July 2021
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113 Reads
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21 Citations
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Publications (6)
July 2021
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288 Reads
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90 Citations
SSRN Electronic Journal
This report maps the challenges of constructing algorithmic impact assessments (AIAs) by analyzing impact assessments in other domains—from the environment to human rights to privacy. Impact assessment is a promising model of algorithmic governance because it bundles an account of potential and actual harms of a system with a means for identifying who is responsible for their remedy. Success in governing with AIAs requires thoughtful engagement with the ongoing exercise of social and political power, rather than defaulting to self-assessment and narrow technical metrics. Without such engagement, AIAs run the risk of not adequately facilitating the measurement of, and contestation over, harms experienced by people, communities, and society.
July 2021
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65 Reads
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25 Citations
Big Data & Society
Frequent public uproar over forms of data science that rely on information about people demonstrates the challenges of defining and demonstrating trustworthy digital data research practices. This paper reviews problems of trustworthiness in what we term pervasive data research: scholarship that relies on the rich information generated about people through digital interaction. We highlight the entwined problems of participant unawareness of such research and the relationship of pervasive data research to corporate datafication and surveillance. We suggest a way forward by drawing from the history of a different methodological approach in which researchers have struggled with trustworthy practice: ethnography. To grapple with the colonial legacy of their methods, ethnographers have developed analytic lenses and researcher practices that foreground relations of awareness and power. These lenses are inspiring but also challenging for pervasive data research, given the flattening of contexts inherent in digital data collection. We propose ways that pervasive data researchers can incorporate reflection on awareness and power within their research to support the development of trustworthy data science.
March 2021
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446 Reads
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198 Citations
October 2020
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45 Reads
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21 Citations
Patterns
The COVID-19 pandemic has, in a matter of a few short months, drastically reshaped society around the world. Because of the growing perception of machine learning as a technology capable of addressing large problems at scale, machine learning applications have been seen as desirable interventions in mitigating the risks of the pandemic disease. However, machine learning, like many tools of technocratic governance, is deeply implicated in the social production and distribution of risk and the role of machine learning in the production of risk must be considered as engineers and other technologists develop tools for the current crisis. This paper describes the coupling of machine learning and the social production of risk, generally, and in pandemic responses specifically. It goes on to describe the role of risk management in the effort to institutionalize ethics in the technology industry and how such efforts can benefit from a deeper understanding of the social production of risk through machine learning.
January 2020
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443 Reads
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15 Citations
SSRN Electronic Journal
Citations (6)
... These principles and policies have been quite effective at protecting individual research subjects, but they were never designed to address the impact of research on broader online communities-those who are not the direct subjects of an investigation, but who may be negatively affected by that research. In the area of computer and information science, there are growing concerns that the Belmont Report and overview of ethics review boards are insufficient to manage the growing complexity of pervasive data research [88], online community research [53,77], and gaps in coverage with documents such as the Belmont Report [94,95]. A number of prior research threads have pointed to this substantial gap (e.g., [60]) and it has been a recurring topic of discussion at workshops and community meetings on online ethics (i.e., the Aspen Institute [4], Future of Privacy Forum [2], and Ada Lovelace Institute [6]). ...
- Citing Article
- Full-text available
July 2021
Big Data & Society
... Several authors express concerns about the impact of AI on fundamental rights [4], [5], [7], [8], [9], [10], [11], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26]. Kumar et al [27] and Hipólito et al [28] propose four key vectors for AI judicial systems: explainability, fairness, transparency, and auditability, which are absent in the information on "Justo". ...
- Citing Conference Paper
- Full-text available
July 2021
... Similar observations were made on smart cities wherein citizens are either represented by professional and bureaucratic elites, or are entirely absent from key decision making [97,115]. Concentrating decision-making power beyond democratic oversight risks alienating the public and eroding trust in AI and its governance [5,60,70]. ...
- Citing Technical Report
- Full-text available
July 2021
SSRN Electronic Journal
... Artificial intelligence (AI) risk management has emerged as a critical area of focus for policymakers, developers, and users of this technology. Driven by a rapid rate of adoption and an increase in the public awareness of potential risks associated with artificial intelligence, this focus presents a challenge to policymakers and industry actors given the current lack of standard risk management frameworks (Ezeani et al., 2021;Metcalf et al., 2021). Business managers within the industry must decide whether the benefits of using an AI system outweigh the potential risks. ...
- Citing Conference Paper
- Full-text available
March 2021
... The proliferation of global risks also generates a survival instinct that can lead to short-term, individualistic positions rather than strategies that consider long-term public good (Beck 2006). Beck's formulation of modernity and risk has been used to reflect on the COVID-19 pandemic from a range of perspectives, including the improvements that could be made to science communication after the pandemic (Pietrocola et al. 2021), post-pandemic climate change policies (Cooper, Heath, and Nagel 2022), governmental responses to the pandemic (Giritli Nygren and Olofsson, 2020), ageism during the pandemic (Cook et al. 2021), and the pandemic and machine learning (Moss and Metcalf 2020). ...
- Citing Article
- Full-text available
October 2020
Patterns
... Many public institutions lack the necessary infrastructure and technical expertise to develop and maintain ML systems [38]. Additionally, legal and regulatory frameworks governing the use of AI in policy are still evolving, which can create uncertainty and hinder adoption [39]. Overcoming these challenges requires collaboration across multiple disciplines, including data science, policy-making, and legal sectors, to ensure that ML can be used responsibly and effectively in shaping public policy. ...
- Citing Article
- Full-text available
January 2020
SSRN Electronic Journal