Blind Spots in AI

Leeuwen, Cora van; Smets, Annelien; Jacobs, An

doi:10.1145/3468507.3468514

Cited by 6 publications

(1 citation statement)

References 34 publications

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“…The decision to employ new technologies in decision‐making indicates the start of an organizational change that requires specific leadership skills to better prepare for digitalization (Calderon‐Monge & Ribeiro‐Soriano, 2023). Moreover, a well‐designed system incorporates “the principles of serendipity (diversifiability) and equity (inter‐sectionality, reflexivity and power balances)” that helps to reduce biases and blind spots (van Leeuwen et al, 2020, p. 13).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Strategic organizational changes: Adopting data‐driven decisions

Bilkštytė‐Skanė,

Akstinaite

2023

Strategic Change

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The paper examines the adoption of a data‐driven decision‐making (DDDM) process in organizations from purposefully selected European Union (EU) countries. It determines what organizational changes are required to adopt this process in organizations. This study uses a mixed‐method approach to identify organizational changes required for DDDM adoption. The responses from quantitative research in 10 EU countries (1091 respondents) and qualitative research with 20 C‐level managers are analyzed. The study offers the following organizational changes needed to implement DDDM in organizations: culture and mindset changes, digitalization, process improvements, new competencies, re‐organization, and legal requirements. This research contributes to a better understanding of the usage and adoption of DDDM globally and suggests specific organizational changes required to adopt this process.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Strategic organizational changes: Adopting data‐driven decisions

Bilkštytė‐Skanė,

Akstinaite

2023

Strategic Change

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Algorithms as Decision-Makers

Rusko,

Koivisto,

Jestilä

2023

Analytics Enabled Decision Making

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Towards responsible media recommendation

et al. 2021

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Reading or viewing recommendations are a common feature on modern media sites. What is shown to consumers as recommendations is nowadays often automatically determined by AI algorithms, typically with the goal of helping consumers discover relevant content more easily. However, the highlighting or filtering of information that comes with such recommendations may lead to undesired effects on consumers or even society, for example, when an algorithm leads to the creation of filter bubbles or amplifies the spread of misinformation. These well-documented phenomena create a need for improved mechanisms for responsible media recommendation, which avoid such negative effects of recommender systems. In this research note, we review the threats and challenges that may result from the use of automated media recommendation technology, and we outline possible steps to mitigate such undesired societal effects in the future.

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Blind Spots in AI

Cited by 6 publications

References 34 publications

Strategic organizational changes: Adopting data‐driven decisions

Strategic organizational changes: Adopting data‐driven decisions

Algorithms as Decision-Makers

Towards responsible media recommendation

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