Toward algorithmic transparency and accountability

Garfinkel, Simson L.; Matthews, Jeanna; Shapiro, Stuart; Smith, Jonathan M.

doi:10.1145/3125780

Cited by 70 publications

(50 citation statements)

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“…Fairness and transparency Alternatively, self-disclosure can be encouraged when firms provide assurances of algorithmic fairness and transparency (Garfinkel et al, 2017). In terms of fairness, audits and certifications provide assurances that the firm's CAs are fair and unlikely to generate biased interactions towards particular subgroups, such as customers of certain race, gender, or socioeconomic status.…”

Section: Encouraging Voluntary Self-disclosure With Conversational Agmentioning

confidence: 99%

Learning from the Dark Web: leveraging conversational agents in the era of hyper-privacy to enhance marketing

Thomaz

Salge

Karahanna

et al. 2019

J. of the Acad. Mark. Sci.

127

105

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The Web is a constantly evolving, complex system, with important implications for both marketers and consumers. In this paper, we contend that over the next five to ten years society will see a shift in the nature of the Web, as consumers, firms and regulators become increasingly concerned about privacy. In particular, we predict that, as a result of this privacy-focus, various information sharing and protection practices currently found on the Dark Web will be increasingly adapted in the overall Web, and in the process, firms will lose much of their ability to fuel a modern marketing machinery that relies on abundant, rich, and timely consumer data. In this type of controlled information-sharing environment, we foresee the emersion of two distinct types of consumers: (1) those generally willing to share their information with marketers (Buffs), and (2) those who generally deny access to their personal information (Ghosts). We argue that one way marketers can navigate this new environment is by effectively designing and deploying conversational agents (CAs), often referred to as "chatbots." In particular, we propose that CAs may be used to understand and engage both types of consumers, while providing personalization, and serving both as a form of differentiation and as an important strategic asset for the firm-one capable of eliciting self-disclosure of otherwise private consumer information.

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Section: Encouraging Voluntary Self-disclosure With Conversational Agmentioning

confidence: 99%

Learning from the Dark Web: leveraging conversational agents in the era of hyper-privacy to enhance marketing

Thomaz

Salge

Karahanna

et al. 2019

J. of the Acad. Mark. Sci.

127

105

View full text Add to dashboard Cite

show abstract

“…ACM [39] introduces a set of principles intended to ensure fairness in the evolving policy and technology ecosystem: awareness, access and redness, accountability, explanation, data provenance, auditability, and validation and testing. We particularly focus on awareness, explanation and auditability, as we do not rely on the Facebook API to collect impressions.…”

Section: Fairness Accountability and Transparencymentioning

confidence: 99%

Fairness in online social network timelines: Measurements, models and mechanism design

Hargreaves

Agosti

Menasché

et al. 2019

Performance Evaluation

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Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithm lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements, an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. Measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility and occupancy bias incurred by users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition, we find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.

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“…For example, identification of the corpus-the collection of data points from which an AI system learns and bases its decisions-as the source of bias, as in the case of natural language processing that uses only one dialect of English, is similar to the role that literary warrant-or the decision to base classification decisions on the extant collection of literature-plays in the bias of systems such as the Library of Congress Subject Headings toward the historical (often white and male) canon of American literature (Olson, 2000). The way in which knowledge organization scholars have taken these ubiquitous examples of bias to move toward transparency and "responsible bias" (Feinberg, 2007) is a possible path forward for machine learning, consistent with the recent critiques within the field (Garfinkel, Matthews, Shapiro, & Smith, 2017).…”

Section: Biasmentioning

confidence: 89%

Machine Knowledge: Information Studies and Artificial Intelligence in Dialogue

Bullard¹,

Abdul-Mageed²

2018

CAIS/ACSI

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In this piece, we examine the parallels and contrasts between knowledge organization and data science with a focus on the subfields of AI and machine learning in particular. Our analysis aims to both highlight and aid making connections between information studies and artificial intelligence through the discussion of shared practical and theoretical considerations and concerns around simplification and transparency.

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Toward algorithmic transparency and accountability

Cited by 70 publications

References 0 publications

Learning from the Dark Web: leveraging conversational agents in the era of hyper-privacy to enhance marketing

Learning from the Dark Web: leveraging conversational agents in the era of hyper-privacy to enhance marketing

Fairness in online social network timelines: Measurements, models and mechanism design

Machine Knowledge: Information Studies and Artificial Intelligence in Dialogue

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