Abstract Artificial intelligence (AI) is already widely used in daily communication, but despite concerns about AI’s negative effects on society the social consequences of using it to communicate remain largely unexplored. We investigate the social consequences of one of the most pervasive AI applications, algorithmic response suggestions (“smart replies”), which are used to send billions of messages each day. Two randomized experiments provide evidence that these types of algorithmic recommender systems change how people interact with and perceive one another in both pro-social and anti-social ways. We find that using algorithmic responses changes language and social relationships. More specifically, it increases communication speed, use of positive emotional language, and conversation partners evaluate each other as closer and more cooperative. However, consistent with common assumptions about the adverse effects of AI, people are evaluated more negatively if they are suspected to be using algorithmic responses. Thus, even though AI can increase the speed of communication and improve interpersonal perceptions, the prevailing anti-social connotations of AI undermine these potential benefits if used overtly.
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This content will become publicly available on May 11, 2025
Making Transparency Influencers: A Case Study of an Educational Approach to Improve Responsible AI Practices in News and Media
Concerns about the risks posed by artificial intelligence (AI) have resulted in growing interest in algorithmic transparency. While algorithmic transparency is well-studied, there is evidence that many organizations do not value implementing transparency. In this case study, we test a ground-up approach to ensuring better real-world algorithmic transparency by creating transparency influencers — motivated individuals within organizations who advocate for transparency. We held an interactive online workshop on algorithmic transparency and advocacy for 15 professionals from news, media, and journalism. We reflect on workshop design choices and presents insights from participant interviews. We found positive evidence for our approach: In the days following the workshop, three participants had done pro-transparency advocacy. Notably, one of them advocated for algorithmic transparency at an organization-wide AI strategy meeting. In the words of a participant: “if you are questioning whether or not you need to tell people [about AI], you need to tell people.”
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- PAR ID:
- 10514477
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems
- ISBN:
- 9798400703317
- Page Range / eLocation ID:
- 1 to 8
- Subject(s) / Keyword(s):
- responsible AI transparency explainability artificial intelligence machine learning tempered radicals
- Format(s):
- Medium: X
- Location:
- Honolulu HI USA
- Sponsoring Org:
- National Science Foundation
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