Accountability in algorithmic decision making

By Nicholas A. Diakopoulos. 2016

594 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Information Systems, Safety Research, AI Ethics, Fairness & Society

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In Management Information Systems

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Citations in the corpus

Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling (2021, background): “Striving for transparency can help researchers and model users reason through whether their use case is appropriate and worth the risk of engaging with the model (Diakopoulos, 2016).”
Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design (2022, background): “Striving for transparency can help researchers and model users reason through whether their use case is appropriate and worth the risk of engaging with the model (Diakopoulos, 2016).”
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “In the case of inactive accounts, Gong et al. (2015, 2016) found that a signiﬁcant fraction of users, though interested in a given topic, choose to remain silent.”
Data Epistemologies, The Coloniality of Power, and Resistance (2019, background): “…data activism (Milan and Gutiérrez 2015; Milan and van der Velden 2016), algorithmic resistance (Treré 2017, 2018), data justice (Dencik et al. 2016), algorithmic accountability (Ananny and Crawford 2018; Diakopoulos 2016) explores resistance practices and challenges for activists around the globe.”
Algorithmic management in a work context (2021, background): “A driving concern throughout the implementation of algorithmic management is organizational accountability: holding algorithmically-driven decisions accountable to various stakeholders within and outside the boundaries of the organization (Diakopoulos, 2016; Mateescu and Nguyen, 2019).”
Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products (2019, background): “legal developments like the EU General Data Protection Regulation, corporations still have little incentive to disclose details about their systems [10, 15, 35].”
Towards Algorithmic Experience: Initial Efforts for Social Media Contexts (2018, background): “…guided by a semi-structured set of questions developed from Bucher’s and Rader and Gray’s categorization for algorithmic experiences in Facebook [7,23] as well as Diakopoulos invitation for algorithmic accountability [8], but gave room for users to volunteer additional perspectives and experiences.”
Trustworthy AI: From Principles to Practices (2021, background): “Note that in terms of information disclosure, transparency contributes the basic mechanism used to facilitate the accountability of an AI system [94, 100].”
Rehumanized Crowdsourcing: A Labeling Framework Addressing Bias and Ethics in Machine Learning (2019, background): “In regards to biases originating from crowd work, several prior works have looked at biases introduced by the process of labeling via crowds (e.g., [3, 5, 6, 8, 9, 23, 24, 26, 34]).”
Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry (2020, background): “Although transparency can refer to different types of disclosures around algorithmic decisions, here we focus on algorithmic presence , and hence the disclosure about whether an algorithm is involved in the decision (Diakopoulos, 2016).”
Stakeholders in Explainable AI (2018, background): “Terminology is further complicated by concerns over the accountability (Diakopoulos 2016) and fairness (O’Neil 2016) of modern AI systems which, while overlapping the issue of end-user trust, extend into ethical and legal domains.”
The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity (2019, background): “Furthermore, this lack of transparency has inspired calls in academia and practice to hold algorithms accountable (Ananny and Crawford 2018; Angwin 2016; Diakopoulos 2016; Neyland 2015).”
Outlining the Design Space of Explainable Intelligent Systems for Medical Diagnosis (2019, background): “It provides various system-centric solutions, such as developing accountable and transparent algorithms [11,43], visualizing obscure features [12,49], and employing theories from cognitive psychology to explore effective explanations [28,29,32].”
Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda (2018, background): “While research on explainable Intelligent and Ambient Systems (I&A) and Interpretable Machine Learning (iML) have focused on the need to explain to single end-users to gain their individual trust [13, 55, 68], research on fair and accountable, and transparent (FAT) algorithms aims to address macrosc”
Algorithmic Accountability and Public Reason (2017, background): “This phenomenon has been termed algorithmic decision-making, and there are increasing calls to make algorithmic decision-makers accountable for their activities.1 1See e.g. (Diakopoulos, 2016), and section 2 for further examples.”
Explanations as Mechanisms for Supporting Algorithmic Transparency (2018, background): “Researchers and policy-makers argue that transparency is valuable, and advocate greater transparency as a remedy for identifying and preventing various potential negative effects of these systems [12].”