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This paper addresses the challenges of computational accountability in autonomous systems, particularly in Autonomous Vehicles (AVs), where safety and efficiency often conflict. We begin by examining current approaches such as cost minimization, reward maximization, human-centered approaches, and ethical frameworks, noting their limitations addressing these challenges. Foreseeability is a central concept in tort law that limits the accountability and legal liability of an actor to a reasonable scope. Yet, current data-driven methods to determine foreseeability are rigid, ignore uncertainty, and depend on simulation data. In this work, we advocate for a new computational approach to establish foreseeability of autonomous systems based on the legal “BPL” formula. We provide open research challenges, using fully autonomous vehicles as a motivating example, and call for researchers to help autonomous systems make accountable decisions in safety-critical scenarios.
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Accountability in the Blue-Collar Data-Driven Workplace
This paper examines how mobile technology impacts employee accountability in the blue-collar data-driven workplace. We conducted an observation-based qualitative study of how electricians in an electrical company interact with data related to their work accountability, which comprises the information employees feel is reasonable to share and document about their work. The electricians we studied capture data both manually, recording the hours spent on a particular task, and automatically, as their mobile devices regularly track data such as location. First, our results demonstrate how work accountability manifests for employees' manual labor work that has become data-driven. We show how employees work through moments of transparency, privacy, and accountability using data focused on location, identification and time. Second, we demonstrate how this data production is interdependent with employees' beliefs about what is a reasonable level of detail and transparency to provide about their work. Lastly, we articulate specific design implications related to work accountability.
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- Award ID(s):
- 1718121
- PAR ID:
- 10067179
- Date Published:
- Journal Name:
- CHI '18 Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
- Page Range / eLocation ID:
- 1 to 12
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This paper addresses the challenges of computational accountability in autonomous systems, particularly in Autonomous Vehicles (AVs), where safety and efficiency often conflict. We begin by examining current approaches such as cost minimization, reward maximization, human-centered approaches, and ethical frameworks, noting their limitations addressing these challenges. Foreseeability is a central concept in tort law that limits the accountability and legal liability of an actor to a reasonable scope. Yet, current data-driven methods to determine foreseeability are rigid, ignore uncertainty, and depend on simulation data. In this work, we advocate for a new computational approach to establish foreseeability of autonomous systems based on the legal “BPL” formula. We provide open research challenges, using fully autonomous vehicles as a motivating example, and call for researchers to help autonomous systems make accountable decisions in safety-critical scenarios.more » « less
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Identifying anomalous human spatial trajectory patterns can indicate dynamic changes in mobility behavior with applications in domains like infectious disease monitoring and elderly care. Recent advancements in large language models (LLMs) have demonstrated their ability to reason in a manner akin to humans. This presents significant potential for analyzing temporal patterns in human mobility. In this paper, we conduct empirical studies to assess the capabilities of leading LLMs like GPT-4 and Claude-2 in detecting anomalous behaviors from mobility data, by comparing to specialized methods. Our key findings demonstrate that LLMs can attain reasonable anomaly detection performance even without any specific cues. In addition, providing contextual clues about potential irregularities could further enhances their prediction efficacy. Moreover, LLMs can provide reasonable explanations for their judgments, thereby improving transparency. Our work provides insights on the strengths and limitations of LLMs for human spatial trajectory analysis.more » « less
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The construction industry has been a predominantly White/Caucasian Men community with a very low representation of women and people from traditionally marginalized backgrounds. Even though companies have been implementing Diversity, Equity, and Inclusion (DEI) statements for many years, we still believe it is neither a diverse nor equitable field. To better understand how DEI statements declared by companies have been understood and recognized by employees, a survey was deployed nationwide to understand how professionals in the construction industry perceive their organization's DEI statements or policies. A complete data set was built from 249 participants. 75% identified themselves as men and 25% as women, and nobody identified with other gender identities. More than 80% of participants were White/Caucasian, 4% Black or African American, 4% Hispanic or Latinx, and 6% Asian. Participants are currently working in small (24%), medium (30%), and large (46%) construction and design companies located across The United States. Regarding the number of employees, companies are small, less than 99 employees; medium, between 100 and 499 employees; and large, more than 500 employees. Also, companies were grouped into four main types, building construction companies (67%), transportation construction companies (6%), special trade contractor companies (17%), and design companies (10%). For more than 65% of professionals in the construction industry who participated in this study, DEI was mainly related to proper representation of women and minoritized populations in the workforce; Merit-based transparent recruitment and promotion; equality, social justice, and nondiscrimination policy statement; and equitable payment and compensation. Other factors such as proper representation of women and minoritized populations at the top management level and payment structure transparency did not emerge from the results. We also found that 70% of professionals identified DEI statements in their companies and 30% of professionals did not identify or did not know about DEI statements. Looking at the company size, 85% of professionals in large companies identified DEI statements in their companies, but 71% and 42% of professionals in medium and small companies identified DEI statements in their companies, respectively. According to the company type, more than 80% of professionals working in design companies recognized DEI statements in their companies, but around 60% in construction and special trade companies. We can highlight that large companies have established policies and practices that result in better socialization and recognition of their DEI statements than medium and small companies. Also, construction and special trade companies need to strengthen their DEI statements and increase the representation of women and people from traditionally marginalized backgrounds. Results from this research give an idea about the current state of DEI in the construction industry and would contribute to the current effort to increase the diversity of the nation's construction workforce.more » « less
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Recently, Meta has shifted towards AI-mediated ad targeting mechanisms that do not require advertisers to provide detailed targeting criteria. The shift is likely driven by excitement over AI capabilities as well as the need to address new data privacy policies and targeting changes agreed upon in civil rights settlements. At the same time, in response to growing public concern about the harms of targeted advertising, Meta has touted their ad preference controls as an effective mechanism for users to exert control over the advertising they see. Furthermore, Meta markets their Why this ad targeting explanation as a transparency tool that allows users to understand the reasons for seeing particular ads and inform their actions to control what ads they see in the future. Our study evaluates the effectiveness of Meta's See less ad control, as well as the actionability of ad targeting explanations following the shift to AI-mediated targeting. We conduct a large-scale study, randomly assigning participants the intervention of marking See less to either Body Weight Control or Parenting topics, and collecting the ads Meta shows to participants and their targeting explanations before and after the intervention. We find that utilizing the See less ad control for the topics we study does not significantly reduce the number of ads shown by Meta on these topics, and that the control is less effective for some users whose demographics are correlated with the topic. Furthermore, we find that the majority of ad targeting explanations for local ads made no reference to location-specific targeting criteria, and did not inform users why ads related to the topics they requested to See less of continued to be delivered. We hypothesize that the poor effectiveness of controls and lack of actionability and comprehensiveness in explanations are the result of the shift to AI-mediated targeting, for which explainability and transparency tools have not yet been developed by Meta. Our work thus provides evidence for the need of new methods for transparency and user control, suitable and reflective of how the increasingly complex and AI-mediated ad delivery systems operate.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3173574.3173906
