Corcoran, Jennifer Jennings & Carolyn Sattin-Bajaj: “This paper reports the results of a large, school-level randomized controlled trial evaluating a set of three informational interventions for young people choosing high schools in 473 middle schools, serving over 115,000 8th graders. The interventions differed in their level of customization to the student and their mode of delivery (paper or online); all treated schools received identical materials to scaffold the decision-making process. Every intervention reduced likelihood of application to and enrollment in schools with graduation rates below the city median (75 percent). An important channel is their effect on reducing nonoptimal first choice application strategies. Providing a simplified, middle-school specific list of relatively high graduation rate schools had the largest impacts, causing students to enroll in high schools with 1.5-percentage point higher graduation rates. Providing the same information online, however, did not alter students’ choices or enrollment. This appears to be due to low utilization. Online interventions with individual customization, including a recommendation tool and search engine, induced students to enroll in high schools with 1-percentage point higher graduation rates, but with more variance in impact. Together, these results show that successful informational interventions must generate engagement with the material, and this is possible through multiple channels…(More)”.

The emergence of algorithmic solidarity: unveiling mutual aid practices and resistance among Chinese delivery workers

Curated on Posted on by Stefaan Verhulst

Paper by Zizheng Yu, Emiliano Treré, and Tiziano Bonini: “This study explores how Chinese riders game the algorithm-mediated governing system of food delivery service platforms and how they mobilize WeChat to build solidarity networks to assist each other and better cope with the platform economy. We rely on 12 interviews with Chinese riders from 4 platforms (Meituan, Eleme, SF Express and Flash EX) in 5 cities, and draw on a 4-month online observation of 7 private WeChat groups. The article provides a detailed account of the gamification ranking and competition techniques employed by delivery platforms to drive the riders to achieve efficiency and productivity gains. Then, it critically explores how Chinese riders adapt and react to the algorithmic systems that govern their work by setting up private WeChat groups and developing everyday practices of resilience and resistance. This study demonstrates that Chinese riders working for food delivery platforms incessantly create a complex repertoire of tactics and develop hidden transcripts to resist the algorithmic control of digital platforms….(More)”.

What’s the problem? How crowdsourcing and text-mining may contribute to the understanding of unprecedented problems such as COVID-19

Curated on Posted on by Stefaan Verhulst

Paper by Julian Wahl, Johann Füller, and Katja Hutter: “In this research, we explore how crowdsourcing combined with text-mining can help to build a sound understanding of unstructured, complex and ill-defined problems. Therefore, we gathered 101 problem descriptions contributed to a crowdsourcing contest about the impact of COVID-19 on the tourism industry. Based on our findings we propose a five-phase process model for problem understanding consisting of: (1) information gathering, (2) information pre-structuring, (3) problem space mapping, (4) problem space exploration, and (5) problem understanding for solution search. While our study confirms that crowdsourcing and text-mining facilitate fast generation and exploration of problem spaces at limited cost, it also reveals the necessity to follow certain process steps and to deal with challenges such as information loss and human interpretation. For practitioners, our model presents a guideline for how to get a faster grasp on complex and rather unprecedented problems…(More)”.

The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence

Curated on Posted on by Stefaan Verhulst

Paper by Erik Brynjolfsson: “In 1950, Alan Turing proposed an “imitation game” as the ultimate test of whether a machine was intelligent: could a machine imitate a human so well that its answers to questions are indistinguishable from those of a human. Ever since, creating intelligence that matches human intelligence has implicitly or explicitly been the goal of thousands of researchers, engineers and entrepreneurs. The benefits of human-like artificial intelligence (HLAI) include soaring productivity, increased leisure, and perhaps most profoundly, a better understanding of our own minds.

