Something New Versus Tried and True: Ensuring ‘Innovative’ AI is ‘Good’ AI

Slota, Stephen C.; Fleischmann, Kenneth R.; Greenberg, Sherri R.; Verma, Nitin; Cummings, Brenna; Li, Lan; Shenefiel, Chris

doi:10.1007/978-3-030-71292-1_3

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…Throughout these interviews, personal and social values were, as expected, a key factor in structuring both daily work and conceptions of the future. While occasionally these values were in tension, such as the tension between security and privacy, or between innovation and regulation (Slota et al, 2021), diffuse, nonspecific, visions of the potential positive impacts of AI (especially compared with the concrete, recent, examples of negative consequence frequently related) indicate that progress in AI was seen as a potential pathway toward reaching a future that aligns well with their personal values.…”

Section: Discussionmentioning

confidence: 99%

“…Our thematic analysis was deductively attentive to the role of values in AI work, especially in addressing issues of AI ethics (Friedman et al, 2008). Prior work drawing upon this corpus of interviews has considered the ways in which public representations of AI inflect upon stakeholders' understanding of its underlying data (Slota, Fleischmann, et al, 2020; Slota, Hoffman, et al, 2020), and the tension between the drive to innovate and the need to manage that regulation (Slota et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

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Locating the work of artificial intelligence ethics

Slota

Fleischmann

Greenberg

et al. 2022

Asso for Info Science & Tech

Self Cite

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The scale and complexity of the data and algorithms used in artificial intelligence (AI)‐based systems present significant challenges for anticipating their ethical, legal, and policy implications. Given these challenges, who does the work of AI ethics, and how do they do it? This study reports findings from interviews with 26 stakeholders in AI research, law, and policy. The primary themes are that the work of AI ethics is structured by personal values and professional commitments, and that it involves situated meaning‐making through data and algorithms. Given the stakes involved, it is not enough to simply satisfy that AI will not behave unethically; rather, the work of AI ethics needs to be incentivized.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Locating the work of artificial intelligence ethics

Slota

Fleischmann

Greenberg

et al. 2022

Asso for Info Science & Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, a strong pro-innovation stance could lead to risk-taking or scientifically unfounded experimentation for innovation and change. Slota et al rightly pointed out this challenge and have critically questioned that innovation may not be positive per se and cannot be unquestionably accepted and suggested that innovation needs to abide by prerequisites to be considered positive, for example, reliability measurements (Slota et al, 2021 ).…”

Section: Lack Of Standard Definition Of Aimentioning

confidence: 99%

AI Through Ethical Lenses: A Discourse Analysis of Guidelines for AI in Healthcare

Arbelaez Ossa,

Milford,

Rost

et al. 2024

Sci Eng Ethics

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While the technologies that enable Artificial Intelligence (AI) continue to advance rapidly, there are increasing promises regarding AI’s beneficial outputs and concerns about the challenges of human–computer interaction in healthcare. To address these concerns, institutions have increasingly resorted to publishing AI guidelines for healthcare, aiming to align AI with ethical practices. However, guidelines as a form of written language can be analyzed to recognize the reciprocal links between its textual communication and underlying societal ideas. From this perspective, we conducted a discourse analysis to understand how these guidelines construct, articulate, and frame ethics for AI in healthcare. We included eight guidelines and identified three prevalent and interwoven discourses: (1) AI is unavoidable and desirable; (2) AI needs to be guided with (some forms of) principles (3) trust in AI is instrumental and primary. These discourses signal an over-spillage of technical ideals to AI ethics, such as over-optimism and resulting hyper-criticism. This research provides insights into the underlying ideas present in AI guidelines and how guidelines influence the practice and alignment of AI with ethical, legal, and societal values expected to shape AI in healthcare.

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