Responsible Innovation in Collaborative, Connected, and Intelligent Systems: Design, Implementation, and Governance

Permanent URI for this collectionhttps://hdl.handle.net/10125/107413

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    Advancing Human-Centred Algorithm Design Through Reflective Practice
    (2024-01-03) Raats, Kaspar
    Autonomous vehicle (AV) algorithms tend to be designed with a techno-solutionism mindset, causing algorithms to fail in real-world applications. This can be attributed to algorithm developers’ lack of routines and knowledge to consider the environments and circumstances AVs are intended to partake in. This paper argues for shifting towards a more responsible human-centred algorithm design (HCAD). It addresses this by demonstrating the different reflective practice qualities obtained by engaging algorithm designers from four companies with ethnographic materials. The study shows that Design Ethnographic (DE) approach allowed developers to consider the value of AVs from sociotechnical perspectives and facilitated collaborative learning and debating about what problems truly need solving to bring societal value. This demonstrates how ethnographically infused HCAD helps expand algorithm developers’ opportunities to participate responsibly in value co-creation for society.
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    Exploring Public Opinion on Responsible AI Through The Lens of Cultural Consensus Theory
    (2024-01-03) Gurkan, Necdet; Suchow, Jordan
    As the societal implications of Artificial Intelligence (AI) continue to grow, the pursuit of responsible AI necessitates public engagement in its development and governance processes. This involvement is crucial for capturing diverse perspectives and promoting equitable practices and outcomes. We applied Cultural Consensus Theory (CCT) to a nationally representative survey dataset on various aspects of AI to discern beliefs and attitudes about responsible AI in the United States. Our results offer valuable insights by identifying shared and contrasting views on responsible AI, pinpointing the most controversial topics across different consensus groups, and even within similar cultural belief systems. Furthermore, these findings serve as critical reference points for developers and policymakers, enabling them to more effectively consider individual variances and group-level cultural perspectives when making significant decisions and addressing the public's concerns.
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    Unraveling the Impact of Visual Cues in Online Portraits on Workers’ Employability in Digital Labor Markets
    (2024-01-03) Jiang, Yuting; Rossi, Matti; Tuunainen, Virpi; Cai, Zhao; Tan, Chee-Wee
    Online portraits constitute a pervasive and critical signal in digital labor markets in that workers can boost their employability by manipulating select visual cues embedded in these portraits. Consequently, we attempt to unravel how visual cues embedded in workers’ portraits within digital labor markets can collectively influence constituent dimensions of employability. Notably, we advance a non-verbal cues classification model that differentiates among demographic, physical appearance, image quality, and non-verbal behavioral cues as focal determinants affecting one’s employment status, the number of job offers received, and rehiring probability. Employing computer vision and deep learning algorithmic techniques to analyze the online portraits and personal information of 53,950 workers on Upwork.com, we demonstrate that visual cues embedded in profile portraits exert a significant effect on workers’ employability in digital labor markets.
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