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  • × author_ss:"Fleischmann, K.R."
  • × year_i:[2020 TO 2030}
  1. Slota, S.C.; Fleischmann, K.R.; Greenberg, S.; Verma, N.; Cummings, B.; Li, L.; Shenefiel, C.: Locating the work of artificial intelligence ethics (2023) 0.03 
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    Abstract
    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.
  2. Slota, S.C.; Fleischmann, K.R.; Lee, M.K.; Greenberg, S.R.; Nigam, I.; Zimmerman, T.; Rodriguez, S.; Snow, J.: ¬A feeling for the data : how government and nonprofit stakeholders negotiate value conflicts in data science approaches to ending homelessness (2023) 0.03 
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    Abstract
    Governmental and organizational policy increasingly claims to be data-driven, data-informed, or knowledge-driven. We explore the data practices of local governments and nonprofits a seeking to end homelessness in the City of Austin. Drawing on 31 interviews with stakeholders, alongside the reflections and experiences of our interdisciplinary, cross-sector collaborative team, we consider the role of data in guiding and informing interventions and policy regarding homelessness. Ending homelessness is a particularly challenging scenario for intervention, with increasing politicization, changing circumstances, and needing rapid intervention to reduce harm. In exploring some implications of data science "in the wild" as it is deployed, understood, and supported within the Travis County Continuum of Care (CoC), we analyze how data-intensive work connects and engages across disciplinary boundaries. Furthermore, we consider how data science and the iField can collaborate in addressing complex, social problems as advisors and partners with invested organizations.

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