But not all types of AI are human-like—in fact, many of the most powerful systems are very different from humans —and an excessive focus on developing and deploying HLAI can lead us into a trap. As machines become better substitutes for human labor, workers lose economic and political bargaining power and become increasingly dependent on those who control the technology. In contrast, when AI is focused on augmenting humans rather than mimicking them, then humans retain the power to insist on a share of the value created. What’s more, augmentation creates new capabilities and new products and services, ultimately generating far more value than merely human-like AI. While both types of AI can be enormously beneficial, there are currently excess incentives for automation rather than augmentation among technologists, business executives, and policymakers…(More)”

Octagon Measurement: Public Attitudes toward AI Ethics

Curated on Posted on by Stefaan Verhulst

Paper by Yuko Ikkatai, Tilman Hartwig, Naohiro Takanashi & Hiromi M. Yokoyama: “Artificial intelligence (AI) is rapidly permeating our lives, but public attitudes toward AI ethics have only partially been investigated quantitatively. In this study, we focused on eight themes commonly shared in AI guidelines: “privacy,” “accountability,” “safety and security,” “transparency and explainability,” “fairness and non-discrimination,” “human control of technology,” “professional responsibility,” and “promotion of human values.” We investigated public attitudes toward AI ethics using four scenarios in Japan. Through an online questionnaire, we found that public disagreement/agreement with using AI varied depending on the scenario. For instance, anxiety over AI ethics was high for the scenario where AI was used with weaponry. Age was significantly related to the themes across the scenarios, but gender and understanding of AI differently related depending on the themes and scenarios. While the eight themes need to be carefully explained to the participants, our Octagon measurement may be useful for understanding how people feel about the risks of the technologies, especially AI, that are rapidly permeating society and what the problems might be…(More)”.

A tale of two labs: Rethinking urban living labs for advancing citizen engagement in food system transformations

Curated on Posted on by Stefaan Verhulst

Paper by Anke Brons et al: “Citizen engagement is heralded as essential for food democracy and equality, yet the implementation of inclusive citizen engagement mechanisms in urban food systems governance has lagged behind. This paper aims to further the agenda of citizen engagement in the transformation towards healthy and sustainable urban food systems by offering a conceptual reflection on urban living labs (ULLs) as a methodological platform. Over the past decades, ULLs have become increasingly popular to actively engage citizens in methodological testbeds for innovations within real-world settings. The paper proposes that ULLs as a tool for inclusive citizen engagement can be utilized in two ways: (i) the ULL as the daily life of which citizens are the experts, aimed at uncovering the unreflexive agency of a highly diverse population in co-shaping the food system and (ii) the ULL as a break with daily life aimed at facilitating reflexive agency in (re)shaping food futures. We argue that both ULL approaches have the potential to facilitate inclusive citizen engagement in different ways by strengthening the breadth and the depth of citizen engagement respectively. The paper concludes by proposing a sequential implementation of the two types of ULL, paying attention to spatial configurations and the short-termed nature of ULLs….(More)”.

Why people believe misinformation and resist correction

Curated on Posted on by Stefaan Verhulst

TechPolicyPress: “…In Nature, a team of nine researchers from the fields of psychology, mass media & communication have published a review of available research on the factors that lead people to “form or endorse misinformed views, and the psychological barriers” to changing their minds….

The authors summarize what is known about a variety of drivers of false beliefs, noting that they “generally arise through the same mechanisms that establish accurate beliefs” and the human weakness for trusting the “gut”. For a variety of reasons, people develop shortcuts when processing information, often defaulting to conclusions rather than evaluating new information critically. A complex set of variables related to information sources, emotional factors and a variety of other cues can lead to the formation of false beliefs. And, people often share information with little focus on its veracity, but rather to accomplish other goals- from self-promotion to signaling group membership to simply sating a desire to ‘watch the world burn’.

Source: Nature Reviews: Psychology, Volume 1, January 022

Barriers to belief revision are also complex, since “the original information is not simply erased or replaced” once corrective information is introduced. There is evidence that misinformation can be “reactivated and retrieved” even after an individual receives accurate information that contradicts it. A variety of factors affect whether correct information can win out. One theory looks at how information is integrated in a person’s “memory network”. Another complementary theory looks at “selective retrieval” and is backed up by neuro-imaging evidence…(More)”.